{"title":"Intelligent workflows \u0026 agents","description":"\u003cp\u003ePut intelligence into everyday operations. Build integrated workflows and assistants around your systems, business rules and human approval requirements.\u003c\/p\u003e","products":[{"product_id":"ealthcare-and-life-sciences-clinic-capacity-optimizer","title":"Healthcare: Clinic capacity optimizer","description":"\u003cp\u003eUnused appointment capacity and slow access. Corefinery works with your healthcare and life sciences team to turn this bottleneck into a practical, measurable workflow. The engagement focuses on rank waitlist candidates and explain slot recommendations rather than a standalone demonstration.\u003c\/p\u003e\u003ch3\u003eWhat the engagement covers\u003c\/h3\u003e\u003cp\u003eOne production workflow; up to two integrations; one business unit. We map the current process with the people doing the work, identify decision rights and exceptions, and agree which actions require approval. The workflow is designed around existing systems and an accountable business owner.\u003c\/p\u003e\u003ch3\u003eWorkflow and data\u003c\/h3\u003e\u003cp\u003eSource inputs: Client-authorized source-system records; approved operating policies; representative task examples; integration documentation. Rank waitlist candidates and explain slot recommendations. Every recommendation should retain its supporting context so an operator can inspect the evidence and correct incomplete or inaccurate output.\u003c\/p\u003e\u003ch3\u003eEngineering approach\u003c\/h3\u003e\u003cp\u003eDeterministic components: Provider credentials; room capacity; appointment durations. These rules control calculations, eligibility and permitted actions. Non-deterministic AI handles interpretation, retrieval and draft recommendations; it does not replace the policy engine. Consequential actions require an authorized human decision.\u003c\/p\u003e\u003cp\u003eModel selection compares task quality, latency, privacy requirements and cost. Claude or OpenAI can support complex language tasks; a specialized small language model can handle narrower classification or retrieval when it meets the same evaluation criteria. Adaptation, quantization and private or edge deployment are considered only when supported by data rights, infrastructure and measured performance. Hosted vendor models remain subject to vendor deployment options and terms.\u003c\/p\u003e\u003ch3\u003eDeliverables\u003c\/h3\u003e\u003cul\u003e\n\u003cli\u003eCurrent-state workflow map, baseline metrics and prioritized requirements\u003c\/li\u003e\n\u003cli\u003eArchitecture, access boundaries, integration design and exception-handling plan\u003c\/li\u003e\n\u003cli\u003eRepresentative evaluation cases and an agreed acceptance scorecard\u003c\/li\u003e\n\u003cli\u003eWorking workflow software, scoped integrations and operator review queue\u003c\/li\u003e\n\u003cli\u003eMonitoring, audit events, deployment runbook, team training and source-code handover\u003c\/li\u003e\n\u003c\/ul\u003e\u003ch3\u003eDelivery and acceptance\u003c\/h3\u003e\u003cp\u003eForward deployed engineers work alongside your operators, with an AI engineer, integration engineer and shared quality and security support. The team reviews progress weekly, tests representative normal and exception cases, and documents limitations. Release follows owner acceptance, access-control testing and a demonstrated pause or rollback process.\u003c\/p\u003e\u003ch3\u003eHow value is measured\u003c\/h3\u003e\u003cp\u003ePrimary measures: Slot utilization; days to appointment. Establish a baseline before changes, then compare a controlled pilot or agreed pre\/post period. Track quality, adoption and attributable benefits alongside labor time and ongoing model costs. Targets and acceptance thresholds are agreed during discovery; financial or operational results are not guaranteed.\u003c\/p\u003e\u003ch3\u003eInvestment and timing\u003c\/h3\u003e\u003cp\u003eEstimated engagement: USD $40,000–$90,000. The listed product price of $40,000 is the starting professional services fee, not a deposit or a guaranteed fixed quote. Indicative delivery: 8-16 weeks after prerequisites are available. Final scope, milestones and commercial terms are agreed in a statement of work. Model\/API usage, cloud infrastructure, hardware, third-party licenses, taxes, major data remediation and ongoing managed support are excluded unless expressly included.\u003c\/p\u003e\u003ch3\u003eGetting started\u003c\/h3\u003e\u003cp\u003eProvide a named process owner, authorized access to representative data, integration documentation and operator time for review. Regulated or sensitive workflows require applicable privacy and security approvals before data access. Clinical decisions remain with qualified professionals. The first working session confirms the use case, baseline, approval boundaries and the smallest valuable delivery scope.\u003c\/p\u003e","brand":"Corefinery","offers":[{"title":"Default Title","offer_id":68850766348482,"sku":"CF-AI-002","price":40000.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0798\/2101\/3186\/files\/Platinum_pathway_to_what_s_next-2.png?v=1794072948"},{"product_id":"ealthcare-and-life-sciences-prior-authorization-workbench","title":"Healthcare: Prior authorization workbench","description":"\u003cp\u003eManual authorization preparation delays care. Corefinery works with your healthcare and life sciences team to turn this bottleneck into a practical, measurable workflow. The engagement focuses on extract supporting evidence and draft authorization packets rather than a standalone demonstration.\u003c\/p\u003e\u003ch3\u003eWhat the engagement covers\u003c\/h3\u003e\u003cp\u003eOne production workflow; up to two integrations; one business unit. We map the current process with the people doing the work, identify decision rights and exceptions, and agree which actions require approval. The workflow is designed around existing systems and an accountable business owner.\u003c\/p\u003e\u003ch3\u003eWorkflow and data\u003c\/h3\u003e\u003cp\u003eSource inputs: Client-authorized source-system records; approved operating policies; representative task examples; integration documentation. Extract supporting evidence and draft authorization packets. Every recommendation should retain its supporting context so an operator can inspect the evidence and correct incomplete or inaccurate output.\u003c\/p\u003e\u003ch3\u003eEngineering approach\u003c\/h3\u003e\u003cp\u003eDeterministic components: Payer checklist; required fields; submission approval. These rules control calculations, eligibility and permitted actions. Non-deterministic AI handles interpretation, retrieval and draft recommendations; it does not replace the policy engine. Consequential actions require an authorized human decision.\u003c\/p\u003e\u003cp\u003eModel selection compares task quality, latency, privacy requirements and cost. Claude or OpenAI can support complex language tasks; a specialized small language model can handle narrower classification or retrieval when it meets the same evaluation criteria. Adaptation, quantization and private or edge deployment are considered only when supported by data rights, infrastructure and measured performance. Hosted vendor models remain subject to vendor deployment options and terms.\u003c\/p\u003e\u003ch3\u003eDeliverables\u003c\/h3\u003e\u003cul\u003e\n\u003cli\u003eCurrent-state workflow map, baseline metrics and prioritized requirements\u003c\/li\u003e\n\u003cli\u003eArchitecture, access boundaries, integration design and exception-handling plan\u003c\/li\u003e\n\u003cli\u003eRepresentative evaluation cases and an agreed acceptance scorecard\u003c\/li\u003e\n\u003cli\u003eWorking workflow software, scoped integrations and operator review queue\u003c\/li\u003e\n\u003cli\u003eMonitoring, audit events, deployment runbook, team training and source-code handover\u003c\/li\u003e\n\u003c\/ul\u003e\u003ch3\u003eDelivery and acceptance\u003c\/h3\u003e\u003cp\u003eForward deployed engineers work alongside your operators, with an AI engineer, integration engineer and shared quality and security support. The team reviews progress weekly, tests representative normal and exception cases, and documents limitations. Release follows owner acceptance, access-control testing and a demonstrated pause or rollback process.\u003c\/p\u003e\u003ch3\u003eHow value is measured\u003c\/h3\u003e\u003cp\u003ePrimary measures: Preparation minutes; first-pass completeness. Establish a baseline before changes, then compare a controlled pilot or agreed pre\/post period. Track quality, adoption and attributable benefits alongside labor time and ongoing model costs. Targets and acceptance thresholds are agreed during discovery; financial or operational results are not guaranteed.\u003c\/p\u003e\u003ch3\u003eInvestment and timing\u003c\/h3\u003e\u003cp\u003eEstimated engagement: USD $60,000–$140,000. The listed product price of $60,000 is the starting professional services fee, not a deposit or a guaranteed fixed quote. Indicative delivery: 8-16 weeks after prerequisites are available. Final scope, milestones and commercial terms are agreed in a statement of work. Model\/API usage, cloud infrastructure, hardware, third-party licenses, taxes, major data remediation and ongoing managed support are excluded unless expressly included.\u003c\/p\u003e\u003ch3\u003eGetting started\u003c\/h3\u003e\u003cp\u003eProvide a named process owner, authorized access to representative data, integration documentation and operator time for review. Regulated or sensitive workflows require applicable privacy and security approvals before data access. Clinical decisions remain with qualified professionals. The first working session confirms the use case, baseline, approval boundaries and the smallest valuable delivery scope.\u003c\/p\u003e","brand":"Corefinery","offers":[{"title":"Default Title","offer_id":68850779783362,"sku":"CF-AI-003","price":60000.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0798\/2101\/3186\/files\/Platinum_pathway_to_what_s_next-2.png?v=1794072948"},{"product_id":"ealthcare-and-life-sciences-clinical-documentation-assistant","title":"Healthcare: Clinical documentation assistant","description":"\u003cp\u003eDocumentation reduces patient-facing time. Corefinery works with your healthcare and life sciences team to turn this bottleneck into a practical, measurable workflow. The engagement focuses on draft encounter summaries from authorized notes rather than a standalone demonstration.\u003c\/p\u003e\u003ch3\u003eWhat the engagement covers\u003c\/h3\u003e\u003cp\u003eOne production workflow; up to two integrations; one business unit. We map the current process with the people doing the work, identify decision rights and exceptions, and agree which actions require approval. The workflow is designed around existing systems and an accountable business owner.\u003c\/p\u003e\u003ch3\u003eWorkflow and data\u003c\/h3\u003e\u003cp\u003eSource inputs: Client-authorized source-system records; approved operating policies; representative task examples; integration documentation. Draft encounter summaries from authorized notes. Every recommendation should retain its supporting context so an operator can inspect the evidence and correct incomplete or inaccurate output.\u003c\/p\u003e\u003ch3\u003eEngineering approach\u003c\/h3\u003e\u003cp\u003eDeterministic components: Template validation; identity checks; clinician sign-off. These rules control calculations, eligibility and permitted actions. Non-deterministic AI handles interpretation, retrieval and draft recommendations; it does not replace the policy engine. Consequential actions require an authorized human decision.\u003c\/p\u003e\u003cp\u003eModel selection compares task quality, latency, privacy requirements and cost. Claude or OpenAI can support complex language tasks; a specialized small language model can handle narrower classification or retrieval when it meets the same evaluation criteria. Adaptation, quantization and private or edge deployment are considered only when supported by data rights, infrastructure and measured performance. Hosted vendor models remain subject to vendor deployment options and terms.\u003c\/p\u003e\u003ch3\u003eDeliverables\u003c\/h3\u003e\u003cul\u003e\n\u003cli\u003eCurrent-state workflow map, baseline metrics and prioritized requirements\u003c\/li\u003e\n\u003cli\u003eArchitecture, access boundaries, integration design and exception-handling plan\u003c\/li\u003e\n\u003cli\u003eRepresentative evaluation cases and an agreed acceptance scorecard\u003c\/li\u003e\n\u003cli\u003eWorking workflow software, scoped integrations and operator review queue\u003c\/li\u003e\n\u003cli\u003eMonitoring, audit events, deployment runbook, team training and source-code handover\u003c\/li\u003e\n\u003c\/ul\u003e\u003ch3\u003eDelivery and acceptance\u003c\/h3\u003e\u003cp\u003eForward deployed engineers work alongside your operators, with an AI engineer, integration engineer and shared quality and security support. The team reviews progress weekly, tests representative normal and exception cases, and documents limitations. Release follows owner acceptance, access-control testing and a demonstrated pause or rollback process.\u003c\/p\u003e\u003ch3\u003eHow value is measured\u003c\/h3\u003e\u003cp\u003ePrimary measures: Documentation minutes; correction rate. Establish a baseline before changes, then compare a controlled pilot or agreed pre\/post period. Track quality, adoption and attributable benefits alongside labor time and ongoing model costs. Targets and acceptance thresholds are agreed during discovery; financial or operational results are not guaranteed.\u003c\/p\u003e\u003ch3\u003eInvestment and timing\u003c\/h3\u003e\u003cp\u003eEstimated engagement: USD $75,000–$180,000. The listed product price of $75,000 is the starting professional services fee, not a deposit or a guaranteed fixed quote. Indicative delivery: 8-16 weeks after prerequisites are available. Final scope, milestones and commercial terms are agreed in a statement of work. Model\/API usage, cloud infrastructure, hardware, third-party licenses, taxes, major data remediation and ongoing managed support are excluded unless expressly included.\u003c\/p\u003e\u003ch3\u003eGetting started\u003c\/h3\u003e\u003cp\u003eProvide a named process owner, authorized access to representative data, integration documentation and operator time for review. Regulated or sensitive workflows require applicable privacy and security approvals before data access. Clinical decisions remain with qualified professionals. The first working session confirms the use case, baseline, approval boundaries and the smallest valuable delivery scope.\u003c\/p\u003e","brand":"Corefinery","offers":[{"title":"Default Title","offer_id":68850779881666,"sku":"CF-AI-004","price":75000.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0798\/2101\/3186\/files\/Platinum_pathway_to_what_s_next-2.png?v=1794072948"},{"product_id":"ealthcare-and-life-sciences-denial-recovery-copilot","title":"Healthcare: Denial recovery copilot","description":"\u003cp\u003eRecoverable claims remain unresolved. Corefinery works with your healthcare and life sciences team to turn this bottleneck into a practical, measurable workflow. The engagement focuses on classify denial reasons and draft evidence-linked appeals rather than a standalone demonstration.\u003c\/p\u003e\u003ch3\u003eWhat the engagement covers\u003c\/h3\u003e\u003cp\u003eOne production workflow; up to two integrations; one business unit. We map the current process with the people doing the work, identify decision rights and exceptions, and agree which actions require approval. The workflow is designed around existing systems and an accountable business owner.\u003c\/p\u003e\u003ch3\u003eWorkflow and data\u003c\/h3\u003e\u003cp\u003eSource inputs: Client-authorized source-system records; approved operating policies; representative task examples; integration documentation. Classify denial reasons and draft evidence-linked appeals. Every recommendation should retain its supporting context so an operator can inspect the evidence and correct incomplete or inaccurate output.\u003c\/p\u003e\u003ch3\u003eEngineering approach\u003c\/h3\u003e\u003cp\u003eDeterministic components: Filing deadlines; claim reconciliation; appeal approval. These rules control calculations, eligibility and permitted actions. Non-deterministic AI handles interpretation, retrieval and draft recommendations; it does not replace the policy engine. Consequential actions require an authorized human decision.\u003c\/p\u003e\u003cp\u003eModel selection compares task quality, latency, privacy requirements and cost. Claude or OpenAI can support complex language tasks; a specialized small language model can handle narrower classification or retrieval when it meets the same evaluation criteria. Adaptation, quantization and private or edge deployment are considered only when supported by data rights, infrastructure and measured performance. Hosted vendor models remain subject to vendor deployment options and terms.\u003c\/p\u003e\u003ch3\u003eDeliverables\u003c\/h3\u003e\u003cul\u003e\n\u003cli\u003eCurrent-state workflow map, baseline metrics and prioritized requirements\u003c\/li\u003e\n\u003cli\u003eArchitecture, access boundaries, integration design and exception-handling plan\u003c\/li\u003e\n\u003cli\u003eRepresentative evaluation cases and an agreed acceptance scorecard\u003c\/li\u003e\n\u003cli\u003eWorking workflow software, scoped integrations and operator review queue\u003c\/li\u003e\n\u003cli\u003eMonitoring, audit events, deployment runbook, team training and source-code handover\u003c\/li\u003e\n\u003c\/ul\u003e\u003ch3\u003eDelivery and acceptance\u003c\/h3\u003e\u003cp\u003eForward deployed engineers work alongside your operators, with an AI engineer, integration engineer and shared quality and security support. The team reviews progress weekly, tests representative normal and exception cases, and documents limitations. Release follows owner acceptance, access-control testing and a demonstrated pause or rollback process.\u003c\/p\u003e\u003ch3\u003eHow value is measured\u003c\/h3\u003e\u003cp\u003ePrimary measures: Recovery rate; days to resolution. Establish a baseline before changes, then compare a controlled pilot or agreed pre\/post period. Track quality, adoption and attributable benefits alongside labor time and ongoing model costs. Targets and acceptance thresholds are agreed during discovery; financial or operational results are not guaranteed.\u003c\/p\u003e\u003ch3\u003eInvestment and timing\u003c\/h3\u003e\u003cp\u003eEstimated engagement: USD $90,000–$225,000. The listed product price of $90,000 is the starting professional services fee, not a deposit or a guaranteed fixed quote. Indicative delivery: 8-16 weeks after prerequisites are available. Final scope, milestones and commercial terms are agreed in a statement of work. Model\/API usage, cloud infrastructure, hardware, third-party licenses, taxes, major data remediation and ongoing managed support are excluded unless expressly included.\u003c\/p\u003e\u003ch3\u003eGetting started\u003c\/h3\u003e\u003cp\u003eProvide a named process owner, authorized access to representative data, integration documentation and operator time for review. Regulated or sensitive workflows require applicable privacy and security approvals before data access. Clinical decisions remain with qualified professionals. The first working session confirms the use case, baseline, approval boundaries and the smallest valuable delivery scope.\u003c\/p\u003e","brand":"Corefinery","offers":[{"title":"Default Title","offer_id":68850779947202,"sku":"CF-AI-005","price":90000.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0798\/2101\/3186\/files\/Platinum_pathway_to_what_s_next-2.png?v=1794072948"},{"product_id":"ealthcare-and-life-sciences-patient-access-navigator","title":"Healthcare: Patient access navigator","description":"\u003cp\u003eStaff repeat administrative answers. Corefinery works with your healthcare and life sciences team to turn this bottleneck into a practical, measurable workflow. The engagement focuses on answer administrative questions and summarize requests rather than a standalone demonstration.\u003c\/p\u003e\u003ch3\u003eWhat the engagement covers\u003c\/h3\u003e\u003cp\u003eOne production workflow; up to two integrations; one business unit. We map the current process with the people doing the work, identify decision rights and exceptions, and agree which actions require approval. The workflow is designed around existing systems and an accountable business owner.\u003c\/p\u003e\u003ch3\u003eWorkflow and data\u003c\/h3\u003e\u003cp\u003eSource inputs: Client-authorized source-system records; approved operating policies; representative task examples; integration documentation. Answer administrative questions and summarize requests. Every recommendation should retain its supporting context so an operator can inspect the evidence and correct incomplete or inaccurate output.\u003c\/p\u003e\u003ch3\u003eEngineering approach\u003c\/h3\u003e\u003cp\u003eDeterministic components: Approved service directory; identity checks; escalation rules. These rules control calculations, eligibility and permitted actions. Non-deterministic AI handles interpretation, retrieval and draft recommendations; it does not replace the policy engine. Consequential actions require an authorized human decision.\u003c\/p\u003e\u003cp\u003eModel selection compares task quality, latency, privacy requirements and cost. Claude or OpenAI can support complex language tasks; a specialized small language model can handle narrower classification or retrieval when it meets the same evaluation criteria. Adaptation, quantization and private or edge deployment are considered only when supported by data rights, infrastructure and measured performance. Hosted vendor models remain subject to vendor deployment options and terms.\u003c\/p\u003e\u003ch3\u003eDeliverables\u003c\/h3\u003e\u003cul\u003e\n\u003cli\u003eCurrent-state workflow map, baseline metrics and prioritized requirements\u003c\/li\u003e\n\u003cli\u003eArchitecture, access boundaries, integration design and exception-handling plan\u003c\/li\u003e\n\u003cli\u003eRepresentative evaluation cases and an agreed acceptance scorecard\u003c\/li\u003e\n\u003cli\u003eWorking workflow software, scoped integrations and operator review queue\u003c\/li\u003e\n\u003cli\u003eMonitoring, audit events, deployment runbook, team training and source-code handover\u003c\/li\u003e\n\u003c\/ul\u003e\u003ch3\u003eDelivery and acceptance\u003c\/h3\u003e\u003cp\u003eForward deployed engineers work alongside your operators, with an AI engineer, integration engineer and shared quality and security support. The team reviews progress weekly, tests representative normal and exception cases, and documents limitations. Release follows owner acceptance, access-control testing and a demonstrated pause or rollback process.\u003c\/p\u003e\u003ch3\u003eHow value is measured\u003c\/h3\u003e\u003cp\u003ePrimary measures: Resolution time; escalation accuracy. Establish a baseline before changes, then compare a controlled pilot or agreed pre\/post period. Track quality, adoption and attributable benefits alongside labor time and ongoing model costs. Targets and acceptance thresholds are agreed during discovery; financial or operational results are not guaranteed.\u003c\/p\u003e\u003ch3\u003eInvestment and timing\u003c\/h3\u003e\u003cp\u003eEstimated engagement: USD $100,000–$250,000. The listed product price of $100,000 is the starting professional services fee, not a deposit or a guaranteed fixed quote. Indicative delivery: 8-16 weeks after prerequisites are available. Final scope, milestones and commercial terms are agreed in a statement of work. Model\/API usage, cloud infrastructure, hardware, third-party licenses, taxes, major data remediation and ongoing managed support are excluded unless expressly included.\u003c\/p\u003e\u003ch3\u003eGetting started\u003c\/h3\u003e\u003cp\u003eProvide a named process owner, authorized access to representative data, integration documentation and operator time for review. Regulated or sensitive workflows require applicable privacy and security approvals before data access. Clinical decisions remain with qualified professionals. The first working session confirms the use case, baseline, approval boundaries and the smallest valuable delivery scope.\u003c\/p\u003e","brand":"Corefinery","offers":[{"title":"Default Title","offer_id":68850779979970,"sku":"CF-AI-006","price":100000.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0798\/2101\/3186\/files\/Platinum_pathway_to_what_s_next-2.png?v=1794072948"},{"product_id":"ealthcare-and-life-sciences-clinical-trial-operations-matcher","title":"Healthcare: Clinical trial operations matcher","description":"\u003cp\u003eResearch teams manually screen records. Corefinery works with your healthcare and life sciences team to turn this bottleneck into a practical, measurable workflow. The engagement focuses on extract candidate evidence for coordinator review rather than a standalone demonstration.\u003c\/p\u003e\u003ch3\u003eWhat the engagement covers\u003c\/h3\u003e\u003cp\u003eOne production workflow; up to two integrations; one business unit. We map the current process with the people doing the work, identify decision rights and exceptions, and agree which actions require approval. The workflow is designed around existing systems and an accountable business owner.\u003c\/p\u003e\u003ch3\u003eWorkflow and data\u003c\/h3\u003e\u003cp\u003eSource inputs: Client-authorized source-system records; approved operating policies; representative task examples; integration documentation. Extract candidate evidence for coordinator review. Every recommendation should retain its supporting context so an operator can inspect the evidence and correct incomplete or inaccurate output.\u003c\/p\u003e\u003ch3\u003eEngineering approach\u003c\/h3\u003e\u003cp\u003eDeterministic components: Protocol inclusion checks; exclusion flags; consent gating. These rules control calculations, eligibility and permitted actions. Non-deterministic AI handles interpretation, retrieval and draft recommendations; it does not replace the policy engine. Consequential actions require an authorized human decision.\u003c\/p\u003e\u003cp\u003eModel selection compares task quality, latency, privacy requirements and cost. Claude or OpenAI can support complex language tasks; a specialized small language model can handle narrower classification or retrieval when it meets the same evaluation criteria. Adaptation, quantization and private or edge deployment are considered only when supported by data rights, infrastructure and measured performance. Hosted vendor models remain subject to vendor deployment options and terms.\u003c\/p\u003e\u003ch3\u003eDeliverables\u003c\/h3\u003e\u003cul\u003e\n\u003cli\u003eCurrent-state workflow map, baseline metrics and prioritized requirements\u003c\/li\u003e\n\u003cli\u003eArchitecture, access boundaries, integration design and exception-handling plan\u003c\/li\u003e\n\u003cli\u003eRepresentative evaluation cases and an agreed acceptance scorecard\u003c\/li\u003e\n\u003cli\u003eWorking workflow software, scoped integrations and operator review queue\u003c\/li\u003e\n\u003cli\u003eMonitoring, audit events, deployment runbook, team training and source-code handover\u003c\/li\u003e\n\u003c\/ul\u003e\u003ch3\u003eDelivery and acceptance\u003c\/h3\u003e\u003cp\u003eForward deployed engineers work alongside your operators, with an AI engineer, integration engineer and shared quality and security support. The team reviews progress weekly, tests representative normal and exception cases, and documents limitations. Release follows owner acceptance, access-control testing and a demonstrated pause or rollback process.\u003c\/p\u003e\u003ch3\u003eHow value is measured\u003c\/h3\u003e\u003cp\u003ePrimary measures: Screening time; reviewer-confirmed match precision. Establish a baseline before changes, then compare a controlled pilot or agreed pre\/post period. Track quality, adoption and attributable benefits alongside labor time and ongoing model costs. Targets and acceptance thresholds are agreed during discovery; financial or operational results are not guaranteed.\u003c\/p\u003e\u003ch3\u003eInvestment and timing\u003c\/h3\u003e\u003cp\u003eEstimated engagement: USD $125,000–$300,000. The listed product price of $125,000 is the starting professional services fee, not a deposit or a guaranteed fixed quote. Indicative delivery: 8-16 weeks after prerequisites are available. Final scope, milestones and commercial terms are agreed in a statement of work. Model\/API usage, cloud infrastructure, hardware, third-party licenses, taxes, major data remediation and ongoing managed support are excluded unless expressly included.\u003c\/p\u003e\u003ch3\u003eGetting started\u003c\/h3\u003e\u003cp\u003eProvide a named process owner, authorized access to representative data, integration documentation and operator time for review. Regulated or sensitive workflows require applicable privacy and security approvals before data access. Clinical decisions remain with qualified professionals. The first working session confirms the use case, baseline, approval boundaries and the smallest valuable delivery scope.\u003c\/p\u003e","brand":"Corefinery","offers":[{"title":"Default Title","offer_id":68850780045506,"sku":"CF-AI-007","price":125000.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0798\/2101\/3186\/files\/Platinum_pathway_to_what_s_next-2.png?v=1794072948"},{"product_id":"ealthcare-and-life-sciences-pharmacovigilance-intake-assistant","title":"Healthcare: Pharmacovigilance intake assistant","description":"\u003cp\u003eSafety case intake is fragmented. Corefinery works with your healthcare and life sciences team to turn this bottleneck into a practical, measurable workflow. The engagement focuses on extract case facts and suggest coding for specialist review rather than a standalone demonstration.\u003c\/p\u003e\u003ch3\u003eWhat the engagement covers\u003c\/h3\u003e\u003cp\u003eOne production workflow; up to two integrations; one business unit. We map the current process with the people doing the work, identify decision rights and exceptions, and agree which actions require approval. The workflow is designed around existing systems and an accountable business owner.\u003c\/p\u003e\u003ch3\u003eWorkflow and data\u003c\/h3\u003e\u003cp\u003eSource inputs: Client-authorized source-system records; approved operating policies; representative task examples; integration documentation. Extract case facts and suggest coding for specialist review. Every recommendation should retain its supporting context so an operator can inspect the evidence and correct incomplete or inaccurate output.\u003c\/p\u003e\u003ch3\u003eEngineering approach\u003c\/h3\u003e\u003cp\u003eDeterministic components: Mandatory fields; duplicate checks; reporting queues. These rules control calculations, eligibility and permitted actions. Non-deterministic AI handles interpretation, retrieval and draft recommendations; it does not replace the policy engine. Consequential actions require an authorized human decision.\u003c\/p\u003e\u003cp\u003eModel selection compares task quality, latency, privacy requirements and cost. Claude or OpenAI can support complex language tasks; a specialized small language model can handle narrower classification or retrieval when it meets the same evaluation criteria. Adaptation, quantization and private or edge deployment are considered only when supported by data rights, infrastructure and measured performance. Hosted vendor models remain subject to vendor deployment options and terms.\u003c\/p\u003e\u003ch3\u003eDeliverables\u003c\/h3\u003e\u003cul\u003e\n\u003cli\u003eCurrent-state workflow map, baseline metrics and prioritized requirements\u003c\/li\u003e\n\u003cli\u003eArchitecture, access boundaries, integration design and exception-handling plan\u003c\/li\u003e\n\u003cli\u003eRepresentative evaluation cases and an agreed acceptance scorecard\u003c\/li\u003e\n\u003cli\u003eWorking workflow software, scoped integrations and operator review queue\u003c\/li\u003e\n\u003cli\u003eMonitoring, audit events, deployment runbook, team training and source-code handover\u003c\/li\u003e\n\u003c\/ul\u003e\u003ch3\u003eDelivery and acceptance\u003c\/h3\u003e\u003cp\u003eForward deployed engineers work alongside your operators, with an AI engineer, integration engineer and shared quality and security support. The team reviews progress weekly, tests representative normal and exception cases, and documents limitations. Release follows owner acceptance, access-control testing and a demonstrated pause or rollback process.\u003c\/p\u003e\u003ch3\u003eHow value is measured\u003c\/h3\u003e\u003cp\u003ePrimary measures: Case preparation time; missing-field rate. Establish a baseline before changes, then compare a controlled pilot or agreed pre\/post period. Track quality, adoption and attributable benefits alongside labor time and ongoing model costs. Targets and acceptance thresholds are agreed during discovery; financial or operational results are not guaranteed.\u003c\/p\u003e\u003ch3\u003eInvestment and timing\u003c\/h3\u003e\u003cp\u003eEstimated engagement: USD $150,000–$350,000. The listed product price of $150,000 is the starting professional services fee, not a deposit or a guaranteed fixed quote. Indicative delivery: 8-16 weeks after prerequisites are available. Final scope, milestones and commercial terms are agreed in a statement of work. Model\/API usage, cloud infrastructure, hardware, third-party licenses, taxes, major data remediation and ongoing managed support are excluded unless expressly included.\u003c\/p\u003e\u003ch3\u003eGetting started\u003c\/h3\u003e\u003cp\u003eProvide a named process owner, authorized access to representative data, integration documentation and operator time for review. Regulated or sensitive workflows require applicable privacy and security approvals before data access. Clinical decisions remain with qualified professionals. The first working session confirms the use case, baseline, approval boundaries and the smallest valuable delivery scope.\u003c\/p\u003e","brand":"Corefinery","offers":[{"title":"Default Title","offer_id":68850780078274,"sku":"CF-AI-008","price":150000.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0798\/2101\/3186\/files\/Platinum_pathway_to_what_s_next-2.png?v=1794072948"},{"product_id":"ealthcare-and-life-sciences-private-slm-care-operations-assistant","title":"Healthcare: Private SLM care operations assistant","description":"\u003cp\u003eSensitive routine tasks depend on costly general models. Corefinery works with your healthcare and life sciences team to turn this bottleneck into a practical, measurable workflow. The engagement focuses on classify referrals and retrieve approved operating procedures rather than a standalone demonstration.\u003c\/p\u003e\u003ch3\u003eWhat the engagement covers\u003c\/h3\u003e\u003cp\u003eOne production workflow; up to two integrations; one business unit. We map the current process with the people doing the work, identify decision rights and exceptions, and agree which actions require approval. The workflow is designed around existing systems and an accountable business owner.\u003c\/p\u003e\u003ch3\u003eWorkflow and data\u003c\/h3\u003e\u003cp\u003eSource inputs: Client-authorized source-system records; approved operating policies; representative task examples; integration documentation. Classify referrals and retrieve approved operating procedures. Every recommendation should retain its supporting context so an operator can inspect the evidence and correct incomplete or inaccurate output.\u003c\/p\u003e\u003ch3\u003eEngineering approach\u003c\/h3\u003e\u003cp\u003eDeterministic components: Role permissions; retention controls; approved action allowlist. These rules control calculations, eligibility and permitted actions. Non-deterministic AI handles interpretation, retrieval and draft recommendations; it does not replace the policy engine. Consequential actions require an authorized human decision.\u003c\/p\u003e\u003cp\u003eModel selection compares task quality, latency, privacy requirements and cost. Claude or OpenAI can support complex language tasks; a specialized small language model can handle narrower classification or retrieval when it meets the same evaluation criteria. Adaptation, quantization and private or edge deployment are considered only when supported by data rights, infrastructure and measured performance. Hosted vendor models remain subject to vendor deployment options and terms.\u003c\/p\u003e\u003ch3\u003eDeliverables\u003c\/h3\u003e\u003cul\u003e\n\u003cli\u003eCurrent-state workflow map, baseline metrics and prioritized requirements\u003c\/li\u003e\n\u003cli\u003eArchitecture, access boundaries, integration design and exception-handling plan\u003c\/li\u003e\n\u003cli\u003eRepresentative evaluation cases and an agreed acceptance scorecard\u003c\/li\u003e\n\u003cli\u003eWorking workflow software, scoped integrations and operator review queue\u003c\/li\u003e\n\u003cli\u003eMonitoring, audit events, deployment runbook, team training and source-code handover\u003c\/li\u003e\n\u003c\/ul\u003e\u003ch3\u003eDelivery and acceptance\u003c\/h3\u003e\u003cp\u003eForward deployed engineers work alongside your operators, with an AI engineer, integration engineer and shared quality and security support. The team reviews progress weekly, tests representative normal and exception cases, and documents limitations. Release follows owner acceptance, access-control testing and a demonstrated pause or rollback process.\u003c\/p\u003e\u003ch3\u003eHow value is measured\u003c\/h3\u003e\u003cp\u003ePrimary measures: Cost per task; referral routing precision. Establish a baseline before changes, then compare a controlled pilot or agreed pre\/post period. Track quality, adoption and attributable benefits alongside labor time and ongoing model costs. Targets and acceptance thresholds are agreed during discovery; financial or operational results are not guaranteed.\u003c\/p\u003e\u003ch3\u003eInvestment and timing\u003c\/h3\u003e\u003cp\u003eEstimated engagement: USD $175,000–$400,000. The listed product price of $175,000 is the starting professional services fee, not a deposit or a guaranteed fixed quote. Indicative delivery: 8-16 weeks after prerequisites are available. Final scope, milestones and commercial terms are agreed in a statement of work. Model\/API usage, cloud infrastructure, hardware, third-party licenses, taxes, major data remediation and ongoing managed support are excluded unless expressly included.\u003c\/p\u003e\u003ch3\u003eGetting started\u003c\/h3\u003e\u003cp\u003eProvide a named process owner, authorized access to representative data, integration documentation and operator time for review. Regulated or sensitive workflows require applicable privacy and security approvals before data access. Clinical decisions remain with qualified professionals. The first working session confirms the use case, baseline, approval boundaries and the smallest valuable delivery scope.\u003c\/p\u003e","brand":"Corefinery","offers":[{"title":"Default Title","offer_id":68850780176578,"sku":"CF-AI-009","price":175000.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0798\/2101\/3186\/files\/Platinum_pathway_to_what_s_next-2.png?v=1794072948"},{"product_id":"anking-and-financial-services-kyc-evidence-workbench","title":"Banking: KYC evidence workbench","description":"\u003cp\u003eAnalysts manually assemble onboarding evidence. Corefinery works with your banking and financial services team to turn this bottleneck into a practical, measurable workflow. The engagement focuses on extract entity relationships and summarize evidence gaps rather than a standalone demonstration.\u003c\/p\u003e\u003ch3\u003eWhat the engagement covers\u003c\/h3\u003e\u003cp\u003eOne production workflow; up to two integrations; one business unit. We map the current process with the people doing the work, identify decision rights and exceptions, and agree which actions require approval. The workflow is designed around existing systems and an accountable business owner.\u003c\/p\u003e\u003ch3\u003eWorkflow and data\u003c\/h3\u003e\u003cp\u003eSource inputs: Client-authorized source-system records; approved operating policies; representative task examples; integration documentation. Extract entity relationships and summarize evidence gaps. Every recommendation should retain its supporting context so an operator can inspect the evidence and correct incomplete or inaccurate output.\u003c\/p\u003e\u003ch3\u003eEngineering approach\u003c\/h3\u003e\u003cp\u003eDeterministic components: Document expiry; required evidence; sanctions workflow gates. These rules control calculations, eligibility and permitted actions. Non-deterministic AI handles interpretation, retrieval and draft recommendations; it does not replace the policy engine. Consequential actions require an authorized human decision.\u003c\/p\u003e\u003cp\u003eModel selection compares task quality, latency, privacy requirements and cost. Claude or OpenAI can support complex language tasks; a specialized small language model can handle narrower classification or retrieval when it meets the same evaluation criteria. Adaptation, quantization and private or edge deployment are considered only when supported by data rights, infrastructure and measured performance. Hosted vendor models remain subject to vendor deployment options and terms.\u003c\/p\u003e\u003ch3\u003eDeliverables\u003c\/h3\u003e\u003cul\u003e\n\u003cli\u003eCurrent-state workflow map, baseline metrics and prioritized requirements\u003c\/li\u003e\n\u003cli\u003eArchitecture, access boundaries, integration design and exception-handling plan\u003c\/li\u003e\n\u003cli\u003eRepresentative evaluation cases and an agreed acceptance scorecard\u003c\/li\u003e\n\u003cli\u003eWorking workflow software, scoped integrations and operator review queue\u003c\/li\u003e\n\u003cli\u003eMonitoring, audit events, deployment runbook, team training and source-code handover\u003c\/li\u003e\n\u003c\/ul\u003e\u003ch3\u003eDelivery and acceptance\u003c\/h3\u003e\u003cp\u003eForward deployed engineers work alongside your operators, with an AI engineer, integration engineer and shared quality and security support. The team reviews progress weekly, tests representative normal and exception cases, and documents limitations. Release follows owner acceptance, access-control testing and a demonstrated pause or rollback process.\u003c\/p\u003e\u003ch3\u003eHow value is measured\u003c\/h3\u003e\u003cp\u003ePrimary measures: Onboarding time; rework rate. Establish a baseline before changes, then compare a controlled pilot or agreed pre\/post period. Track quality, adoption and attributable benefits alongside labor time and ongoing model costs. Targets and acceptance thresholds are agreed during discovery; financial or operational results are not guaranteed.\u003c\/p\u003e\u003ch3\u003eInvestment and timing\u003c\/h3\u003e\u003cp\u003eEstimated engagement: USD $40,000–$90,000. The listed product price of $40,000 is the starting professional services fee, not a deposit or a guaranteed fixed quote. Indicative delivery: 8-16 weeks after prerequisites are available. Final scope, milestones and commercial terms are agreed in a statement of work. Model\/API usage, cloud infrastructure, hardware, third-party licenses, taxes, major data remediation and ongoing managed support are excluded unless expressly included.\u003c\/p\u003e\u003ch3\u003eGetting started\u003c\/h3\u003e\u003cp\u003eProvide a named process owner, authorized access to representative data, integration documentation and operator time for review. Regulated or sensitive workflows require applicable privacy and security approvals before data access. The first working session confirms the use case, baseline, approval boundaries and the smallest valuable delivery scope.\u003c\/p\u003e","brand":"Corefinery","offers":[{"title":"Default Title","offer_id":68850796691650,"sku":"CF-AI-012","price":40000.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0798\/2101\/3186\/files\/Platinum_pathway_to_what_s_next-2.png?v=1794072948"},{"product_id":"anking-and-financial-services-aml-investigation-copilot","title":"Banking: AML investigation copilot","description":"\u003cp\u003eAlert backlogs consume investigator capacity. Corefinery works with your banking and financial services team to turn this bottleneck into a practical, measurable workflow. The engagement focuses on summarize suspicious patterns with source-linked timelines rather than a standalone demonstration.\u003c\/p\u003e\u003ch3\u003eWhat the engagement covers\u003c\/h3\u003e\u003cp\u003eOne production workflow; up to two integrations; one business unit. We map the current process with the people doing the work, identify decision rights and exceptions, and agree which actions require approval. The workflow is designed around existing systems and an accountable business owner.\u003c\/p\u003e\u003ch3\u003eWorkflow and data\u003c\/h3\u003e\u003cp\u003eSource inputs: Client-authorized source-system records; approved operating policies; representative task examples; integration documentation. Summarize suspicious patterns with source-linked timelines. Every recommendation should retain its supporting context so an operator can inspect the evidence and correct incomplete or inaccurate output.\u003c\/p\u003e\u003ch3\u003eEngineering approach\u003c\/h3\u003e\u003cp\u003eDeterministic components: Scenario rules; transaction reconciliation; investigator disposition. These rules control calculations, eligibility and permitted actions. Non-deterministic AI handles interpretation, retrieval and draft recommendations; it does not replace the policy engine. Consequential actions require an authorized human decision.\u003c\/p\u003e\u003cp\u003eModel selection compares task quality, latency, privacy requirements and cost. Claude or OpenAI can support complex language tasks; a specialized small language model can handle narrower classification or retrieval when it meets the same evaluation criteria. Adaptation, quantization and private or edge deployment are considered only when supported by data rights, infrastructure and measured performance. Hosted vendor models remain subject to vendor deployment options and terms.\u003c\/p\u003e\u003ch3\u003eDeliverables\u003c\/h3\u003e\u003cul\u003e\n\u003cli\u003eCurrent-state workflow map, baseline metrics and prioritized requirements\u003c\/li\u003e\n\u003cli\u003eArchitecture, access boundaries, integration design and exception-handling plan\u003c\/li\u003e\n\u003cli\u003eRepresentative evaluation cases and an agreed acceptance scorecard\u003c\/li\u003e\n\u003cli\u003eWorking workflow software, scoped integrations and operator review queue\u003c\/li\u003e\n\u003cli\u003eMonitoring, audit events, deployment runbook, team training and source-code handover\u003c\/li\u003e\n\u003c\/ul\u003e\u003ch3\u003eDelivery and acceptance\u003c\/h3\u003e\u003cp\u003eForward deployed engineers work alongside your operators, with an AI engineer, integration engineer and shared quality and security support. The team reviews progress weekly, tests representative normal and exception cases, and documents limitations. Release follows owner acceptance, access-control testing and a demonstrated pause or rollback process.\u003c\/p\u003e\u003ch3\u003eHow value is measured\u003c\/h3\u003e\u003cp\u003ePrimary measures: Investigation time; review agreement. Establish a baseline before changes, then compare a controlled pilot or agreed pre\/post period. Track quality, adoption and attributable benefits alongside labor time and ongoing model costs. Targets and acceptance thresholds are agreed during discovery; financial or operational results are not guaranteed.\u003c\/p\u003e\u003ch3\u003eInvestment and timing\u003c\/h3\u003e\u003cp\u003eEstimated engagement: USD $60,000–$140,000. The listed product price of $60,000 is the starting professional services fee, not a deposit or a guaranteed fixed quote. Indicative delivery: 8-16 weeks after prerequisites are available. Final scope, milestones and commercial terms are agreed in a statement of work. Model\/API usage, cloud infrastructure, hardware, third-party licenses, taxes, major data remediation and ongoing managed support are excluded unless expressly included.\u003c\/p\u003e\u003ch3\u003eGetting started\u003c\/h3\u003e\u003cp\u003eProvide a named process owner, authorized access to representative data, integration documentation and operator time for review. Regulated or sensitive workflows require applicable privacy and security approvals before data access. The first working session confirms the use case, baseline, approval boundaries and the smallest valuable delivery scope.\u003c\/p\u003e","brand":"Corefinery","offers":[{"title":"Default Title","offer_id":68850796724418,"sku":"CF-AI-013","price":60000.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0798\/2101\/3186\/files\/Platinum_pathway_to_what_s_next-2.png?v=1794072948"},{"product_id":"anking-and-financial-services-credit-memo-accelerator","title":"Banking: Credit memo accelerator","description":"\u003cp\u003eCredit analysis is document intensive. Corefinery works with your banking and financial services team to turn this bottleneck into a practical, measurable workflow. The engagement focuses on extract borrower facts and draft credit narratives rather than a standalone demonstration.\u003c\/p\u003e\u003ch3\u003eWhat the engagement covers\u003c\/h3\u003e\u003cp\u003eOne production workflow; up to two integrations; one business unit. We map the current process with the people doing the work, identify decision rights and exceptions, and agree which actions require approval. The workflow is designed around existing systems and an accountable business owner.\u003c\/p\u003e\u003ch3\u003eWorkflow and data\u003c\/h3\u003e\u003cp\u003eSource inputs: Client-authorized source-system records; approved operating policies; representative task examples; integration documentation. Extract borrower facts and draft credit narratives. Every recommendation should retain its supporting context so an operator can inspect the evidence and correct incomplete or inaccurate output.\u003c\/p\u003e\u003ch3\u003eEngineering approach\u003c\/h3\u003e\u003cp\u003eDeterministic components: Ratio calculations; policy thresholds; approval matrix. These rules control calculations, eligibility and permitted actions. Non-deterministic AI handles interpretation, retrieval and draft recommendations; it does not replace the policy engine. Consequential actions require an authorized human decision.\u003c\/p\u003e\u003cp\u003eModel selection compares task quality, latency, privacy requirements and cost. Claude or OpenAI can support complex language tasks; a specialized small language model can handle narrower classification or retrieval when it meets the same evaluation criteria. Adaptation, quantization and private or edge deployment are considered only when supported by data rights, infrastructure and measured performance. Hosted vendor models remain subject to vendor deployment options and terms.\u003c\/p\u003e\u003ch3\u003eDeliverables\u003c\/h3\u003e\u003cul\u003e\n\u003cli\u003eCurrent-state workflow map, baseline metrics and prioritized requirements\u003c\/li\u003e\n\u003cli\u003eArchitecture, access boundaries, integration design and exception-handling plan\u003c\/li\u003e\n\u003cli\u003eRepresentative evaluation cases and an agreed acceptance scorecard\u003c\/li\u003e\n\u003cli\u003eWorking workflow software, scoped integrations and operator review queue\u003c\/li\u003e\n\u003cli\u003eMonitoring, audit events, deployment runbook, team training and source-code handover\u003c\/li\u003e\n\u003c\/ul\u003e\u003ch3\u003eDelivery and acceptance\u003c\/h3\u003e\u003cp\u003eForward deployed engineers work alongside your operators, with an AI engineer, integration engineer and shared quality and security support. The team reviews progress weekly, tests representative normal and exception cases, and documents limitations. Release follows owner acceptance, access-control testing and a demonstrated pause or rollback process.\u003c\/p\u003e\u003ch3\u003eHow value is measured\u003c\/h3\u003e\u003cp\u003ePrimary measures: Memo preparation time; factual error rate. Establish a baseline before changes, then compare a controlled pilot or agreed pre\/post period. Track quality, adoption and attributable benefits alongside labor time and ongoing model costs. Targets and acceptance thresholds are agreed during discovery; financial or operational results are not guaranteed.\u003c\/p\u003e\u003ch3\u003eInvestment and timing\u003c\/h3\u003e\u003cp\u003eEstimated engagement: USD $75,000–$180,000. The listed product price of $75,000 is the starting professional services fee, not a deposit or a guaranteed fixed quote. Indicative delivery: 8-16 weeks after prerequisites are available. Final scope, milestones and commercial terms are agreed in a statement of work. Model\/API usage, cloud infrastructure, hardware, third-party licenses, taxes, major data remediation and ongoing managed support are excluded unless expressly included.\u003c\/p\u003e\u003ch3\u003eGetting started\u003c\/h3\u003e\u003cp\u003eProvide a named process owner, authorized access to representative data, integration documentation and operator time for review. Regulated or sensitive workflows require applicable privacy and security approvals before data access. The first working session confirms the use case, baseline, approval boundaries and the smallest valuable delivery scope.\u003c\/p\u003e","brand":"Corefinery","offers":[{"title":"Default Title","offer_id":68850796757186,"sku":"CF-AI-014","price":75000.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0798\/2101\/3186\/files\/Platinum_pathway_to_what_s_next-2.png?v=1794072948"},{"product_id":"anking-and-financial-services-treasury-exception-resolver","title":"Banking: Treasury exception resolver","description":"\u003cp\u003eCash breaks require manual investigation. Corefinery works with your banking and financial services team to turn this bottleneck into a practical, measurable workflow. The engagement focuses on explain unmatched transactions and propose resolutions rather than a standalone demonstration.\u003c\/p\u003e\u003ch3\u003eWhat the engagement covers\u003c\/h3\u003e\u003cp\u003eOne production workflow; up to two integrations; one business unit. We map the current process with the people doing the work, identify decision rights and exceptions, and agree which actions require approval. The workflow is designed around existing systems and an accountable business owner.\u003c\/p\u003e\u003ch3\u003eWorkflow and data\u003c\/h3\u003e\u003cp\u003eSource inputs: Client-authorized source-system records; approved operating policies; representative task examples; integration documentation. Explain unmatched transactions and propose resolutions. Every recommendation should retain its supporting context so an operator can inspect the evidence and correct incomplete or inaccurate output.\u003c\/p\u003e\u003ch3\u003eEngineering approach\u003c\/h3\u003e\u003cp\u003eDeterministic components: Ledger matching; payment limits; dual authorization. These rules control calculations, eligibility and permitted actions. Non-deterministic AI handles interpretation, retrieval and draft recommendations; it does not replace the policy engine. Consequential actions require an authorized human decision.\u003c\/p\u003e\u003cp\u003eModel selection compares task quality, latency, privacy requirements and cost. Claude or OpenAI can support complex language tasks; a specialized small language model can handle narrower classification or retrieval when it meets the same evaluation criteria. Adaptation, quantization and private or edge deployment are considered only when supported by data rights, infrastructure and measured performance. Hosted vendor models remain subject to vendor deployment options and terms.\u003c\/p\u003e\u003ch3\u003eDeliverables\u003c\/h3\u003e\u003cul\u003e\n\u003cli\u003eCurrent-state workflow map, baseline metrics and prioritized requirements\u003c\/li\u003e\n\u003cli\u003eArchitecture, access boundaries, integration design and exception-handling plan\u003c\/li\u003e\n\u003cli\u003eRepresentative evaluation cases and an agreed acceptance scorecard\u003c\/li\u003e\n\u003cli\u003eWorking workflow software, scoped integrations and operator review queue\u003c\/li\u003e\n\u003cli\u003eMonitoring, audit events, deployment runbook, team training and source-code handover\u003c\/li\u003e\n\u003c\/ul\u003e\u003ch3\u003eDelivery and acceptance\u003c\/h3\u003e\u003cp\u003eForward deployed engineers work alongside your operators, with an AI engineer, integration engineer and shared quality and security support. The team reviews progress weekly, tests representative normal and exception cases, and documents limitations. Release follows owner acceptance, access-control testing and a demonstrated pause or rollback process.\u003c\/p\u003e\u003ch3\u003eHow value is measured\u003c\/h3\u003e\u003cp\u003ePrimary measures: Break age; analyst hours. Establish a baseline before changes, then compare a controlled pilot or agreed pre\/post period. Track quality, adoption and attributable benefits alongside labor time and ongoing model costs. Targets and acceptance thresholds are agreed during discovery; financial or operational results are not guaranteed.\u003c\/p\u003e\u003ch3\u003eInvestment and timing\u003c\/h3\u003e\u003cp\u003eEstimated engagement: USD $90,000–$225,000. The listed product price of $90,000 is the starting professional services fee, not a deposit or a guaranteed fixed quote. Indicative delivery: 8-16 weeks after prerequisites are available. Final scope, milestones and commercial terms are agreed in a statement of work. Model\/API usage, cloud infrastructure, hardware, third-party licenses, taxes, major data remediation and ongoing managed support are excluded unless expressly included.\u003c\/p\u003e\u003ch3\u003eGetting started\u003c\/h3\u003e\u003cp\u003eProvide a named process owner, authorized access to representative data, integration documentation and operator time for review. Regulated or sensitive workflows require applicable privacy and security approvals before data access. The first working session confirms the use case, baseline, approval boundaries and the smallest valuable delivery scope.\u003c\/p\u003e","brand":"Corefinery","offers":[{"title":"Default Title","offer_id":68850798297282,"sku":"CF-AI-015","price":90000.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0798\/2101\/3186\/files\/Platinum_pathway_to_what_s_next-2.png?v=1794072948"},{"product_id":"anking-and-financial-services-advisor-knowledge-assistant","title":"Banking: Advisor knowledge assistant","description":"\u003cp\u003eAdvisors struggle to find current approved guidance. Corefinery works with your banking and financial services team to turn this bottleneck into a practical, measurable workflow. The engagement focuses on retrieve cited product and service guidance rather than a standalone demonstration.\u003c\/p\u003e\u003ch3\u003eWhat the engagement covers\u003c\/h3\u003e\u003cp\u003eOne production workflow; up to two integrations; one business unit. We map the current process with the people doing the work, identify decision rights and exceptions, and agree which actions require approval. The workflow is designed around existing systems and an accountable business owner.\u003c\/p\u003e\u003ch3\u003eWorkflow and data\u003c\/h3\u003e\u003cp\u003eSource inputs: Client-authorized source-system records; approved operating policies; representative task examples; integration documentation. Retrieve cited product and service guidance. Every recommendation should retain its supporting context so an operator can inspect the evidence and correct incomplete or inaccurate output.\u003c\/p\u003e\u003ch3\u003eEngineering approach\u003c\/h3\u003e\u003cp\u003eDeterministic components: Entitlement checks; document freshness; communication approval. These rules control calculations, eligibility and permitted actions. Non-deterministic AI handles interpretation, retrieval and draft recommendations; it does not replace the policy engine. Consequential actions require an authorized human decision.\u003c\/p\u003e\u003cp\u003eModel selection compares task quality, latency, privacy requirements and cost. Claude or OpenAI can support complex language tasks; a specialized small language model can handle narrower classification or retrieval when it meets the same evaluation criteria. Adaptation, quantization and private or edge deployment are considered only when supported by data rights, infrastructure and measured performance. Hosted vendor models remain subject to vendor deployment options and terms.\u003c\/p\u003e\u003ch3\u003eDeliverables\u003c\/h3\u003e\u003cul\u003e\n\u003cli\u003eCurrent-state workflow map, baseline metrics and prioritized requirements\u003c\/li\u003e\n\u003cli\u003eArchitecture, access boundaries, integration design and exception-handling plan\u003c\/li\u003e\n\u003cli\u003eRepresentative evaluation cases and an agreed acceptance scorecard\u003c\/li\u003e\n\u003cli\u003eWorking workflow software, scoped integrations and operator review queue\u003c\/li\u003e\n\u003cli\u003eMonitoring, audit events, deployment runbook, team training and source-code handover\u003c\/li\u003e\n\u003c\/ul\u003e\u003ch3\u003eDelivery and acceptance\u003c\/h3\u003e\u003cp\u003eForward deployed engineers work alongside your operators, with an AI engineer, integration engineer and shared quality and security support. The team reviews progress weekly, tests representative normal and exception cases, and documents limitations. Release follows owner acceptance, access-control testing and a demonstrated pause or rollback process.\u003c\/p\u003e\u003ch3\u003eHow value is measured\u003c\/h3\u003e\u003cp\u003ePrimary measures: Search time; citation validity. Establish a baseline before changes, then compare a controlled pilot or agreed pre\/post period. Track quality, adoption and attributable benefits alongside labor time and ongoing model costs. Targets and acceptance thresholds are agreed during discovery; financial or operational results are not guaranteed.\u003c\/p\u003e\u003ch3\u003eInvestment and timing\u003c\/h3\u003e\u003cp\u003eEstimated engagement: USD $100,000–$250,000. The listed product price of $100,000 is the starting professional services fee, not a deposit or a guaranteed fixed quote. Indicative delivery: 8-16 weeks after prerequisites are available. Final scope, milestones and commercial terms are agreed in a statement of work. Model\/API usage, cloud infrastructure, hardware, third-party licenses, taxes, major data remediation and ongoing managed support are excluded unless expressly included.\u003c\/p\u003e\u003ch3\u003eGetting started\u003c\/h3\u003e\u003cp\u003eProvide a named process owner, authorized access to representative data, integration documentation and operator time for review. Regulated or sensitive workflows require applicable privacy and security approvals before data access. The first working session confirms the use case, baseline, approval boundaries and the smallest valuable delivery scope.\u003c\/p\u003e","brand":"Corefinery","offers":[{"title":"Default Title","offer_id":68850798428354,"sku":"CF-AI-016","price":100000.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0798\/2101\/3186\/files\/Platinum_pathway_to_what_s_next-2.png?v=1794072948"},{"product_id":"anking-and-financial-services-fraud-operations-triage","title":"Banking: Fraud operations triage","description":"\u003cp\u003eFraud teams face noisy cases. Corefinery works with your banking and financial services team to turn this bottleneck into a practical, measurable workflow. The engagement focuses on rank alerts and summarize supporting evidence rather than a standalone demonstration.\u003c\/p\u003e\u003ch3\u003eWhat the engagement covers\u003c\/h3\u003e\u003cp\u003eOne production workflow; up to two integrations; one business unit. We map the current process with the people doing the work, identify decision rights and exceptions, and agree which actions require approval. The workflow is designed around existing systems and an accountable business owner.\u003c\/p\u003e\u003ch3\u003eWorkflow and data\u003c\/h3\u003e\u003cp\u003eSource inputs: Client-authorized source-system records; approved operating policies; representative task examples; integration documentation. Rank alerts and summarize supporting evidence. Every recommendation should retain its supporting context so an operator can inspect the evidence and correct incomplete or inaccurate output.\u003c\/p\u003e\u003ch3\u003eEngineering approach\u003c\/h3\u003e\u003cp\u003eDeterministic components: Velocity checks; hold limits; analyst release gate. These rules control calculations, eligibility and permitted actions. Non-deterministic AI handles interpretation, retrieval and draft recommendations; it does not replace the policy engine. Consequential actions require an authorized human decision.\u003c\/p\u003e\u003cp\u003eModel selection compares task quality, latency, privacy requirements and cost. Claude or OpenAI can support complex language tasks; a specialized small language model can handle narrower classification or retrieval when it meets the same evaluation criteria. Adaptation, quantization and private or edge deployment are considered only when supported by data rights, infrastructure and measured performance. Hosted vendor models remain subject to vendor deployment options and terms.\u003c\/p\u003e\u003ch3\u003eDeliverables\u003c\/h3\u003e\u003cul\u003e\n\u003cli\u003eCurrent-state workflow map, baseline metrics and prioritized requirements\u003c\/li\u003e\n\u003cli\u003eArchitecture, access boundaries, integration design and exception-handling plan\u003c\/li\u003e\n\u003cli\u003eRepresentative evaluation cases and an agreed acceptance scorecard\u003c\/li\u003e\n\u003cli\u003eWorking workflow software, scoped integrations and operator review queue\u003c\/li\u003e\n\u003cli\u003eMonitoring, audit events, deployment runbook, team training and source-code handover\u003c\/li\u003e\n\u003c\/ul\u003e\u003ch3\u003eDelivery and acceptance\u003c\/h3\u003e\u003cp\u003eForward deployed engineers work alongside your operators, with an AI engineer, integration engineer and shared quality and security support. The team reviews progress weekly, tests representative normal and exception cases, and documents limitations. Release follows owner acceptance, access-control testing and a demonstrated pause or rollback process.\u003c\/p\u003e\u003ch3\u003eHow value is measured\u003c\/h3\u003e\u003cp\u003ePrimary measures: Review time; confirmed-case precision. Establish a baseline before changes, then compare a controlled pilot or agreed pre\/post period. Track quality, adoption and attributable benefits alongside labor time and ongoing model costs. Targets and acceptance thresholds are agreed during discovery; financial or operational results are not guaranteed.\u003c\/p\u003e\u003ch3\u003eInvestment and timing\u003c\/h3\u003e\u003cp\u003eEstimated engagement: USD $125,000–$300,000. The listed product price of $125,000 is the starting professional services fee, not a deposit or a guaranteed fixed quote. Indicative delivery: 8-16 weeks after prerequisites are available. Final scope, milestones and commercial terms are agreed in a statement of work. Model\/API usage, cloud infrastructure, hardware, third-party licenses, taxes, major data remediation and ongoing managed support are excluded unless expressly included.\u003c\/p\u003e\u003ch3\u003eGetting started\u003c\/h3\u003e\u003cp\u003eProvide a named process owner, authorized access to representative data, integration documentation and operator time for review. Regulated or sensitive workflows require applicable privacy and security approvals before data access. The first working session confirms the use case, baseline, approval boundaries and the smallest valuable delivery scope.\u003c\/p\u003e","brand":"Corefinery","offers":[{"title":"Default Title","offer_id":68850798854338,"sku":"CF-AI-017","price":125000.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0798\/2101\/3186\/files\/Platinum_pathway_to_what_s_next-2.png?v=1794072948"},{"product_id":"anking-and-financial-services-regulatory-evidence-assembler","title":"Banking: Regulatory evidence assembler","description":"\u003cp\u003eControl owners repeatedly collect the same evidence. Corefinery works with your banking and financial services team to turn this bottleneck into a practical, measurable workflow. The engagement focuses on map artifacts to obligations and explain missing evidence rather than a standalone demonstration.\u003c\/p\u003e\u003ch3\u003eWhat the engagement covers\u003c\/h3\u003e\u003cp\u003eOne production workflow; up to two integrations; one business unit. We map the current process with the people doing the work, identify decision rights and exceptions, and agree which actions require approval. The workflow is designed around existing systems and an accountable business owner.\u003c\/p\u003e\u003ch3\u003eWorkflow and data\u003c\/h3\u003e\u003cp\u003eSource inputs: Client-authorized source-system records; approved operating policies; representative task examples; integration documentation. Map artifacts to obligations and explain missing evidence. Every recommendation should retain its supporting context so an operator can inspect the evidence and correct incomplete or inaccurate output.\u003c\/p\u003e\u003ch3\u003eEngineering approach\u003c\/h3\u003e\u003cp\u003eDeterministic components: Evidence timestamps; control mapping; reviewer sign-off. These rules control calculations, eligibility and permitted actions. Non-deterministic AI handles interpretation, retrieval and draft recommendations; it does not replace the policy engine. Consequential actions require an authorized human decision.\u003c\/p\u003e\u003cp\u003eModel selection compares task quality, latency, privacy requirements and cost. Claude or OpenAI can support complex language tasks; a specialized small language model can handle narrower classification or retrieval when it meets the same evaluation criteria. Adaptation, quantization and private or edge deployment are considered only when supported by data rights, infrastructure and measured performance. Hosted vendor models remain subject to vendor deployment options and terms.\u003c\/p\u003e\u003ch3\u003eDeliverables\u003c\/h3\u003e\u003cul\u003e\n\u003cli\u003eCurrent-state workflow map, baseline metrics and prioritized requirements\u003c\/li\u003e\n\u003cli\u003eArchitecture, access boundaries, integration design and exception-handling plan\u003c\/li\u003e\n\u003cli\u003eRepresentative evaluation cases and an agreed acceptance scorecard\u003c\/li\u003e\n\u003cli\u003eWorking workflow software, scoped integrations and operator review queue\u003c\/li\u003e\n\u003cli\u003eMonitoring, audit events, deployment runbook, team training and source-code handover\u003c\/li\u003e\n\u003c\/ul\u003e\u003ch3\u003eDelivery and acceptance\u003c\/h3\u003e\u003cp\u003eForward deployed engineers work alongside your operators, with an AI engineer, integration engineer and shared quality and security support. The team reviews progress weekly, tests representative normal and exception cases, and documents limitations. Release follows owner acceptance, access-control testing and a demonstrated pause or rollback process.\u003c\/p\u003e\u003ch3\u003eHow value is measured\u003c\/h3\u003e\u003cp\u003ePrimary measures: Evidence collection hours; completeness. Establish a baseline before changes, then compare a controlled pilot or agreed pre\/post period. Track quality, adoption and attributable benefits alongside labor time and ongoing model costs. Targets and acceptance thresholds are agreed during discovery; financial or operational results are not guaranteed.\u003c\/p\u003e\u003ch3\u003eInvestment and timing\u003c\/h3\u003e\u003cp\u003eEstimated engagement: USD $150,000–$350,000. The listed product price of $150,000 is the starting professional services fee, not a deposit or a guaranteed fixed quote. Indicative delivery: 8-16 weeks after prerequisites are available. Final scope, milestones and commercial terms are agreed in a statement of work. Model\/API usage, cloud infrastructure, hardware, third-party licenses, taxes, major data remediation and ongoing managed support are excluded unless expressly included.\u003c\/p\u003e\u003ch3\u003eGetting started\u003c\/h3\u003e\u003cp\u003eProvide a named process owner, authorized access to representative data, integration documentation and operator time for review. Regulated or sensitive workflows require applicable privacy and security approvals before data access. The first working session confirms the use case, baseline, approval boundaries and the smallest valuable delivery scope.\u003c\/p\u003e","brand":"Corefinery","offers":[{"title":"Default Title","offer_id":68850812354754,"sku":"CF-AI-018","price":150000.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0798\/2101\/3186\/files\/Platinum_pathway_to_what_s_next-2.png?v=1794072948"},{"product_id":"anking-and-financial-services-private-slm-service-operations","title":"Banking: Private SLM service operations","description":"\u003cp\u003eHigh-volume service requests are costly to route. Corefinery works with your banking and financial services team to turn this bottleneck into a practical, measurable workflow. The engagement focuses on classify requests and draft grounded service responses rather than a standalone demonstration.\u003c\/p\u003e\u003ch3\u003eWhat the engagement covers\u003c\/h3\u003e\u003cp\u003eOne production workflow; up to two integrations; one business unit. We map the current process with the people doing the work, identify decision rights and exceptions, and agree which actions require approval. The workflow is designed around existing systems and an accountable business owner.\u003c\/p\u003e\u003ch3\u003eWorkflow and data\u003c\/h3\u003e\u003cp\u003eSource inputs: Client-authorized source-system records; approved operating policies; representative task examples; integration documentation. Classify requests and draft grounded service responses. Every recommendation should retain its supporting context so an operator can inspect the evidence and correct incomplete or inaccurate output.\u003c\/p\u003e\u003ch3\u003eEngineering approach\u003c\/h3\u003e\u003cp\u003eDeterministic components: Identity verification; account access policy; approved intents. These rules control calculations, eligibility and permitted actions. Non-deterministic AI handles interpretation, retrieval and draft recommendations; it does not replace the policy engine. Consequential actions require an authorized human decision.\u003c\/p\u003e\u003cp\u003eModel selection compares task quality, latency, privacy requirements and cost. Claude or OpenAI can support complex language tasks; a specialized small language model can handle narrower classification or retrieval when it meets the same evaluation criteria. Adaptation, quantization and private or edge deployment are considered only when supported by data rights, infrastructure and measured performance. Hosted vendor models remain subject to vendor deployment options and terms.\u003c\/p\u003e\u003ch3\u003eDeliverables\u003c\/h3\u003e\u003cul\u003e\n\u003cli\u003eCurrent-state workflow map, baseline metrics and prioritized requirements\u003c\/li\u003e\n\u003cli\u003eArchitecture, access boundaries, integration design and exception-handling plan\u003c\/li\u003e\n\u003cli\u003eRepresentative evaluation cases and an agreed acceptance scorecard\u003c\/li\u003e\n\u003cli\u003eWorking workflow software, scoped integrations and operator review queue\u003c\/li\u003e\n\u003cli\u003eMonitoring, audit events, deployment runbook, team training and source-code handover\u003c\/li\u003e\n\u003c\/ul\u003e\u003ch3\u003eDelivery and acceptance\u003c\/h3\u003e\u003cp\u003eForward deployed engineers work alongside your operators, with an AI engineer, integration engineer and shared quality and security support. The team reviews progress weekly, tests representative normal and exception cases, and documents limitations. Release follows owner acceptance, access-control testing and a demonstrated pause or rollback process.\u003c\/p\u003e\u003ch3\u003eHow value is measured\u003c\/h3\u003e\u003cp\u003ePrimary measures: Cost per resolved request; routing precision. Establish a baseline before changes, then compare a controlled pilot or agreed pre\/post period. Track quality, adoption and attributable benefits alongside labor time and ongoing model costs. Targets and acceptance thresholds are agreed during discovery; financial or operational results are not guaranteed.\u003c\/p\u003e\u003ch3\u003eInvestment and timing\u003c\/h3\u003e\u003cp\u003eEstimated engagement: USD $175,000–$400,000. The listed product price of $175,000 is the starting professional services fee, not a deposit or a guaranteed fixed quote. Indicative delivery: 8-16 weeks after prerequisites are available. Final scope, milestones and commercial terms are agreed in a statement of work. Model\/API usage, cloud infrastructure, hardware, third-party licenses, taxes, major data remediation and ongoing managed support are excluded unless expressly included.\u003c\/p\u003e\u003ch3\u003eGetting started\u003c\/h3\u003e\u003cp\u003eProvide a named process owner, authorized access to representative data, integration documentation and operator time for review. Regulated or sensitive workflows require applicable privacy and security approvals before data access. The first working session confirms the use case, baseline, approval boundaries and the smallest valuable delivery scope.\u003c\/p\u003e","brand":"Corefinery","offers":[{"title":"Default Title","offer_id":68850812813506,"sku":"CF-AI-019","price":175000.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0798\/2101\/3186\/files\/Platinum_pathway_to_what_s_next-2.png?v=1794072948"},{"product_id":"nsurance-claims-intake-accelerator","title":"Insurance: Claims intake accelerator","description":"\u003cp\u003eIncomplete submissions slow claims handling. Corefinery works with your insurance team to turn this bottleneck into a practical, measurable workflow. The engagement focuses on extract loss details and summarize missing information rather than a standalone demonstration.\u003c\/p\u003e\u003ch3\u003eWhat the engagement covers\u003c\/h3\u003e\u003cp\u003eOne production workflow; up to two integrations; one business unit. We map the current process with the people doing the work, identify decision rights and exceptions, and agree which actions require approval. The workflow is designed around existing systems and an accountable business owner.\u003c\/p\u003e\u003ch3\u003eWorkflow and data\u003c\/h3\u003e\u003cp\u003eSource inputs: Client-authorized source-system records; approved operating policies; representative task examples; integration documentation. Extract loss details and summarize missing information. Every recommendation should retain its supporting context so an operator can inspect the evidence and correct incomplete or inaccurate output.\u003c\/p\u003e\u003ch3\u003eEngineering approach\u003c\/h3\u003e\u003cp\u003eDeterministic components: Coverage dates; required documents; adjuster routing. These rules control calculations, eligibility and permitted actions. Non-deterministic AI handles interpretation, retrieval and draft recommendations; it does not replace the policy engine. Consequential actions require an authorized human decision.\u003c\/p\u003e\u003cp\u003eModel selection compares task quality, latency, privacy requirements and cost. Claude or OpenAI can support complex language tasks; a specialized small language model can handle narrower classification or retrieval when it meets the same evaluation criteria. Adaptation, quantization and private or edge deployment are considered only when supported by data rights, infrastructure and measured performance. Hosted vendor models remain subject to vendor deployment options and terms.\u003c\/p\u003e\u003ch3\u003eDeliverables\u003c\/h3\u003e\u003cul\u003e\n\u003cli\u003eCurrent-state workflow map, baseline metrics and prioritized requirements\u003c\/li\u003e\n\u003cli\u003eArchitecture, access boundaries, integration design and exception-handling plan\u003c\/li\u003e\n\u003cli\u003eRepresentative evaluation cases and an agreed acceptance scorecard\u003c\/li\u003e\n\u003cli\u003eWorking workflow software, scoped integrations and operator review queue\u003c\/li\u003e\n\u003cli\u003eMonitoring, audit events, deployment runbook, team training and source-code handover\u003c\/li\u003e\n\u003c\/ul\u003e\u003ch3\u003eDelivery and acceptance\u003c\/h3\u003e\u003cp\u003eForward deployed engineers work alongside your operators, with an AI engineer, integration engineer and shared quality and security support. The team reviews progress weekly, tests representative normal and exception cases, and documents limitations. Release follows owner acceptance, access-control testing and a demonstrated pause or rollback process.\u003c\/p\u003e\u003ch3\u003eHow value is measured\u003c\/h3\u003e\u003cp\u003ePrimary measures: Intake time; complete-case rate. Establish a baseline before changes, then compare a controlled pilot or agreed pre\/post period. Track quality, adoption and attributable benefits alongside labor time and ongoing model costs. Targets and acceptance thresholds are agreed during discovery; financial or operational results are not guaranteed.\u003c\/p\u003e\u003ch3\u003eInvestment and timing\u003c\/h3\u003e\u003cp\u003eEstimated engagement: USD $40,000–$90,000. The listed product price of $40,000 is the starting professional services fee, not a deposit or a guaranteed fixed quote. Indicative delivery: 8-16 weeks after prerequisites are available. Final scope, milestones and commercial terms are agreed in a statement of work. Model\/API usage, cloud infrastructure, hardware, third-party licenses, taxes, major data remediation and ongoing managed support are excluded unless expressly included.\u003c\/p\u003e\u003ch3\u003eGetting started\u003c\/h3\u003e\u003cp\u003eProvide a named process owner, authorized access to representative data, integration documentation and operator time for review. Regulated or sensitive workflows require applicable privacy and security approvals before data access. The first working session confirms the use case, baseline, approval boundaries and the smallest valuable delivery scope.\u003c\/p\u003e","brand":"Corefinery","offers":[{"title":"Default Title","offer_id":68850813534402,"sku":"CF-AI-022","price":40000.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0798\/2101\/3186\/files\/Platinum_pathway_to_what_s_next-2.png?v=1794072948"},{"product_id":"nsurance-underwriting-submission-copilot","title":"Insurance: Underwriting submission copilot","description":"\u003cp\u003eUnderwriters spend time reading submissions. Corefinery works with your insurance team to turn this bottleneck into a practical, measurable workflow. The engagement focuses on extract risk facts and draft evidence-linked summaries rather than a standalone demonstration.\u003c\/p\u003e\u003ch3\u003eWhat the engagement covers\u003c\/h3\u003e\u003cp\u003eOne production workflow; up to two integrations; one business unit. We map the current process with the people doing the work, identify decision rights and exceptions, and agree which actions require approval. The workflow is designed around existing systems and an accountable business owner.\u003c\/p\u003e\u003ch3\u003eWorkflow and data\u003c\/h3\u003e\u003cp\u003eSource inputs: Client-authorized source-system records; approved operating policies; representative task examples; integration documentation. Extract risk facts and draft evidence-linked summaries. Every recommendation should retain its supporting context so an operator can inspect the evidence and correct incomplete or inaccurate output.\u003c\/p\u003e\u003ch3\u003eEngineering approach\u003c\/h3\u003e\u003cp\u003eDeterministic components: Appetite rules; authority limits; referral triggers. These rules control calculations, eligibility and permitted actions. Non-deterministic AI handles interpretation, retrieval and draft recommendations; it does not replace the policy engine. Consequential actions require an authorized human decision.\u003c\/p\u003e\u003cp\u003eModel selection compares task quality, latency, privacy requirements and cost. Claude or OpenAI can support complex language tasks; a specialized small language model can handle narrower classification or retrieval when it meets the same evaluation criteria. Adaptation, quantization and private or edge deployment are considered only when supported by data rights, infrastructure and measured performance. Hosted vendor models remain subject to vendor deployment options and terms.\u003c\/p\u003e\u003ch3\u003eDeliverables\u003c\/h3\u003e\u003cul\u003e\n\u003cli\u003eCurrent-state workflow map, baseline metrics and prioritized requirements\u003c\/li\u003e\n\u003cli\u003eArchitecture, access boundaries, integration design and exception-handling plan\u003c\/li\u003e\n\u003cli\u003eRepresentative evaluation cases and an agreed acceptance scorecard\u003c\/li\u003e\n\u003cli\u003eWorking workflow software, scoped integrations and operator review queue\u003c\/li\u003e\n\u003cli\u003eMonitoring, audit events, deployment runbook, team training and source-code handover\u003c\/li\u003e\n\u003c\/ul\u003e\u003ch3\u003eDelivery and acceptance\u003c\/h3\u003e\u003cp\u003eForward deployed engineers work alongside your operators, with an AI engineer, integration engineer and shared quality and security support. The team reviews progress weekly, tests representative normal and exception cases, and documents limitations. Release follows owner acceptance, access-control testing and a demonstrated pause or rollback process.\u003c\/p\u003e\u003ch3\u003eHow value is measured\u003c\/h3\u003e\u003cp\u003ePrimary measures: Quote turnaround; extraction accuracy. Establish a baseline before changes, then compare a controlled pilot or agreed pre\/post period. Track quality, adoption and attributable benefits alongside labor time and ongoing model costs. Targets and acceptance thresholds are agreed during discovery; financial or operational results are not guaranteed.\u003c\/p\u003e\u003ch3\u003eInvestment and timing\u003c\/h3\u003e\u003cp\u003eEstimated engagement: USD $60,000–$140,000. The listed product price of $60,000 is the starting professional services fee, not a deposit or a guaranteed fixed quote. Indicative delivery: 8-16 weeks after prerequisites are available. Final scope, milestones and commercial terms are agreed in a statement of work. Model\/API usage, cloud infrastructure, hardware, third-party licenses, taxes, major data remediation and ongoing managed support are excluded unless expressly included.\u003c\/p\u003e\u003ch3\u003eGetting started\u003c\/h3\u003e\u003cp\u003eProvide a named process owner, authorized access to representative data, integration documentation and operator time for review. Regulated or sensitive workflows require applicable privacy and security approvals before data access. The first working session confirms the use case, baseline, approval boundaries and the smallest valuable delivery scope.\u003c\/p\u003e","brand":"Corefinery","offers":[{"title":"Default Title","offer_id":68850813665474,"sku":"CF-AI-023","price":60000.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0798\/2101\/3186\/files\/Platinum_pathway_to_what_s_next-2.png?v=1794072948"},{"product_id":"nsurance-claims-leakage-review","title":"Insurance: Claims leakage review","description":"\u003cp\u003eInconsistent case review misses recoveries. Corefinery works with your insurance team to turn this bottleneck into a practical, measurable workflow. The engagement focuses on identify evidence gaps and suggest review priorities rather than a standalone demonstration.\u003c\/p\u003e\u003ch3\u003eWhat the engagement covers\u003c\/h3\u003e\u003cp\u003eOne production workflow; up to two integrations; one business unit. We map the current process with the people doing the work, identify decision rights and exceptions, and agree which actions require approval. The workflow is designed around existing systems and an accountable business owner.\u003c\/p\u003e\u003ch3\u003eWorkflow and data\u003c\/h3\u003e\u003cp\u003eSource inputs: Client-authorized source-system records; approved operating policies; representative task examples; integration documentation. Identify evidence gaps and suggest review priorities. Every recommendation should retain its supporting context so an operator can inspect the evidence and correct incomplete or inaccurate output.\u003c\/p\u003e\u003ch3\u003eEngineering approach\u003c\/h3\u003e\u003cp\u003eDeterministic components: Payment reconciliation; reserve thresholds; approval limits. These rules control calculations, eligibility and permitted actions. Non-deterministic AI handles interpretation, retrieval and draft recommendations; it does not replace the policy engine. Consequential actions require an authorized human decision.\u003c\/p\u003e\u003cp\u003eModel selection compares task quality, latency, privacy requirements and cost. Claude or OpenAI can support complex language tasks; a specialized small language model can handle narrower classification or retrieval when it meets the same evaluation criteria. Adaptation, quantization and private or edge deployment are considered only when supported by data rights, infrastructure and measured performance. Hosted vendor models remain subject to vendor deployment options and terms.\u003c\/p\u003e\u003ch3\u003eDeliverables\u003c\/h3\u003e\u003cul\u003e\n\u003cli\u003eCurrent-state workflow map, baseline metrics and prioritized requirements\u003c\/li\u003e\n\u003cli\u003eArchitecture, access boundaries, integration design and exception-handling plan\u003c\/li\u003e\n\u003cli\u003eRepresentative evaluation cases and an agreed acceptance scorecard\u003c\/li\u003e\n\u003cli\u003eWorking workflow software, scoped integrations and operator review queue\u003c\/li\u003e\n\u003cli\u003eMonitoring, audit events, deployment runbook, team training and source-code handover\u003c\/li\u003e\n\u003c\/ul\u003e\u003ch3\u003eDelivery and acceptance\u003c\/h3\u003e\u003cp\u003eForward deployed engineers work alongside your operators, with an AI engineer, integration engineer and shared quality and security support. The team reviews progress weekly, tests representative normal and exception cases, and documents limitations. Release follows owner acceptance, access-control testing and a demonstrated pause or rollback process.\u003c\/p\u003e\u003ch3\u003eHow value is measured\u003c\/h3\u003e\u003cp\u003ePrimary measures: Reviewed leakage opportunities; review time. Establish a baseline before changes, then compare a controlled pilot or agreed pre\/post period. Track quality, adoption and attributable benefits alongside labor time and ongoing model costs. Targets and acceptance thresholds are agreed during discovery; financial or operational results are not guaranteed.\u003c\/p\u003e\u003ch3\u003eInvestment and timing\u003c\/h3\u003e\u003cp\u003eEstimated engagement: USD $75,000–$180,000. The listed product price of $75,000 is the starting professional services fee, not a deposit or a guaranteed fixed quote. Indicative delivery: 8-16 weeks after prerequisites are available. Final scope, milestones and commercial terms are agreed in a statement of work. Model\/API usage, cloud infrastructure, hardware, third-party licenses, taxes, major data remediation and ongoing managed support are excluded unless expressly included.\u003c\/p\u003e\u003ch3\u003eGetting started\u003c\/h3\u003e\u003cp\u003eProvide a named process owner, authorized access to representative data, integration documentation and operator time for review. Regulated or sensitive workflows require applicable privacy and security approvals before data access. The first working session confirms the use case, baseline, approval boundaries and the smallest valuable delivery scope.\u003c\/p\u003e","brand":"Corefinery","offers":[{"title":"Default Title","offer_id":68850817695938,"sku":"CF-AI-024","price":75000.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0798\/2101\/3186\/files\/Platinum_pathway_to_what_s_next-2.png?v=1794072948"},{"product_id":"nsurance-policy-servicing-assistant","title":"Insurance: Policy servicing assistant","description":"\u003cp\u003eEndorsements generate repetitive manual work. Corefinery works with your insurance team to turn this bottleneck into a practical, measurable workflow. The engagement focuses on interpret change requests and draft servicing instructions rather than a standalone demonstration.\u003c\/p\u003e\u003ch3\u003eWhat the engagement covers\u003c\/h3\u003e\u003cp\u003eOne production workflow; up to two integrations; one business unit. We map the current process with the people doing the work, identify decision rights and exceptions, and agree which actions require approval. The workflow is designed around existing systems and an accountable business owner.\u003c\/p\u003e\u003ch3\u003eWorkflow and data\u003c\/h3\u003e\u003cp\u003eSource inputs: Client-authorized source-system records; approved operating policies; representative task examples; integration documentation. Interpret change requests and draft servicing instructions. Every recommendation should retain its supporting context so an operator can inspect the evidence and correct incomplete or inaccurate output.\u003c\/p\u003e\u003ch3\u003eEngineering approach\u003c\/h3\u003e\u003cp\u003eDeterministic components: Eligibility checks; effective dates; binding authority. These rules control calculations, eligibility and permitted actions. Non-deterministic AI handles interpretation, retrieval and draft recommendations; it does not replace the policy engine. Consequential actions require an authorized human decision.\u003c\/p\u003e\u003cp\u003eModel selection compares task quality, latency, privacy requirements and cost. Claude or OpenAI can support complex language tasks; a specialized small language model can handle narrower classification or retrieval when it meets the same evaluation criteria. Adaptation, quantization and private or edge deployment are considered only when supported by data rights, infrastructure and measured performance. Hosted vendor models remain subject to vendor deployment options and terms.\u003c\/p\u003e\u003ch3\u003eDeliverables\u003c\/h3\u003e\u003cul\u003e\n\u003cli\u003eCurrent-state workflow map, baseline metrics and prioritized requirements\u003c\/li\u003e\n\u003cli\u003eArchitecture, access boundaries, integration design and exception-handling plan\u003c\/li\u003e\n\u003cli\u003eRepresentative evaluation cases and an agreed acceptance scorecard\u003c\/li\u003e\n\u003cli\u003eWorking workflow software, scoped integrations and operator review queue\u003c\/li\u003e\n\u003cli\u003eMonitoring, audit events, deployment runbook, team training and source-code handover\u003c\/li\u003e\n\u003c\/ul\u003e\u003ch3\u003eDelivery and acceptance\u003c\/h3\u003e\u003cp\u003eForward deployed engineers work alongside your operators, with an AI engineer, integration engineer and shared quality and security support. The team reviews progress weekly, tests representative normal and exception cases, and documents limitations. Release follows owner acceptance, access-control testing and a demonstrated pause or rollback process.\u003c\/p\u003e\u003ch3\u003eHow value is measured\u003c\/h3\u003e\u003cp\u003ePrimary measures: Endorsement cycle time; rework. Establish a baseline before changes, then compare a controlled pilot or agreed pre\/post period. Track quality, adoption and attributable benefits alongside labor time and ongoing model costs. Targets and acceptance thresholds are agreed during discovery; financial or operational results are not guaranteed.\u003c\/p\u003e\u003ch3\u003eInvestment and timing\u003c\/h3\u003e\u003cp\u003eEstimated engagement: USD $90,000–$225,000. The listed product price of $90,000 is the starting professional services fee, not a deposit or a guaranteed fixed quote. Indicative delivery: 8-16 weeks after prerequisites are available. Final scope, milestones and commercial terms are agreed in a statement of work. Model\/API usage, cloud infrastructure, hardware, third-party licenses, taxes, major data remediation and ongoing managed support are excluded unless expressly included.\u003c\/p\u003e\u003ch3\u003eGetting started\u003c\/h3\u003e\u003cp\u003eProvide a named process owner, authorized access to representative data, integration documentation and operator time for review. Regulated or sensitive workflows require applicable privacy and security approvals before data access. The first working session confirms the use case, baseline, approval boundaries and the smallest valuable delivery scope.\u003c\/p\u003e","brand":"Corefinery","offers":[{"title":"Default Title","offer_id":68850817728706,"sku":"CF-AI-025","price":90000.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0798\/2101\/3186\/files\/Platinum_pathway_to_what_s_next-2.png?v=1794072948"},{"product_id":"nsurance-subrogation-opportunity-finder","title":"Insurance: Subrogation opportunity finder","description":"\u003cp\u003eRecovery opportunities remain buried in files. Corefinery works with your insurance team to turn this bottleneck into a practical, measurable workflow. The engagement focuses on extract liability evidence and suggest recovery candidates rather than a standalone demonstration.\u003c\/p\u003e\u003ch3\u003eWhat the engagement covers\u003c\/h3\u003e\u003cp\u003eOne production workflow; up to two integrations; one business unit. We map the current process with the people doing the work, identify decision rights and exceptions, and agree which actions require approval. The workflow is designed around existing systems and an accountable business owner.\u003c\/p\u003e\u003ch3\u003eWorkflow and data\u003c\/h3\u003e\u003cp\u003eSource inputs: Client-authorized source-system records; approved operating policies; representative task examples; integration documentation. Extract liability evidence and suggest recovery candidates. Every recommendation should retain its supporting context so an operator can inspect the evidence and correct incomplete or inaccurate output.\u003c\/p\u003e\u003ch3\u003eEngineering approach\u003c\/h3\u003e\u003cp\u003eDeterministic components: Recovery deadlines; payment ledger checks; referral gates. These rules control calculations, eligibility and permitted actions. Non-deterministic AI handles interpretation, retrieval and draft recommendations; it does not replace the policy engine. Consequential actions require an authorized human decision.\u003c\/p\u003e\u003cp\u003eModel selection compares task quality, latency, privacy requirements and cost. Claude or OpenAI can support complex language tasks; a specialized small language model can handle narrower classification or retrieval when it meets the same evaluation criteria. Adaptation, quantization and private or edge deployment are considered only when supported by data rights, infrastructure and measured performance. Hosted vendor models remain subject to vendor deployment options and terms.\u003c\/p\u003e\u003ch3\u003eDeliverables\u003c\/h3\u003e\u003cul\u003e\n\u003cli\u003eCurrent-state workflow map, baseline metrics and prioritized requirements\u003c\/li\u003e\n\u003cli\u003eArchitecture, access boundaries, integration design and exception-handling plan\u003c\/li\u003e\n\u003cli\u003eRepresentative evaluation cases and an agreed acceptance scorecard\u003c\/li\u003e\n\u003cli\u003eWorking workflow software, scoped integrations and operator review queue\u003c\/li\u003e\n\u003cli\u003eMonitoring, audit events, deployment runbook, team training and source-code handover\u003c\/li\u003e\n\u003c\/ul\u003e\u003ch3\u003eDelivery and acceptance\u003c\/h3\u003e\u003cp\u003eForward deployed engineers work alongside your operators, with an AI engineer, integration engineer and shared quality and security support. The team reviews progress weekly, tests representative normal and exception cases, and documents limitations. Release follows owner acceptance, access-control testing and a demonstrated pause or rollback process.\u003c\/p\u003e\u003ch3\u003eHow value is measured\u003c\/h3\u003e\u003cp\u003ePrimary measures: Validated recovery opportunities; case preparation hours. Establish a baseline before changes, then compare a controlled pilot or agreed pre\/post period. Track quality, adoption and attributable benefits alongside labor time and ongoing model costs. Targets and acceptance thresholds are agreed during discovery; financial or operational results are not guaranteed.\u003c\/p\u003e\u003ch3\u003eInvestment and timing\u003c\/h3\u003e\u003cp\u003eEstimated engagement: USD $100,000–$250,000. The listed product price of $100,000 is the starting professional services fee, not a deposit or a guaranteed fixed quote. Indicative delivery: 8-16 weeks after prerequisites are available. Final scope, milestones and commercial terms are agreed in a statement of work. Model\/API usage, cloud infrastructure, hardware, third-party licenses, taxes, major data remediation and ongoing managed support are excluded unless expressly included.\u003c\/p\u003e\u003ch3\u003eGetting started\u003c\/h3\u003e\u003cp\u003eProvide a named process owner, authorized access to representative data, integration documentation and operator time for review. Regulated or sensitive workflows require applicable privacy and security approvals before data access. The first working session confirms the use case, baseline, approval boundaries and the smallest valuable delivery scope.\u003c\/p\u003e","brand":"Corefinery","offers":[{"title":"Default Title","offer_id":68850817761474,"sku":"CF-AI-026","price":100000.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0798\/2101\/3186\/files\/Platinum_pathway_to_what_s_next-2.png?v=1794072948"},{"product_id":"nsurance-broker-placement-copilot","title":"Insurance: Broker placement copilot","description":"\u003cp\u003ePlacement teams compare inconsistent documents. Corefinery works with your insurance team to turn this bottleneck into a practical, measurable workflow. The engagement focuses on summarize policy differences and draft broker questions rather than a standalone demonstration.\u003c\/p\u003e\u003ch3\u003eWhat the engagement covers\u003c\/h3\u003e\u003cp\u003eOne production workflow; up to two integrations; one business unit. We map the current process with the people doing the work, identify decision rights and exceptions, and agree which actions require approval. The workflow is designed around existing systems and an accountable business owner.\u003c\/p\u003e\u003ch3\u003eWorkflow and data\u003c\/h3\u003e\u003cp\u003eSource inputs: Client-authorized source-system records; approved operating policies; representative task examples; integration documentation. Summarize policy differences and draft broker questions. Every recommendation should retain its supporting context so an operator can inspect the evidence and correct incomplete or inaccurate output.\u003c\/p\u003e\u003ch3\u003eEngineering approach\u003c\/h3\u003e\u003cp\u003eDeterministic components: Coverage comparison schema; exclusion checks; approval gate. These rules control calculations, eligibility and permitted actions. Non-deterministic AI handles interpretation, retrieval and draft recommendations; it does not replace the policy engine. Consequential actions require an authorized human decision.\u003c\/p\u003e\u003cp\u003eModel selection compares task quality, latency, privacy requirements and cost. Claude or OpenAI can support complex language tasks; a specialized small language model can handle narrower classification or retrieval when it meets the same evaluation criteria. Adaptation, quantization and private or edge deployment are considered only when supported by data rights, infrastructure and measured performance. Hosted vendor models remain subject to vendor deployment options and terms.\u003c\/p\u003e\u003ch3\u003eDeliverables\u003c\/h3\u003e\u003cul\u003e\n\u003cli\u003eCurrent-state workflow map, baseline metrics and prioritized requirements\u003c\/li\u003e\n\u003cli\u003eArchitecture, access boundaries, integration design and exception-handling plan\u003c\/li\u003e\n\u003cli\u003eRepresentative evaluation cases and an agreed acceptance scorecard\u003c\/li\u003e\n\u003cli\u003eWorking workflow software, scoped integrations and operator review queue\u003c\/li\u003e\n\u003cli\u003eMonitoring, audit events, deployment runbook, team training and source-code handover\u003c\/li\u003e\n\u003c\/ul\u003e\u003ch3\u003eDelivery and acceptance\u003c\/h3\u003e\u003cp\u003eForward deployed engineers work alongside your operators, with an AI engineer, integration engineer and shared quality and security support. The team reviews progress weekly, tests representative normal and exception cases, and documents limitations. Release follows owner acceptance, access-control testing and a demonstrated pause or rollback process.\u003c\/p\u003e\u003ch3\u003eHow value is measured\u003c\/h3\u003e\u003cp\u003ePrimary measures: Comparison time; omitted-clause rate. Establish a baseline before changes, then compare a controlled pilot or agreed pre\/post period. Track quality, adoption and attributable benefits alongside labor time and ongoing model costs. Targets and acceptance thresholds are agreed during discovery; financial or operational results are not guaranteed.\u003c\/p\u003e\u003ch3\u003eInvestment and timing\u003c\/h3\u003e\u003cp\u003eEstimated engagement: USD $125,000–$300,000. The listed product price of $125,000 is the starting professional services fee, not a deposit or a guaranteed fixed quote. Indicative delivery: 8-16 weeks after prerequisites are available. Final scope, milestones and commercial terms are agreed in a statement of work. Model\/API usage, cloud infrastructure, hardware, third-party licenses, taxes, major data remediation and ongoing managed support are excluded unless expressly included.\u003c\/p\u003e\u003ch3\u003eGetting started\u003c\/h3\u003e\u003cp\u003eProvide a named process owner, authorized access to representative data, integration documentation and operator time for review. Regulated or sensitive workflows require applicable privacy and security approvals before data access. The first working session confirms the use case, baseline, approval boundaries and the smallest valuable delivery scope.\u003c\/p\u003e","brand":"Corefinery","offers":[{"title":"Default Title","offer_id":68850817794242,"sku":"CF-AI-027","price":125000.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0798\/2101\/3186\/files\/Platinum_pathway_to_what_s_next-2.png?v=1794072948"},{"product_id":"nsurance-catastrophe-response-planner","title":"Insurance: Catastrophe response planner","description":"\u003cp\u003eSurges overwhelm claims staffing. Corefinery works with your insurance team to turn this bottleneck into a practical, measurable workflow. The engagement focuses on summarize event exposure and propose triage priorities rather than a standalone demonstration.\u003c\/p\u003e\u003ch3\u003eWhat the engagement covers\u003c\/h3\u003e\u003cp\u003eOne production workflow; up to two integrations; one business unit. We map the current process with the people doing the work, identify decision rights and exceptions, and agree which actions require approval. The workflow is designed around existing systems and an accountable business owner.\u003c\/p\u003e\u003ch3\u003eWorkflow and data\u003c\/h3\u003e\u003cp\u003eSource inputs: Client-authorized source-system records; approved operating policies; representative task examples; integration documentation. Summarize event exposure and propose triage priorities. Every recommendation should retain its supporting context so an operator can inspect the evidence and correct incomplete or inaccurate output.\u003c\/p\u003e\u003ch3\u003eEngineering approach\u003c\/h3\u003e\u003cp\u003eDeterministic components: Territory limits; adjuster availability; assignment caps. These rules control calculations, eligibility and permitted actions. Non-deterministic AI handles interpretation, retrieval and draft recommendations; it does not replace the policy engine. Consequential actions require an authorized human decision.\u003c\/p\u003e\u003cp\u003eModel selection compares task quality, latency, privacy requirements and cost. Claude or OpenAI can support complex language tasks; a specialized small language model can handle narrower classification or retrieval when it meets the same evaluation criteria. Adaptation, quantization and private or edge deployment are considered only when supported by data rights, infrastructure and measured performance. Hosted vendor models remain subject to vendor deployment options and terms.\u003c\/p\u003e\u003ch3\u003eDeliverables\u003c\/h3\u003e\u003cul\u003e\n\u003cli\u003eCurrent-state workflow map, baseline metrics and prioritized requirements\u003c\/li\u003e\n\u003cli\u003eArchitecture, access boundaries, integration design and exception-handling plan\u003c\/li\u003e\n\u003cli\u003eRepresentative evaluation cases and an agreed acceptance scorecard\u003c\/li\u003e\n\u003cli\u003eWorking workflow software, scoped integrations and operator review queue\u003c\/li\u003e\n\u003cli\u003eMonitoring, audit events, deployment runbook, team training and source-code handover\u003c\/li\u003e\n\u003c\/ul\u003e\u003ch3\u003eDelivery and acceptance\u003c\/h3\u003e\u003cp\u003eForward deployed engineers work alongside your operators, with an AI engineer, integration engineer and shared quality and security support. The team reviews progress weekly, tests representative normal and exception cases, and documents limitations. Release follows owner acceptance, access-control testing and a demonstrated pause or rollback process.\u003c\/p\u003e\u003ch3\u003eHow value is measured\u003c\/h3\u003e\u003cp\u003ePrimary measures: Assignment time; backlog age. Establish a baseline before changes, then compare a controlled pilot or agreed pre\/post period. Track quality, adoption and attributable benefits alongside labor time and ongoing model costs. Targets and acceptance thresholds are agreed during discovery; financial or operational results are not guaranteed.\u003c\/p\u003e\u003ch3\u003eInvestment and timing\u003c\/h3\u003e\u003cp\u003eEstimated engagement: USD $150,000–$350,000. The listed product price of $150,000 is the starting professional services fee, not a deposit or a guaranteed fixed quote. Indicative delivery: 8-16 weeks after prerequisites are available. Final scope, milestones and commercial terms are agreed in a statement of work. Model\/API usage, cloud infrastructure, hardware, third-party licenses, taxes, major data remediation and ongoing managed support are excluded unless expressly included.\u003c\/p\u003e\u003ch3\u003eGetting started\u003c\/h3\u003e\u003cp\u003eProvide a named process owner, authorized access to representative data, integration documentation and operator time for review. Regulated or sensitive workflows require applicable privacy and security approvals before data access. The first working session confirms the use case, baseline, approval boundaries and the smallest valuable delivery scope.\u003c\/p\u003e","brand":"Corefinery","offers":[{"title":"Default Title","offer_id":68850824773826,"sku":"CF-AI-028","price":150000.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0798\/2101\/3186\/files\/Platinum_pathway_to_what_s_next-2.png?v=1794072948"},{"product_id":"nsurance-private-slm-policy-knowledge-engine","title":"Insurance: Private SLM policy knowledge engine","description":"\u003cp\u003eTeams need economical access to complex wordings. Corefinery works with your insurance team to turn this bottleneck into a practical, measurable workflow. The engagement focuses on retrieve policy clauses and summarize wording differences rather than a standalone demonstration.\u003c\/p\u003e\u003ch3\u003eWhat the engagement covers\u003c\/h3\u003e\u003cp\u003eOne production workflow; up to two integrations; one business unit. We map the current process with the people doing the work, identify decision rights and exceptions, and agree which actions require approval. The workflow is designed around existing systems and an accountable business owner.\u003c\/p\u003e\u003ch3\u003eWorkflow and data\u003c\/h3\u003e\u003cp\u003eSource inputs: Client-authorized source-system records; approved operating policies; representative task examples; integration documentation. Retrieve policy clauses and summarize wording differences. Every recommendation should retain its supporting context so an operator can inspect the evidence and correct incomplete or inaccurate output.\u003c\/p\u003e\u003ch3\u003eEngineering approach\u003c\/h3\u003e\u003cp\u003eDeterministic components: Version control; entitlements; source citation checks. These rules control calculations, eligibility and permitted actions. Non-deterministic AI handles interpretation, retrieval and draft recommendations; it does not replace the policy engine. Consequential actions require an authorized human decision.\u003c\/p\u003e\u003cp\u003eModel selection compares task quality, latency, privacy requirements and cost. Claude or OpenAI can support complex language tasks; a specialized small language model can handle narrower classification or retrieval when it meets the same evaluation criteria. Adaptation, quantization and private or edge deployment are considered only when supported by data rights, infrastructure and measured performance. Hosted vendor models remain subject to vendor deployment options and terms.\u003c\/p\u003e\u003ch3\u003eDeliverables\u003c\/h3\u003e\u003cul\u003e\n\u003cli\u003eCurrent-state workflow map, baseline metrics and prioritized requirements\u003c\/li\u003e\n\u003cli\u003eArchitecture, access boundaries, integration design and exception-handling plan\u003c\/li\u003e\n\u003cli\u003eRepresentative evaluation cases and an agreed acceptance scorecard\u003c\/li\u003e\n\u003cli\u003eWorking workflow software, scoped integrations and operator review queue\u003c\/li\u003e\n\u003cli\u003eMonitoring, audit events, deployment runbook, team training and source-code handover\u003c\/li\u003e\n\u003c\/ul\u003e\u003ch3\u003eDelivery and acceptance\u003c\/h3\u003e\u003cp\u003eForward deployed engineers work alongside your operators, with an AI engineer, integration engineer and shared quality and security support. The team reviews progress weekly, tests representative normal and exception cases, and documents limitations. Release follows owner acceptance, access-control testing and a demonstrated pause or rollback process.\u003c\/p\u003e\u003ch3\u003eHow value is measured\u003c\/h3\u003e\u003cp\u003ePrimary measures: Cost per answer; grounded answer accuracy. Establish a baseline before changes, then compare a controlled pilot or agreed pre\/post period. Track quality, adoption and attributable benefits alongside labor time and ongoing model costs. Targets and acceptance thresholds are agreed during discovery; financial or operational results are not guaranteed.\u003c\/p\u003e\u003ch3\u003eInvestment and timing\u003c\/h3\u003e\u003cp\u003eEstimated engagement: USD $175,000–$400,000. The listed product price of $175,000 is the starting professional services fee, not a deposit or a guaranteed fixed quote. Indicative delivery: 8-16 weeks after prerequisites are available. Final scope, milestones and commercial terms are agreed in a statement of work. Model\/API usage, cloud infrastructure, hardware, third-party licenses, taxes, major data remediation and ongoing managed support are excluded unless expressly included.\u003c\/p\u003e\u003ch3\u003eGetting started\u003c\/h3\u003e\u003cp\u003eProvide a named process owner, authorized access to representative data, integration documentation and operator time for review. Regulated or sensitive workflows require applicable privacy and security approvals before data access. The first working session confirms the use case, baseline, approval boundaries and the smallest valuable delivery scope.\u003c\/p\u003e","brand":"Corefinery","offers":[{"title":"Default Title","offer_id":68850824806594,"sku":"CF-AI-029","price":175000.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0798\/2101\/3186\/files\/Platinum_pathway_to_what_s_next-2.png?v=1794072948"},{"product_id":"etail-and-consumer-goods-agentic-product-discovery","title":"Retail: Agentic product discovery","description":"\u003cp\u003eCustomers struggle to find suitable products. Corefinery works with your retail and consumer goods team to turn this bottleneck into a practical, measurable workflow. The engagement focuses on interpret shopper intent and explain product matches rather than a standalone demonstration.\u003c\/p\u003e\u003ch3\u003eWhat the engagement covers\u003c\/h3\u003e\u003cp\u003eOne production workflow; up to two integrations; one business unit. We map the current process with the people doing the work, identify decision rights and exceptions, and agree which actions require approval. The workflow is designed around existing systems and an accountable business owner.\u003c\/p\u003e\u003ch3\u003eWorkflow and data\u003c\/h3\u003e\u003cp\u003eSource inputs: Client-authorized source-system records; approved operating policies; representative task examples; integration documentation. Interpret shopper intent and explain product matches. Every recommendation should retain its supporting context so an operator can inspect the evidence and correct incomplete or inaccurate output.\u003c\/p\u003e\u003ch3\u003eEngineering approach\u003c\/h3\u003e\u003cp\u003eDeterministic components: Stock checks; price rules; attribute filters. These rules control calculations, eligibility and permitted actions. Non-deterministic AI handles interpretation, retrieval and draft recommendations; it does not replace the policy engine. Consequential actions require an authorized human decision.\u003c\/p\u003e\u003cp\u003eModel selection compares task quality, latency, privacy requirements and cost. Claude or OpenAI can support complex language tasks; a specialized small language model can handle narrower classification or retrieval when it meets the same evaluation criteria. Adaptation, quantization and private or edge deployment are considered only when supported by data rights, infrastructure and measured performance. Hosted vendor models remain subject to vendor deployment options and terms.\u003c\/p\u003e\u003ch3\u003eDeliverables\u003c\/h3\u003e\u003cul\u003e\n\u003cli\u003eCurrent-state workflow map, baseline metrics and prioritized requirements\u003c\/li\u003e\n\u003cli\u003eArchitecture, access boundaries, integration design and exception-handling plan\u003c\/li\u003e\n\u003cli\u003eRepresentative evaluation cases and an agreed acceptance scorecard\u003c\/li\u003e\n\u003cli\u003eWorking workflow software, scoped integrations and operator review queue\u003c\/li\u003e\n\u003cli\u003eMonitoring, audit events, deployment runbook, team training and source-code handover\u003c\/li\u003e\n\u003c\/ul\u003e\u003ch3\u003eDelivery and acceptance\u003c\/h3\u003e\u003cp\u003eForward deployed engineers work alongside your operators, with an AI engineer, integration engineer and shared quality and security support. The team reviews progress weekly, tests representative normal and exception cases, and documents limitations. Release follows owner acceptance, access-control testing and a demonstrated pause or rollback process.\u003c\/p\u003e\u003ch3\u003eHow value is measured\u003c\/h3\u003e\u003cp\u003ePrimary measures: Search-to-cart rate; assisted conversion. Establish a baseline before changes, then compare a controlled pilot or agreed pre\/post period. Track quality, adoption and attributable benefits alongside labor time and ongoing model costs. Targets and acceptance thresholds are agreed during discovery; financial or operational results are not guaranteed.\u003c\/p\u003e\u003ch3\u003eInvestment and timing\u003c\/h3\u003e\u003cp\u003eEstimated engagement: USD $40,000–$90,000. The listed product price of $40,000 is the starting professional services fee, not a deposit or a guaranteed fixed quote. Indicative delivery: 8-16 weeks after prerequisites are available. Final scope, milestones and commercial terms are agreed in a statement of work. Model\/API usage, cloud infrastructure, hardware, third-party licenses, taxes, major data remediation and ongoing managed support are excluded unless expressly included.\u003c\/p\u003e\u003ch3\u003eGetting started\u003c\/h3\u003e\u003cp\u003eProvide a named process owner, authorized access to representative data, integration documentation and operator time for review. Regulated or sensitive workflows require applicable privacy and security approvals before data access. The first working session confirms the use case, baseline, approval boundaries and the smallest valuable delivery scope.\u003c\/p\u003e","brand":"Corefinery","offers":[{"title":"Default Title","offer_id":68850824904898,"sku":"CF-AI-032","price":40000.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0798\/2101\/3186\/files\/Platinum_pathway_to_what_s_next-2.png?v=1794072948"},{"product_id":"etail-and-consumer-goods-merchandising-intelligence-studio","title":"Retail: Merchandising intelligence studio","description":"\u003cp\u003eAssortment decisions lack unified signals. Corefinery works with your retail and consumer goods team to turn this bottleneck into a practical, measurable workflow. The engagement focuses on summarize demand signals and propose assortment actions rather than a standalone demonstration.\u003c\/p\u003e\u003ch3\u003eWhat the engagement covers\u003c\/h3\u003e\u003cp\u003eOne production workflow; up to two integrations; one business unit. We map the current process with the people doing the work, identify decision rights and exceptions, and agree which actions require approval. The workflow is designed around existing systems and an accountable business owner.\u003c\/p\u003e\u003ch3\u003eWorkflow and data\u003c\/h3\u003e\u003cp\u003eSource inputs: Client-authorized source-system records; approved operating policies; representative task examples; integration documentation. Summarize demand signals and propose assortment actions. Every recommendation should retain its supporting context so an operator can inspect the evidence and correct incomplete or inaccurate output.\u003c\/p\u003e\u003ch3\u003eEngineering approach\u003c\/h3\u003e\u003cp\u003eDeterministic components: Margin floors; inventory limits; category rules. These rules control calculations, eligibility and permitted actions. Non-deterministic AI handles interpretation, retrieval and draft recommendations; it does not replace the policy engine. Consequential actions require an authorized human decision.\u003c\/p\u003e\u003cp\u003eModel selection compares task quality, latency, privacy requirements and cost. Claude or OpenAI can support complex language tasks; a specialized small language model can handle narrower classification or retrieval when it meets the same evaluation criteria. Adaptation, quantization and private or edge deployment are considered only when supported by data rights, infrastructure and measured performance. Hosted vendor models remain subject to vendor deployment options and terms.\u003c\/p\u003e\u003ch3\u003eDeliverables\u003c\/h3\u003e\u003cul\u003e\n\u003cli\u003eCurrent-state workflow map, baseline metrics and prioritized requirements\u003c\/li\u003e\n\u003cli\u003eArchitecture, access boundaries, integration design and exception-handling plan\u003c\/li\u003e\n\u003cli\u003eRepresentative evaluation cases and an agreed acceptance scorecard\u003c\/li\u003e\n\u003cli\u003eWorking workflow software, scoped integrations and operator review queue\u003c\/li\u003e\n\u003cli\u003eMonitoring, audit events, deployment runbook, team training and source-code handover\u003c\/li\u003e\n\u003c\/ul\u003e\u003ch3\u003eDelivery and acceptance\u003c\/h3\u003e\u003cp\u003eForward deployed engineers work alongside your operators, with an AI engineer, integration engineer and shared quality and security support. The team reviews progress weekly, tests representative normal and exception cases, and documents limitations. Release follows owner acceptance, access-control testing and a demonstrated pause or rollback process.\u003c\/p\u003e\u003ch3\u003eHow value is measured\u003c\/h3\u003e\u003cp\u003ePrimary measures: Sell-through; aged inventory. Establish a baseline before changes, then compare a controlled pilot or agreed pre\/post period. Track quality, adoption and attributable benefits alongside labor time and ongoing model costs. Targets and acceptance thresholds are agreed during discovery; financial or operational results are not guaranteed.\u003c\/p\u003e\u003ch3\u003eInvestment and timing\u003c\/h3\u003e\u003cp\u003eEstimated engagement: USD $60,000–$140,000. The listed product price of $60,000 is the starting professional services fee, not a deposit or a guaranteed fixed quote. Indicative delivery: 8-16 weeks after prerequisites are available. Final scope, milestones and commercial terms are agreed in a statement of work. Model\/API usage, cloud infrastructure, hardware, third-party licenses, taxes, major data remediation and ongoing managed support are excluded unless expressly included.\u003c\/p\u003e\u003ch3\u003eGetting started\u003c\/h3\u003e\u003cp\u003eProvide a named process owner, authorized access to representative data, integration documentation and operator time for review. Regulated or sensitive workflows require applicable privacy and security approvals before data access. The first working session confirms the use case, baseline, approval boundaries and the smallest valuable delivery scope.\u003c\/p\u003e","brand":"Corefinery","offers":[{"title":"Default Title","offer_id":68850837389506,"sku":"CF-AI-033","price":60000.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0798\/2101\/3186\/files\/Platinum_pathway_to_what_s_next-2.png?v=1794072948"},{"product_id":"etail-and-consumer-goods-inventory-allocation-optimizer","title":"Retail: Inventory allocation optimizer","description":"\u003cp\u003eStock is mismatched across locations. Corefinery works with your retail and consumer goods team to turn this bottleneck into a practical, measurable workflow. The engagement focuses on explain demand exceptions and propose allocation scenarios rather than a standalone demonstration.\u003c\/p\u003e\u003ch3\u003eWhat the engagement covers\u003c\/h3\u003e\u003cp\u003eOne production workflow; up to two integrations; one business unit. We map the current process with the people doing the work, identify decision rights and exceptions, and agree which actions require approval. The workflow is designed around existing systems and an accountable business owner.\u003c\/p\u003e\u003ch3\u003eWorkflow and data\u003c\/h3\u003e\u003cp\u003eSource inputs: Client-authorized source-system records; approved operating policies; representative task examples; integration documentation. Explain demand exceptions and propose allocation scenarios. Every recommendation should retain its supporting context so an operator can inspect the evidence and correct incomplete or inaccurate output.\u003c\/p\u003e\u003ch3\u003eEngineering approach\u003c\/h3\u003e\u003cp\u003eDeterministic components: Inventory conservation; transfer costs; capacity constraints. These rules control calculations, eligibility and permitted actions. Non-deterministic AI handles interpretation, retrieval and draft recommendations; it does not replace the policy engine. Consequential actions require an authorized human decision.\u003c\/p\u003e\u003cp\u003eModel selection compares task quality, latency, privacy requirements and cost. Claude or OpenAI can support complex language tasks; a specialized small language model can handle narrower classification or retrieval when it meets the same evaluation criteria. Adaptation, quantization and private or edge deployment are considered only when supported by data rights, infrastructure and measured performance. Hosted vendor models remain subject to vendor deployment options and terms.\u003c\/p\u003e\u003ch3\u003eDeliverables\u003c\/h3\u003e\u003cul\u003e\n\u003cli\u003eCurrent-state workflow map, baseline metrics and prioritized requirements\u003c\/li\u003e\n\u003cli\u003eArchitecture, access boundaries, integration design and exception-handling plan\u003c\/li\u003e\n\u003cli\u003eRepresentative evaluation cases and an agreed acceptance scorecard\u003c\/li\u003e\n\u003cli\u003eWorking workflow software, scoped integrations and operator review queue\u003c\/li\u003e\n\u003cli\u003eMonitoring, audit events, deployment runbook, team training and source-code handover\u003c\/li\u003e\n\u003c\/ul\u003e\u003ch3\u003eDelivery and acceptance\u003c\/h3\u003e\u003cp\u003eForward deployed engineers work alongside your operators, with an AI engineer, integration engineer and shared quality and security support. The team reviews progress weekly, tests representative normal and exception cases, and documents limitations. Release follows owner acceptance, access-control testing and a demonstrated pause or rollback process.\u003c\/p\u003e\u003ch3\u003eHow value is measured\u003c\/h3\u003e\u003cp\u003ePrimary measures: Stockout rate; transfer economics. Establish a baseline before changes, then compare a controlled pilot or agreed pre\/post period. Track quality, adoption and attributable benefits alongside labor time and ongoing model costs. Targets and acceptance thresholds are agreed during discovery; financial or operational results are not guaranteed.\u003c\/p\u003e\u003ch3\u003eInvestment and timing\u003c\/h3\u003e\u003cp\u003eEstimated engagement: USD $75,000–$180,000. The listed product price of $75,000 is the starting professional services fee, not a deposit or a guaranteed fixed quote. Indicative delivery: 8-16 weeks after prerequisites are available. Final scope, milestones and commercial terms are agreed in a statement of work. Model\/API usage, cloud infrastructure, hardware, third-party licenses, taxes, major data remediation and ongoing managed support are excluded unless expressly included.\u003c\/p\u003e\u003ch3\u003eGetting started\u003c\/h3\u003e\u003cp\u003eProvide a named process owner, authorized access to representative data, integration documentation and operator time for review. Regulated or sensitive workflows require applicable privacy and security approvals before data access. The first working session confirms the use case, baseline, approval boundaries and the smallest valuable delivery scope.\u003c\/p\u003e","brand":"Corefinery","offers":[{"title":"Default Title","offer_id":68850837422274,"sku":"CF-AI-034","price":75000.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0798\/2101\/3186\/files\/Platinum_pathway_to_what_s_next-2.png?v=1794072948"},{"product_id":"etail-and-consumer-goods-returns-resolution-copilot","title":"Retail: Returns resolution copilot","description":"\u003cp\u003eReturns consume service capacity. Corefinery works with your retail and consumer goods team to turn this bottleneck into a practical, measurable workflow. The engagement focuses on summarize issues and propose policy-grounded resolutions rather than a standalone demonstration.\u003c\/p\u003e\u003ch3\u003eWhat the engagement covers\u003c\/h3\u003e\u003cp\u003eOne production workflow; up to two integrations; one business unit. We map the current process with the people doing the work, identify decision rights and exceptions, and agree which actions require approval. The workflow is designed around existing systems and an accountable business owner.\u003c\/p\u003e\u003ch3\u003eWorkflow and data\u003c\/h3\u003e\u003cp\u003eSource inputs: Client-authorized source-system records; approved operating policies; representative task examples; integration documentation. Summarize issues and propose policy-grounded resolutions. Every recommendation should retain its supporting context so an operator can inspect the evidence and correct incomplete or inaccurate output.\u003c\/p\u003e\u003ch3\u003eEngineering approach\u003c\/h3\u003e\u003cp\u003eDeterministic components: Return window; refund limits; approval gates. These rules control calculations, eligibility and permitted actions. Non-deterministic AI handles interpretation, retrieval and draft recommendations; it does not replace the policy engine. Consequential actions require an authorized human decision.\u003c\/p\u003e\u003cp\u003eModel selection compares task quality, latency, privacy requirements and cost. Claude or OpenAI can support complex language tasks; a specialized small language model can handle narrower classification or retrieval when it meets the same evaluation criteria. Adaptation, quantization and private or edge deployment are considered only when supported by data rights, infrastructure and measured performance. Hosted vendor models remain subject to vendor deployment options and terms.\u003c\/p\u003e\u003ch3\u003eDeliverables\u003c\/h3\u003e\u003cul\u003e\n\u003cli\u003eCurrent-state workflow map, baseline metrics and prioritized requirements\u003c\/li\u003e\n\u003cli\u003eArchitecture, access boundaries, integration design and exception-handling plan\u003c\/li\u003e\n\u003cli\u003eRepresentative evaluation cases and an agreed acceptance scorecard\u003c\/li\u003e\n\u003cli\u003eWorking workflow software, scoped integrations and operator review queue\u003c\/li\u003e\n\u003cli\u003eMonitoring, audit events, deployment runbook, team training and source-code handover\u003c\/li\u003e\n\u003c\/ul\u003e\u003ch3\u003eDelivery and acceptance\u003c\/h3\u003e\u003cp\u003eForward deployed engineers work alongside your operators, with an AI engineer, integration engineer and shared quality and security support. The team reviews progress weekly, tests representative normal and exception cases, and documents limitations. Release follows owner acceptance, access-control testing and a demonstrated pause or rollback process.\u003c\/p\u003e\u003ch3\u003eHow value is measured\u003c\/h3\u003e\u003cp\u003ePrimary measures: Resolution time; repeat contact rate. Establish a baseline before changes, then compare a controlled pilot or agreed pre\/post period. Track quality, adoption and attributable benefits alongside labor time and ongoing model costs. Targets and acceptance thresholds are agreed during discovery; financial or operational results are not guaranteed.\u003c\/p\u003e\u003ch3\u003eInvestment and timing\u003c\/h3\u003e\u003cp\u003eEstimated engagement: USD $90,000–$225,000. The listed product price of $90,000 is the starting professional services fee, not a deposit or a guaranteed fixed quote. Indicative delivery: 8-16 weeks after prerequisites are available. Final scope, milestones and commercial terms are agreed in a statement of work. Model\/API usage, cloud infrastructure, hardware, third-party licenses, taxes, major data remediation and ongoing managed support are excluded unless expressly included.\u003c\/p\u003e\u003ch3\u003eGetting started\u003c\/h3\u003e\u003cp\u003eProvide a named process owner, authorized access to representative data, integration documentation and operator time for review. Regulated or sensitive workflows require applicable privacy and security approvals before data access. The first working session confirms the use case, baseline, approval boundaries and the smallest valuable delivery scope.\u003c\/p\u003e","brand":"Corefinery","offers":[{"title":"Default Title","offer_id":68850837455042,"sku":"CF-AI-035","price":90000.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0798\/2101\/3186\/files\/Platinum_pathway_to_what_s_next-2.png?v=1794072948"},{"product_id":"etail-and-consumer-goods-catalog-enrichment-factory","title":"Retail: Catalog enrichment factory","description":"\u003cp\u003eIncomplete product data weakens discovery. Corefinery works with your retail and consumer goods team to turn this bottleneck into a practical, measurable workflow. The engagement focuses on generate descriptions and normalize attributes from source data rather than a standalone demonstration.\u003c\/p\u003e\u003ch3\u003eWhat the engagement covers\u003c\/h3\u003e\u003cp\u003eOne production workflow; up to two integrations; one business unit. We map the current process with the people doing the work, identify decision rights and exceptions, and agree which actions require approval. The workflow is designed around existing systems and an accountable business owner.\u003c\/p\u003e\u003ch3\u003eWorkflow and data\u003c\/h3\u003e\u003cp\u003eSource inputs: Client-authorized source-system records; approved operating policies; representative task examples; integration documentation. Generate descriptions and normalize attributes from source data. Every recommendation should retain its supporting context so an operator can inspect the evidence and correct incomplete or inaccurate output.\u003c\/p\u003e\u003ch3\u003eEngineering approach\u003c\/h3\u003e\u003cp\u003eDeterministic components: Required attributes; taxonomy checks; prohibited claim rules. These rules control calculations, eligibility and permitted actions. Non-deterministic AI handles interpretation, retrieval and draft recommendations; it does not replace the policy engine. Consequential actions require an authorized human decision.\u003c\/p\u003e\u003cp\u003eModel selection compares task quality, latency, privacy requirements and cost. Claude or OpenAI can support complex language tasks; a specialized small language model can handle narrower classification or retrieval when it meets the same evaluation criteria. Adaptation, quantization and private or edge deployment are considered only when supported by data rights, infrastructure and measured performance. Hosted vendor models remain subject to vendor deployment options and terms.\u003c\/p\u003e\u003ch3\u003eDeliverables\u003c\/h3\u003e\u003cul\u003e\n\u003cli\u003eCurrent-state workflow map, baseline metrics and prioritized requirements\u003c\/li\u003e\n\u003cli\u003eArchitecture, access boundaries, integration design and exception-handling plan\u003c\/li\u003e\n\u003cli\u003eRepresentative evaluation cases and an agreed acceptance scorecard\u003c\/li\u003e\n\u003cli\u003eWorking workflow software, scoped integrations and operator review queue\u003c\/li\u003e\n\u003cli\u003eMonitoring, audit events, deployment runbook, team training and source-code handover\u003c\/li\u003e\n\u003c\/ul\u003e\u003ch3\u003eDelivery and acceptance\u003c\/h3\u003e\u003cp\u003eForward deployed engineers work alongside your operators, with an AI engineer, integration engineer and shared quality and security support. The team reviews progress weekly, tests representative normal and exception cases, and documents limitations. Release follows owner acceptance, access-control testing and a demonstrated pause or rollback process.\u003c\/p\u003e\u003ch3\u003eHow value is measured\u003c\/h3\u003e\u003cp\u003ePrimary measures: Attribute completeness; editing time. Establish a baseline before changes, then compare a controlled pilot or agreed pre\/post period. Track quality, adoption and attributable benefits alongside labor time and ongoing model costs. Targets and acceptance thresholds are agreed during discovery; financial or operational results are not guaranteed.\u003c\/p\u003e\u003ch3\u003eInvestment and timing\u003c\/h3\u003e\u003cp\u003eEstimated engagement: USD $100,000–$250,000. The listed product price of $100,000 is the starting professional services fee, not a deposit or a guaranteed fixed quote. Indicative delivery: 8-16 weeks after prerequisites are available. Final scope, milestones and commercial terms are agreed in a statement of work. Model\/API usage, cloud infrastructure, hardware, third-party licenses, taxes, major data remediation and ongoing managed support are excluded unless expressly included.\u003c\/p\u003e\u003ch3\u003eGetting started\u003c\/h3\u003e\u003cp\u003eProvide a named process owner, authorized access to representative data, integration documentation and operator time for review. Regulated or sensitive workflows require applicable privacy and security approvals before data access. The first working session confirms the use case, baseline, approval boundaries and the smallest valuable delivery scope.\u003c\/p\u003e","brand":"Corefinery","offers":[{"title":"Default Title","offer_id":68850837487810,"sku":"CF-AI-036","price":100000.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0798\/2101\/3186\/files\/Platinum_pathway_to_what_s_next-2.png?v=1794072948"},{"product_id":"etail-and-consumer-goods-member-growth-workbench","title":"Retail: Member growth workbench","description":"\u003cp\u003eGeneric outreach misses individual needs. Corefinery works with your retail and consumer goods team to turn this bottleneck into a practical, measurable workflow. The engagement focuses on draft personalized outreach grounded in purchase history rather than a standalone demonstration.\u003c\/p\u003e\u003ch3\u003eWhat the engagement covers\u003c\/h3\u003e\u003cp\u003eOne production workflow; up to two integrations; one business unit. We map the current process with the people doing the work, identify decision rights and exceptions, and agree which actions require approval. The workflow is designed around existing systems and an accountable business owner.\u003c\/p\u003e\u003ch3\u003eWorkflow and data\u003c\/h3\u003e\u003cp\u003eSource inputs: Client-authorized source-system records; approved operating policies; representative task examples; integration documentation. Draft personalized outreach grounded in purchase history. Every recommendation should retain its supporting context so an operator can inspect the evidence and correct incomplete or inaccurate output.\u003c\/p\u003e\u003ch3\u003eEngineering approach\u003c\/h3\u003e\u003cp\u003eDeterministic components: Consent; frequency caps; offer eligibility. These rules control calculations, eligibility and permitted actions. Non-deterministic AI handles interpretation, retrieval and draft recommendations; it does not replace the policy engine. Consequential actions require an authorized human decision.\u003c\/p\u003e\u003cp\u003eModel selection compares task quality, latency, privacy requirements and cost. Claude or OpenAI can support complex language tasks; a specialized small language model can handle narrower classification or retrieval when it meets the same evaluation criteria. Adaptation, quantization and private or edge deployment are considered only when supported by data rights, infrastructure and measured performance. Hosted vendor models remain subject to vendor deployment options and terms.\u003c\/p\u003e\u003ch3\u003eDeliverables\u003c\/h3\u003e\u003cul\u003e\n\u003cli\u003eCurrent-state workflow map, baseline metrics and prioritized requirements\u003c\/li\u003e\n\u003cli\u003eArchitecture, access boundaries, integration design and exception-handling plan\u003c\/li\u003e\n\u003cli\u003eRepresentative evaluation cases and an agreed acceptance scorecard\u003c\/li\u003e\n\u003cli\u003eWorking workflow software, scoped integrations and operator review queue\u003c\/li\u003e\n\u003cli\u003eMonitoring, audit events, deployment runbook, team training and source-code handover\u003c\/li\u003e\n\u003c\/ul\u003e\u003ch3\u003eDelivery and acceptance\u003c\/h3\u003e\u003cp\u003eForward deployed engineers work alongside your operators, with an AI engineer, integration engineer and shared quality and security support. The team reviews progress weekly, tests representative normal and exception cases, and documents limitations. Release follows owner acceptance, access-control testing and a demonstrated pause or rollback process.\u003c\/p\u003e\u003ch3\u003eHow value is measured\u003c\/h3\u003e\u003cp\u003ePrimary measures: Incremental repeat purchase; unsubscribe rate. Establish a baseline before changes, then compare a controlled pilot or agreed pre\/post period. Track quality, adoption and attributable benefits alongside labor time and ongoing model costs. Targets and acceptance thresholds are agreed during discovery; financial or operational results are not guaranteed.\u003c\/p\u003e\u003ch3\u003eInvestment and timing\u003c\/h3\u003e\u003cp\u003eEstimated engagement: USD $125,000–$300,000. The listed product price of $125,000 is the starting professional services fee, not a deposit or a guaranteed fixed quote. Indicative delivery: 8-16 weeks after prerequisites are available. Final scope, milestones and commercial terms are agreed in a statement of work. Model\/API usage, cloud infrastructure, hardware, third-party licenses, taxes, major data remediation and ongoing managed support are excluded unless expressly included.\u003c\/p\u003e\u003ch3\u003eGetting started\u003c\/h3\u003e\u003cp\u003eProvide a named process owner, authorized access to representative data, integration documentation and operator time for review. Regulated or sensitive workflows require applicable privacy and security approvals before data access. The first working session confirms the use case, baseline, approval boundaries and the smallest valuable delivery scope.\u003c\/p\u003e","brand":"Corefinery","offers":[{"title":"Default Title","offer_id":68850837520578,"sku":"CF-AI-037","price":125000.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0798\/2101\/3186\/files\/Platinum_pathway_to_what_s_next-2.png?v=1794072948"},{"product_id":"etail-and-consumer-goods-supplier-negotiation-intelligence","title":"Retail: Supplier negotiation intelligence","description":"\u003cp\u003eBuyers manually reconcile supplier terms. Corefinery works with your retail and consumer goods team to turn this bottleneck into a practical, measurable workflow. The engagement focuses on summarize supplier performance and draft negotiation options rather than a standalone demonstration.\u003c\/p\u003e\u003ch3\u003eWhat the engagement covers\u003c\/h3\u003e\u003cp\u003eOne production workflow; up to two integrations; one business unit. We map the current process with the people doing the work, identify decision rights and exceptions, and agree which actions require approval. The workflow is designed around existing systems and an accountable business owner.\u003c\/p\u003e\u003ch3\u003eWorkflow and data\u003c\/h3\u003e\u003cp\u003eSource inputs: Client-authorized source-system records; approved operating policies; representative task examples; integration documentation. Summarize supplier performance and draft negotiation options. Every recommendation should retain its supporting context so an operator can inspect the evidence and correct incomplete or inaccurate output.\u003c\/p\u003e\u003ch3\u003eEngineering approach\u003c\/h3\u003e\u003cp\u003eDeterministic components: Landed cost calculations; target margins; approval limits. These rules control calculations, eligibility and permitted actions. Non-deterministic AI handles interpretation, retrieval and draft recommendations; it does not replace the policy engine. Consequential actions require an authorized human decision.\u003c\/p\u003e\u003cp\u003eModel selection compares task quality, latency, privacy requirements and cost. Claude or OpenAI can support complex language tasks; a specialized small language model can handle narrower classification or retrieval when it meets the same evaluation criteria. Adaptation, quantization and private or edge deployment are considered only when supported by data rights, infrastructure and measured performance. Hosted vendor models remain subject to vendor deployment options and terms.\u003c\/p\u003e\u003ch3\u003eDeliverables\u003c\/h3\u003e\u003cul\u003e\n\u003cli\u003eCurrent-state workflow map, baseline metrics and prioritized requirements\u003c\/li\u003e\n\u003cli\u003eArchitecture, access boundaries, integration design and exception-handling plan\u003c\/li\u003e\n\u003cli\u003eRepresentative evaluation cases and an agreed acceptance scorecard\u003c\/li\u003e\n\u003cli\u003eWorking workflow software, scoped integrations and operator review queue\u003c\/li\u003e\n\u003cli\u003eMonitoring, audit events, deployment runbook, team training and source-code handover\u003c\/li\u003e\n\u003c\/ul\u003e\u003ch3\u003eDelivery and acceptance\u003c\/h3\u003e\u003cp\u003eForward deployed engineers work alongside your operators, with an AI engineer, integration engineer and shared quality and security support. The team reviews progress weekly, tests representative normal and exception cases, and documents limitations. Release follows owner acceptance, access-control testing and a demonstrated pause or rollback process.\u003c\/p\u003e\u003ch3\u003eHow value is measured\u003c\/h3\u003e\u003cp\u003ePrimary measures: Buyer hours; negotiated savings. Establish a baseline before changes, then compare a controlled pilot or agreed pre\/post period. Track quality, adoption and attributable benefits alongside labor time and ongoing model costs. Targets and acceptance thresholds are agreed during discovery; financial or operational results are not guaranteed.\u003c\/p\u003e\u003ch3\u003eInvestment and timing\u003c\/h3\u003e\u003cp\u003eEstimated engagement: USD $150,000–$350,000. The listed product price of $150,000 is the starting professional services fee, not a deposit or a guaranteed fixed quote. Indicative delivery: 8-16 weeks after prerequisites are available. Final scope, milestones and commercial terms are agreed in a statement of work. Model\/API usage, cloud infrastructure, hardware, third-party licenses, taxes, major data remediation and ongoing managed support are excluded unless expressly included.\u003c\/p\u003e\u003ch3\u003eGetting started\u003c\/h3\u003e\u003cp\u003eProvide a named process owner, authorized access to representative data, integration documentation and operator time for review. Regulated or sensitive workflows require applicable privacy and security approvals before data access. The first working session confirms the use case, baseline, approval boundaries and the smallest valuable delivery scope.\u003c\/p\u003e","brand":"Corefinery","offers":[{"title":"Default Title","offer_id":68850837553346,"sku":"CF-AI-038","price":150000.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0798\/2101\/3186\/files\/Platinum_pathway_to_what_s_next-2.png?v=1794072948"},{"product_id":"etail-and-consumer-goods-private-slm-commerce-operations","title":"Retail: Private SLM commerce operations","description":"\u003cp\u003eRoutine product and order tasks have high inference cost. Corefinery works with your retail and consumer goods team to turn this bottleneck into a practical, measurable workflow. The engagement focuses on classify customer intent and retrieve product guidance rather than a standalone demonstration.\u003c\/p\u003e\u003ch3\u003eWhat the engagement covers\u003c\/h3\u003e\u003cp\u003eOne production workflow; up to two integrations; one business unit. We map the current process with the people doing the work, identify decision rights and exceptions, and agree which actions require approval. The workflow is designed around existing systems and an accountable business owner.\u003c\/p\u003e\u003ch3\u003eWorkflow and data\u003c\/h3\u003e\u003cp\u003eSource inputs: Client-authorized source-system records; approved operating policies; representative task examples; integration documentation. Classify customer intent and retrieve product guidance. Every recommendation should retain its supporting context so an operator can inspect the evidence and correct incomplete or inaccurate output.\u003c\/p\u003e\u003ch3\u003eEngineering approach\u003c\/h3\u003e\u003cp\u003eDeterministic components: Order permissions; stock validation; approved action schema. These rules control calculations, eligibility and permitted actions. Non-deterministic AI handles interpretation, retrieval and draft recommendations; it does not replace the policy engine. Consequential actions require an authorized human decision.\u003c\/p\u003e\u003cp\u003eModel selection compares task quality, latency, privacy requirements and cost. Claude or OpenAI can support complex language tasks; a specialized small language model can handle narrower classification or retrieval when it meets the same evaluation criteria. Adaptation, quantization and private or edge deployment are considered only when supported by data rights, infrastructure and measured performance. Hosted vendor models remain subject to vendor deployment options and terms.\u003c\/p\u003e\u003ch3\u003eDeliverables\u003c\/h3\u003e\u003cul\u003e\n\u003cli\u003eCurrent-state workflow map, baseline metrics and prioritized requirements\u003c\/li\u003e\n\u003cli\u003eArchitecture, access boundaries, integration design and exception-handling plan\u003c\/li\u003e\n\u003cli\u003eRepresentative evaluation cases and an agreed acceptance scorecard\u003c\/li\u003e\n\u003cli\u003eWorking workflow software, scoped integrations and operator review queue\u003c\/li\u003e\n\u003cli\u003eMonitoring, audit events, deployment runbook, team training and source-code handover\u003c\/li\u003e\n\u003c\/ul\u003e\u003ch3\u003eDelivery and acceptance\u003c\/h3\u003e\u003cp\u003eForward deployed engineers work alongside your operators, with an AI engineer, integration engineer and shared quality and security support. The team reviews progress weekly, tests representative normal and exception cases, and documents limitations. Release follows owner acceptance, access-control testing and a demonstrated pause or rollback process.\u003c\/p\u003e\u003ch3\u003eHow value is measured\u003c\/h3\u003e\u003cp\u003ePrimary measures: Cost per task; task completion accuracy. Establish a baseline before changes, then compare a controlled pilot or agreed pre\/post period. Track quality, adoption and attributable benefits alongside labor time and ongoing model costs. Targets and acceptance thresholds are agreed during discovery; financial or operational results are not guaranteed.\u003c\/p\u003e\u003ch3\u003eInvestment and timing\u003c\/h3\u003e\u003cp\u003eEstimated engagement: USD $175,000–$400,000. The listed product price of $175,000 is the starting professional services fee, not a deposit or a guaranteed fixed quote. Indicative delivery: 8-16 weeks after prerequisites are available. Final scope, milestones and commercial terms are agreed in a statement of work. Model\/API usage, cloud infrastructure, hardware, third-party licenses, taxes, major data remediation and ongoing managed support are excluded unless expressly included.\u003c\/p\u003e\u003ch3\u003eGetting started\u003c\/h3\u003e\u003cp\u003eProvide a named process owner, authorized access to representative data, integration documentation and operator time for review. Regulated or sensitive workflows require applicable privacy and security approvals before data access. The first working session confirms the use case, baseline, approval boundaries and the smallest valuable delivery scope.\u003c\/p\u003e","brand":"Corefinery","offers":[{"title":"Default Title","offer_id":68850838372546,"sku":"CF-AI-039","price":175000.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0798\/2101\/3186\/files\/Platinum_pathway_to_what_s_next-2.png?v=1794072948"},{"product_id":"anufacturing-and-industrial-maintenance-intelligence-workbench","title":"Manufacturing: Maintenance intelligence workbench","description":"\u003cp\u003eUnplanned downtime disrupts production. Corefinery works with your manufacturing and industrial team to turn this bottleneck into a practical, measurable workflow. The engagement focuses on summarize sensor exceptions and draft technician checklists rather than a standalone demonstration.\u003c\/p\u003e\u003ch3\u003eWhat the engagement covers\u003c\/h3\u003e\u003cp\u003eOne production workflow; up to two integrations; one business unit. We map the current process with the people doing the work, identify decision rights and exceptions, and agree which actions require approval. The workflow is designed around existing systems and an accountable business owner.\u003c\/p\u003e\u003ch3\u003eWorkflow and data\u003c\/h3\u003e\u003cp\u003eSource inputs: Client-authorized source-system records; approved operating policies; representative task examples; integration documentation. Summarize sensor exceptions and draft technician checklists. Every recommendation should retain its supporting context so an operator can inspect the evidence and correct incomplete or inaccurate output.\u003c\/p\u003e\u003ch3\u003eEngineering approach\u003c\/h3\u003e\u003cp\u003eDeterministic components: Work order rules; safety interlocks; maintenance authorization. These rules control calculations, eligibility and permitted actions. Non-deterministic AI handles interpretation, retrieval and draft recommendations; it does not replace the policy engine. Consequential actions require an authorized human decision.\u003c\/p\u003e\u003cp\u003eModel selection compares task quality, latency, privacy requirements and cost. Claude or OpenAI can support complex language tasks; a specialized small language model can handle narrower classification or retrieval when it meets the same evaluation criteria. Adaptation, quantization and private or edge deployment are considered only when supported by data rights, infrastructure and measured performance. Hosted vendor models remain subject to vendor deployment options and terms.\u003c\/p\u003e\u003ch3\u003eDeliverables\u003c\/h3\u003e\u003cul\u003e\n\u003cli\u003eCurrent-state workflow map, baseline metrics and prioritized requirements\u003c\/li\u003e\n\u003cli\u003eArchitecture, access boundaries, integration design and exception-handling plan\u003c\/li\u003e\n\u003cli\u003eRepresentative evaluation cases and an agreed acceptance scorecard\u003c\/li\u003e\n\u003cli\u003eWorking workflow software, scoped integrations and operator review queue\u003c\/li\u003e\n\u003cli\u003eMonitoring, audit events, deployment runbook, team training and source-code handover\u003c\/li\u003e\n\u003c\/ul\u003e\u003ch3\u003eDelivery and acceptance\u003c\/h3\u003e\u003cp\u003eForward deployed engineers work alongside your operators, with an AI engineer, integration engineer and shared quality and security support. The team reviews progress weekly, tests representative normal and exception cases, and documents limitations. Release follows owner acceptance, access-control testing and a demonstrated pause or rollback process.\u003c\/p\u003e\u003ch3\u003eHow value is measured\u003c\/h3\u003e\u003cp\u003ePrimary measures: Downtime hours; diagnosis time. Establish a baseline before changes, then compare a controlled pilot or agreed pre\/post period. Track quality, adoption and attributable benefits alongside labor time and ongoing model costs. Targets and acceptance thresholds are agreed during discovery; financial or operational results are not guaranteed.\u003c\/p\u003e\u003ch3\u003eInvestment and timing\u003c\/h3\u003e\u003cp\u003eEstimated engagement: USD $40,000–$90,000. The listed product price of $40,000 is the starting professional services fee, not a deposit or a guaranteed fixed quote. Indicative delivery: 8-16 weeks after prerequisites are available. Final scope, milestones and commercial terms are agreed in a statement of work. Model\/API usage, cloud infrastructure, hardware, third-party licenses, taxes, major data remediation and ongoing managed support are excluded unless expressly included.\u003c\/p\u003e\u003ch3\u003eGetting started\u003c\/h3\u003e\u003cp\u003eProvide a named process owner, authorized access to representative data, integration documentation and operator time for review. Regulated or sensitive workflows require applicable privacy and security approvals before data access. The first working session confirms the use case, baseline, approval boundaries and the smallest valuable delivery scope.\u003c\/p\u003e","brand":"Corefinery","offers":[{"title":"Default Title","offer_id":68850838470850,"sku":"CF-AI-042","price":40000.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0798\/2101\/3186\/files\/Platinum_pathway_to_what_s_next-2.png?v=1794072948"},{"product_id":"anufacturing-and-industrial-quality-investigation-copilot","title":"Manufacturing: Quality investigation copilot","description":"\u003cp\u003eDefect investigations are slow and inconsistent. Corefinery works with your manufacturing and industrial team to turn this bottleneck into a practical, measurable workflow. The engagement focuses on cluster defect narratives and propose root-cause hypotheses rather than a standalone demonstration.\u003c\/p\u003e\u003ch3\u003eWhat the engagement covers\u003c\/h3\u003e\u003cp\u003eOne production workflow; up to two integrations; one business unit. We map the current process with the people doing the work, identify decision rights and exceptions, and agree which actions require approval. The workflow is designed around existing systems and an accountable business owner.\u003c\/p\u003e\u003ch3\u003eWorkflow and data\u003c\/h3\u003e\u003cp\u003eSource inputs: Client-authorized source-system records; approved operating policies; representative task examples; integration documentation. Cluster defect narratives and propose root-cause hypotheses. Every recommendation should retain its supporting context so an operator can inspect the evidence and correct incomplete or inaccurate output.\u003c\/p\u003e\u003ch3\u003eEngineering approach\u003c\/h3\u003e\u003cp\u003eDeterministic components: Specification checks; lot traceability; disposition approval. These rules control calculations, eligibility and permitted actions. Non-deterministic AI handles interpretation, retrieval and draft recommendations; it does not replace the policy engine. Consequential actions require an authorized human decision.\u003c\/p\u003e\u003cp\u003eModel selection compares task quality, latency, privacy requirements and cost. Claude or OpenAI can support complex language tasks; a specialized small language model can handle narrower classification or retrieval when it meets the same evaluation criteria. Adaptation, quantization and private or edge deployment are considered only when supported by data rights, infrastructure and measured performance. Hosted vendor models remain subject to vendor deployment options and terms.\u003c\/p\u003e\u003ch3\u003eDeliverables\u003c\/h3\u003e\u003cul\u003e\n\u003cli\u003eCurrent-state workflow map, baseline metrics and prioritized requirements\u003c\/li\u003e\n\u003cli\u003eArchitecture, access boundaries, integration design and exception-handling plan\u003c\/li\u003e\n\u003cli\u003eRepresentative evaluation cases and an agreed acceptance scorecard\u003c\/li\u003e\n\u003cli\u003eWorking workflow software, scoped integrations and operator review queue\u003c\/li\u003e\n\u003cli\u003eMonitoring, audit events, deployment runbook, team training and source-code handover\u003c\/li\u003e\n\u003c\/ul\u003e\u003ch3\u003eDelivery and acceptance\u003c\/h3\u003e\u003cp\u003eForward deployed engineers work alongside your operators, with an AI engineer, integration engineer and shared quality and security support. The team reviews progress weekly, tests representative normal and exception cases, and documents limitations. Release follows owner acceptance, access-control testing and a demonstrated pause or rollback process.\u003c\/p\u003e\u003ch3\u003eHow value is measured\u003c\/h3\u003e\u003cp\u003ePrimary measures: Investigation time; repeat defect rate. Establish a baseline before changes, then compare a controlled pilot or agreed pre\/post period. Track quality, adoption and attributable benefits alongside labor time and ongoing model costs. Targets and acceptance thresholds are agreed during discovery; financial or operational results are not guaranteed.\u003c\/p\u003e\u003ch3\u003eInvestment and timing\u003c\/h3\u003e\u003cp\u003eEstimated engagement: USD $60,000–$140,000. The listed product price of $60,000 is the starting professional services fee, not a deposit or a guaranteed fixed quote. Indicative delivery: 8-16 weeks after prerequisites are available. Final scope, milestones and commercial terms are agreed in a statement of work. Model\/API usage, cloud infrastructure, hardware, third-party licenses, taxes, major data remediation and ongoing managed support are excluded unless expressly included.\u003c\/p\u003e\u003ch3\u003eGetting started\u003c\/h3\u003e\u003cp\u003eProvide a named process owner, authorized access to representative data, integration documentation and operator time for review. Regulated or sensitive workflows require applicable privacy and security approvals before data access. The first working session confirms the use case, baseline, approval boundaries and the smallest valuable delivery scope.\u003c\/p\u003e","brand":"Corefinery","offers":[{"title":"Default Title","offer_id":68850838503618,"sku":"CF-AI-043","price":60000.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0798\/2101\/3186\/files\/Platinum_pathway_to_what_s_next-2.png?v=1794072948"},{"product_id":"anufacturing-and-industrial-production-scheduling-optimizer","title":"Manufacturing: Production scheduling optimizer","description":"\u003cp\u003ePlans fail to reflect changing constraints. Corefinery works with your manufacturing and industrial team to turn this bottleneck into a practical, measurable workflow. The engagement focuses on explain plan tradeoffs and summarize exceptions rather than a standalone demonstration.\u003c\/p\u003e\u003ch3\u003eWhat the engagement covers\u003c\/h3\u003e\u003cp\u003eOne production workflow; up to two integrations; one business unit. We map the current process with the people doing the work, identify decision rights and exceptions, and agree which actions require approval. The workflow is designed around existing systems and an accountable business owner.\u003c\/p\u003e\u003ch3\u003eWorkflow and data\u003c\/h3\u003e\u003cp\u003eSource inputs: Client-authorized source-system records; approved operating policies; representative task examples; integration documentation. Explain plan tradeoffs and summarize exceptions. Every recommendation should retain its supporting context so an operator can inspect the evidence and correct incomplete or inaccurate output.\u003c\/p\u003e\u003ch3\u003eEngineering approach\u003c\/h3\u003e\u003cp\u003eDeterministic components: Machine capacity; setup time; labor skills; material availability. These rules control calculations, eligibility and permitted actions. Non-deterministic AI handles interpretation, retrieval and draft recommendations; it does not replace the policy engine. Consequential actions require an authorized human decision.\u003c\/p\u003e\u003cp\u003eModel selection compares task quality, latency, privacy requirements and cost. Claude or OpenAI can support complex language tasks; a specialized small language model can handle narrower classification or retrieval when it meets the same evaluation criteria. Adaptation, quantization and private or edge deployment are considered only when supported by data rights, infrastructure and measured performance. Hosted vendor models remain subject to vendor deployment options and terms.\u003c\/p\u003e\u003ch3\u003eDeliverables\u003c\/h3\u003e\u003cul\u003e\n\u003cli\u003eCurrent-state workflow map, baseline metrics and prioritized requirements\u003c\/li\u003e\n\u003cli\u003eArchitecture, access boundaries, integration design and exception-handling plan\u003c\/li\u003e\n\u003cli\u003eRepresentative evaluation cases and an agreed acceptance scorecard\u003c\/li\u003e\n\u003cli\u003eWorking workflow software, scoped integrations and operator review queue\u003c\/li\u003e\n\u003cli\u003eMonitoring, audit events, deployment runbook, team training and source-code handover\u003c\/li\u003e\n\u003c\/ul\u003e\u003ch3\u003eDelivery and acceptance\u003c\/h3\u003e\u003cp\u003eForward deployed engineers work alongside your operators, with an AI engineer, integration engineer and shared quality and security support. The team reviews progress weekly, tests representative normal and exception cases, and documents limitations. Release follows owner acceptance, access-control testing and a demonstrated pause or rollback process.\u003c\/p\u003e\u003ch3\u003eHow value is measured\u003c\/h3\u003e\u003cp\u003ePrimary measures: Schedule adherence; throughput. Establish a baseline before changes, then compare a controlled pilot or agreed pre\/post period. Track quality, adoption and attributable benefits alongside labor time and ongoing model costs. Targets and acceptance thresholds are agreed during discovery; financial or operational results are not guaranteed.\u003c\/p\u003e\u003ch3\u003eInvestment and timing\u003c\/h3\u003e\u003cp\u003eEstimated engagement: USD $75,000–$180,000. The listed product price of $75,000 is the starting professional services fee, not a deposit or a guaranteed fixed quote. Indicative delivery: 8-16 weeks after prerequisites are available. Final scope, milestones and commercial terms are agreed in a statement of work. Model\/API usage, cloud infrastructure, hardware, third-party licenses, taxes, major data remediation and ongoing managed support are excluded unless expressly included.\u003c\/p\u003e\u003ch3\u003eGetting started\u003c\/h3\u003e\u003cp\u003eProvide a named process owner, authorized access to representative data, integration documentation and operator time for review. Regulated or sensitive workflows require applicable privacy and security approvals before data access. The first working session confirms the use case, baseline, approval boundaries and the smallest valuable delivery scope.\u003c\/p\u003e","brand":"Corefinery","offers":[{"title":"Default Title","offer_id":68850838536386,"sku":"CF-AI-044","price":75000.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0798\/2101\/3186\/files\/Platinum_pathway_to_what_s_next-2.png?v=1794072948"},{"product_id":"anufacturing-and-industrial-engineering-change-impact-assistant","title":"Manufacturing: Engineering change impact assistant","description":"\u003cp\u003eDesign changes hide downstream effects. Corefinery works with your manufacturing and industrial team to turn this bottleneck into a practical, measurable workflow. The engagement focuses on trace document dependencies and draft impact summaries rather than a standalone demonstration.\u003c\/p\u003e\u003ch3\u003eWhat the engagement covers\u003c\/h3\u003e\u003cp\u003eOne production workflow; up to two integrations; one business unit. We map the current process with the people doing the work, identify decision rights and exceptions, and agree which actions require approval. The workflow is designed around existing systems and an accountable business owner.\u003c\/p\u003e\u003ch3\u003eWorkflow and data\u003c\/h3\u003e\u003cp\u003eSource inputs: Client-authorized source-system records; approved operating policies; representative task examples; integration documentation. Trace document dependencies and draft impact summaries. Every recommendation should retain its supporting context so an operator can inspect the evidence and correct incomplete or inaccurate output.\u003c\/p\u003e\u003ch3\u003eEngineering approach\u003c\/h3\u003e\u003cp\u003eDeterministic components: BOM version checks; change authority; release gates. These rules control calculations, eligibility and permitted actions. Non-deterministic AI handles interpretation, retrieval and draft recommendations; it does not replace the policy engine. Consequential actions require an authorized human decision.\u003c\/p\u003e\u003cp\u003eModel selection compares task quality, latency, privacy requirements and cost. Claude or OpenAI can support complex language tasks; a specialized small language model can handle narrower classification or retrieval when it meets the same evaluation criteria. Adaptation, quantization and private or edge deployment are considered only when supported by data rights, infrastructure and measured performance. Hosted vendor models remain subject to vendor deployment options and terms.\u003c\/p\u003e\u003ch3\u003eDeliverables\u003c\/h3\u003e\u003cul\u003e\n\u003cli\u003eCurrent-state workflow map, baseline metrics and prioritized requirements\u003c\/li\u003e\n\u003cli\u003eArchitecture, access boundaries, integration design and exception-handling plan\u003c\/li\u003e\n\u003cli\u003eRepresentative evaluation cases and an agreed acceptance scorecard\u003c\/li\u003e\n\u003cli\u003eWorking workflow software, scoped integrations and operator review queue\u003c\/li\u003e\n\u003cli\u003eMonitoring, audit events, deployment runbook, team training and source-code handover\u003c\/li\u003e\n\u003c\/ul\u003e\u003ch3\u003eDelivery and acceptance\u003c\/h3\u003e\u003cp\u003eForward deployed engineers work alongside your operators, with an AI engineer, integration engineer and shared quality and security support. The team reviews progress weekly, tests representative normal and exception cases, and documents limitations. Release follows owner acceptance, access-control testing and a demonstrated pause or rollback process.\u003c\/p\u003e\u003ch3\u003eHow value is measured\u003c\/h3\u003e\u003cp\u003ePrimary measures: Change cycle time; missed dependencies. Establish a baseline before changes, then compare a controlled pilot or agreed pre\/post period. Track quality, adoption and attributable benefits alongside labor time and ongoing model costs. Targets and acceptance thresholds are agreed during discovery; financial or operational results are not guaranteed.\u003c\/p\u003e\u003ch3\u003eInvestment and timing\u003c\/h3\u003e\u003cp\u003eEstimated engagement: USD $90,000–$225,000. The listed product price of $90,000 is the starting professional services fee, not a deposit or a guaranteed fixed quote. Indicative delivery: 8-16 weeks after prerequisites are available. Final scope, milestones and commercial terms are agreed in a statement of work. Model\/API usage, cloud infrastructure, hardware, third-party licenses, taxes, major data remediation and ongoing managed support are excluded unless expressly included.\u003c\/p\u003e\u003ch3\u003eGetting started\u003c\/h3\u003e\u003cp\u003eProvide a named process owner, authorized access to representative data, integration documentation and operator time for review. Regulated or sensitive workflows require applicable privacy and security approvals before data access. The first working session confirms the use case, baseline, approval boundaries and the smallest valuable delivery scope.\u003c\/p\u003e","brand":"Corefinery","offers":[{"title":"Default Title","offer_id":68850839912642,"sku":"CF-AI-045","price":90000.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0798\/2101\/3186\/files\/Platinum_pathway_to_what_s_next-2.png?v=1794072948"},{"product_id":"anufacturing-and-industrial-supplier-quality-recovery","title":"Manufacturing: Supplier quality recovery","description":"\u003cp\u003eSupplier issues create recurring disruption. Corefinery works with your manufacturing and industrial team to turn this bottleneck into a practical, measurable workflow. The engagement focuses on summarize nonconformances and draft corrective-action requests rather than a standalone demonstration.\u003c\/p\u003e\u003ch3\u003eWhat the engagement covers\u003c\/h3\u003e\u003cp\u003eOne production workflow; up to two integrations; one business unit. We map the current process with the people doing the work, identify decision rights and exceptions, and agree which actions require approval. The workflow is designed around existing systems and an accountable business owner.\u003c\/p\u003e\u003ch3\u003eWorkflow and data\u003c\/h3\u003e\u003cp\u003eSource inputs: Client-authorized source-system records; approved operating policies; representative task examples; integration documentation. Summarize nonconformances and draft corrective-action requests. Every recommendation should retain its supporting context so an operator can inspect the evidence and correct incomplete or inaccurate output.\u003c\/p\u003e\u003ch3\u003eEngineering approach\u003c\/h3\u003e\u003cp\u003eDeterministic components: Inspection thresholds; corrective-action deadlines; approval gates. These rules control calculations, eligibility and permitted actions. Non-deterministic AI handles interpretation, retrieval and draft recommendations; it does not replace the policy engine. Consequential actions require an authorized human decision.\u003c\/p\u003e\u003cp\u003eModel selection compares task quality, latency, privacy requirements and cost. Claude or OpenAI can support complex language tasks; a specialized small language model can handle narrower classification or retrieval when it meets the same evaluation criteria. Adaptation, quantization and private or edge deployment are considered only when supported by data rights, infrastructure and measured performance. Hosted vendor models remain subject to vendor deployment options and terms.\u003c\/p\u003e\u003ch3\u003eDeliverables\u003c\/h3\u003e\u003cul\u003e\n\u003cli\u003eCurrent-state workflow map, baseline metrics and prioritized requirements\u003c\/li\u003e\n\u003cli\u003eArchitecture, access boundaries, integration design and exception-handling plan\u003c\/li\u003e\n\u003cli\u003eRepresentative evaluation cases and an agreed acceptance scorecard\u003c\/li\u003e\n\u003cli\u003eWorking workflow software, scoped integrations and operator review queue\u003c\/li\u003e\n\u003cli\u003eMonitoring, audit events, deployment runbook, team training and source-code handover\u003c\/li\u003e\n\u003c\/ul\u003e\u003ch3\u003eDelivery and acceptance\u003c\/h3\u003e\u003cp\u003eForward deployed engineers work alongside your operators, with an AI engineer, integration engineer and shared quality and security support. The team reviews progress weekly, tests representative normal and exception cases, and documents limitations. Release follows owner acceptance, access-control testing and a demonstrated pause or rollback process.\u003c\/p\u003e\u003ch3\u003eHow value is measured\u003c\/h3\u003e\u003cp\u003ePrimary measures: Closure time; recurring defects. Establish a baseline before changes, then compare a controlled pilot or agreed pre\/post period. Track quality, adoption and attributable benefits alongside labor time and ongoing model costs. Targets and acceptance thresholds are agreed during discovery; financial or operational results are not guaranteed.\u003c\/p\u003e\u003ch3\u003eInvestment and timing\u003c\/h3\u003e\u003cp\u003eEstimated engagement: USD $100,000–$250,000. The listed product price of $100,000 is the starting professional services fee, not a deposit or a guaranteed fixed quote. Indicative delivery: 8-16 weeks after prerequisites are available. Final scope, milestones and commercial terms are agreed in a statement of work. Model\/API usage, cloud infrastructure, hardware, third-party licenses, taxes, major data remediation and ongoing managed support are excluded unless expressly included.\u003c\/p\u003e\u003ch3\u003eGetting started\u003c\/h3\u003e\u003cp\u003eProvide a named process owner, authorized access to representative data, integration documentation and operator time for review. Regulated or sensitive workflows require applicable privacy and security approvals before data access. The first working session confirms the use case, baseline, approval boundaries and the smallest valuable delivery scope.\u003c\/p\u003e","brand":"Corefinery","offers":[{"title":"Default Title","offer_id":68850839945410,"sku":"CF-AI-046","price":100000.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0798\/2101\/3186\/files\/Platinum_pathway_to_what_s_next-2.png?v=1794072948"},{"product_id":"anufacturing-and-industrial-technician-knowledge-copilot","title":"Manufacturing: Technician knowledge copilot","description":"\u003cp\u003eExpert knowledge is difficult to access. Corefinery works with your manufacturing and industrial team to turn this bottleneck into a practical, measurable workflow. The engagement focuses on retrieve cited manuals and draft troubleshooting steps rather than a standalone demonstration.\u003c\/p\u003e\u003ch3\u003eWhat the engagement covers\u003c\/h3\u003e\u003cp\u003eOne production workflow; up to two integrations; one business unit. We map the current process with the people doing the work, identify decision rights and exceptions, and agree which actions require approval. The workflow is designed around existing systems and an accountable business owner.\u003c\/p\u003e\u003ch3\u003eWorkflow and data\u003c\/h3\u003e\u003cp\u003eSource inputs: Client-authorized source-system records; approved operating policies; representative task examples; integration documentation. Retrieve cited manuals and draft troubleshooting steps. Every recommendation should retain its supporting context so an operator can inspect the evidence and correct incomplete or inaccurate output.\u003c\/p\u003e\u003ch3\u003eEngineering approach\u003c\/h3\u003e\u003cp\u003eDeterministic components: Equipment permissions; procedure versions; safety escalation. These rules control calculations, eligibility and permitted actions. Non-deterministic AI handles interpretation, retrieval and draft recommendations; it does not replace the policy engine. Consequential actions require an authorized human decision.\u003c\/p\u003e\u003cp\u003eModel selection compares task quality, latency, privacy requirements and cost. Claude or OpenAI can support complex language tasks; a specialized small language model can handle narrower classification or retrieval when it meets the same evaluation criteria. Adaptation, quantization and private or edge deployment are considered only when supported by data rights, infrastructure and measured performance. Hosted vendor models remain subject to vendor deployment options and terms.\u003c\/p\u003e\u003ch3\u003eDeliverables\u003c\/h3\u003e\u003cul\u003e\n\u003cli\u003eCurrent-state workflow map, baseline metrics and prioritized requirements\u003c\/li\u003e\n\u003cli\u003eArchitecture, access boundaries, integration design and exception-handling plan\u003c\/li\u003e\n\u003cli\u003eRepresentative evaluation cases and an agreed acceptance scorecard\u003c\/li\u003e\n\u003cli\u003eWorking workflow software, scoped integrations and operator review queue\u003c\/li\u003e\n\u003cli\u003eMonitoring, audit events, deployment runbook, team training and source-code handover\u003c\/li\u003e\n\u003c\/ul\u003e\u003ch3\u003eDelivery and acceptance\u003c\/h3\u003e\u003cp\u003eForward deployed engineers work alongside your operators, with an AI engineer, integration engineer and shared quality and security support. The team reviews progress weekly, tests representative normal and exception cases, and documents limitations. Release follows owner acceptance, access-control testing and a demonstrated pause or rollback process.\u003c\/p\u003e\u003ch3\u003eHow value is measured\u003c\/h3\u003e\u003cp\u003ePrimary measures: Search time; first-time fix rate. Establish a baseline before changes, then compare a controlled pilot or agreed pre\/post period. Track quality, adoption and attributable benefits alongside labor time and ongoing model costs. Targets and acceptance thresholds are agreed during discovery; financial or operational results are not guaranteed.\u003c\/p\u003e\u003ch3\u003eInvestment and timing\u003c\/h3\u003e\u003cp\u003eEstimated engagement: USD $125,000–$300,000. The listed product price of $125,000 is the starting professional services fee, not a deposit or a guaranteed fixed quote. Indicative delivery: 8-16 weeks after prerequisites are available. Final scope, milestones and commercial terms are agreed in a statement of work. Model\/API usage, cloud infrastructure, hardware, third-party licenses, taxes, major data remediation and ongoing managed support are excluded unless expressly included.\u003c\/p\u003e\u003ch3\u003eGetting started\u003c\/h3\u003e\u003cp\u003eProvide a named process owner, authorized access to representative data, integration documentation and operator time for review. Regulated or sensitive workflows require applicable privacy and security approvals before data access. The first working session confirms the use case, baseline, approval boundaries and the smallest valuable delivery scope.\u003c\/p\u003e","brand":"Corefinery","offers":[{"title":"Default Title","offer_id":68850839978178,"sku":"CF-AI-047","price":125000.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0798\/2101\/3186\/files\/Platinum_pathway_to_what_s_next-2.png?v=1794072948"},{"product_id":"anufacturing-and-industrial-industrial-energy-optimization","title":"Manufacturing: Industrial energy optimization","description":"\u003cp\u003eEnergy waste is difficult to isolate. Corefinery works with your manufacturing and industrial team to turn this bottleneck into a practical, measurable workflow. The engagement focuses on explain anomalous consumption and suggest operating scenarios rather than a standalone demonstration.\u003c\/p\u003e\u003ch3\u003eWhat the engagement covers\u003c\/h3\u003e\u003cp\u003eOne production workflow; up to two integrations; one business unit. We map the current process with the people doing the work, identify decision rights and exceptions, and agree which actions require approval. The workflow is designed around existing systems and an accountable business owner.\u003c\/p\u003e\u003ch3\u003eWorkflow and data\u003c\/h3\u003e\u003cp\u003eSource inputs: Client-authorized source-system records; approved operating policies; representative task examples; integration documentation. Explain anomalous consumption and suggest operating scenarios. Every recommendation should retain its supporting context so an operator can inspect the evidence and correct incomplete or inaccurate output.\u003c\/p\u003e\u003ch3\u003eEngineering approach\u003c\/h3\u003e\u003cp\u003eDeterministic components: Equipment operating limits; tariff calculations; operator approval. These rules control calculations, eligibility and permitted actions. Non-deterministic AI handles interpretation, retrieval and draft recommendations; it does not replace the policy engine. Consequential actions require an authorized human decision.\u003c\/p\u003e\u003cp\u003eModel selection compares task quality, latency, privacy requirements and cost. Claude or OpenAI can support complex language tasks; a specialized small language model can handle narrower classification or retrieval when it meets the same evaluation criteria. Adaptation, quantization and private or edge deployment are considered only when supported by data rights, infrastructure and measured performance. Hosted vendor models remain subject to vendor deployment options and terms.\u003c\/p\u003e\u003ch3\u003eDeliverables\u003c\/h3\u003e\u003cul\u003e\n\u003cli\u003eCurrent-state workflow map, baseline metrics and prioritized requirements\u003c\/li\u003e\n\u003cli\u003eArchitecture, access boundaries, integration design and exception-handling plan\u003c\/li\u003e\n\u003cli\u003eRepresentative evaluation cases and an agreed acceptance scorecard\u003c\/li\u003e\n\u003cli\u003eWorking workflow software, scoped integrations and operator review queue\u003c\/li\u003e\n\u003cli\u003eMonitoring, audit events, deployment runbook, team training and source-code handover\u003c\/li\u003e\n\u003c\/ul\u003e\u003ch3\u003eDelivery and acceptance\u003c\/h3\u003e\u003cp\u003eForward deployed engineers work alongside your operators, with an AI engineer, integration engineer and shared quality and security support. The team reviews progress weekly, tests representative normal and exception cases, and documents limitations. Release follows owner acceptance, access-control testing and a demonstrated pause or rollback process.\u003c\/p\u003e\u003ch3\u003eHow value is measured\u003c\/h3\u003e\u003cp\u003ePrimary measures: Energy per unit; validated savings. Establish a baseline before changes, then compare a controlled pilot or agreed pre\/post period. Track quality, adoption and attributable benefits alongside labor time and ongoing model costs. Targets and acceptance thresholds are agreed during discovery; financial or operational results are not guaranteed.\u003c\/p\u003e\u003ch3\u003eInvestment and timing\u003c\/h3\u003e\u003cp\u003eEstimated engagement: USD $150,000–$350,000. The listed product price of $150,000 is the starting professional services fee, not a deposit or a guaranteed fixed quote. Indicative delivery: 8-16 weeks after prerequisites are available. Final scope, milestones and commercial terms are agreed in a statement of work. Model\/API usage, cloud infrastructure, hardware, third-party licenses, taxes, major data remediation and ongoing managed support are excluded unless expressly included.\u003c\/p\u003e\u003ch3\u003eGetting started\u003c\/h3\u003e\u003cp\u003eProvide a named process owner, authorized access to representative data, integration documentation and operator time for review. Regulated or sensitive workflows require applicable privacy and security approvals before data access. The first working session confirms the use case, baseline, approval boundaries and the smallest valuable delivery scope.\u003c\/p\u003e","brand":"Corefinery","offers":[{"title":"Default Title","offer_id":68850840043714,"sku":"CF-AI-048","price":150000.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0798\/2101\/3186\/files\/Platinum_pathway_to_what_s_next-2.png?v=1794072948"},{"product_id":"anufacturing-and-industrial-edge-slm-factory-assistant","title":"Manufacturing: Edge SLM factory assistant","description":"\u003cp\u003eConnectivity and latency limit shop-floor assistance. Corefinery works with your manufacturing and industrial team to turn this bottleneck into a practical, measurable workflow. The engagement focuses on run compact-model classification and manual retrieval at the edge rather than a standalone demonstration.\u003c\/p\u003e\u003ch3\u003eWhat the engagement covers\u003c\/h3\u003e\u003cp\u003eOne production workflow; up to two integrations; one business unit. We map the current process with the people doing the work, identify decision rights and exceptions, and agree which actions require approval. The workflow is designed around existing systems and an accountable business owner.\u003c\/p\u003e\u003ch3\u003eWorkflow and data\u003c\/h3\u003e\u003cp\u003eSource inputs: Client-authorized source-system records; approved operating policies; representative task examples; integration documentation. Run compact-model classification and manual retrieval at the edge. Every recommendation should retain its supporting context so an operator can inspect the evidence and correct incomplete or inaccurate output.\u003c\/p\u003e\u003ch3\u003eEngineering approach\u003c\/h3\u003e\u003cp\u003eDeterministic components: Local access control; device resource limits; command allowlist. These rules control calculations, eligibility and permitted actions. Non-deterministic AI handles interpretation, retrieval and draft recommendations; it does not replace the policy engine. Consequential actions require an authorized human decision.\u003c\/p\u003e\u003cp\u003eModel selection compares task quality, latency, privacy requirements and cost. Claude or OpenAI can support complex language tasks; a specialized small language model can handle narrower classification or retrieval when it meets the same evaluation criteria. Adaptation, quantization and private or edge deployment are considered only when supported by data rights, infrastructure and measured performance. Hosted vendor models remain subject to vendor deployment options and terms.\u003c\/p\u003e\u003ch3\u003eDeliverables\u003c\/h3\u003e\u003cul\u003e\n\u003cli\u003eCurrent-state workflow map, baseline metrics and prioritized requirements\u003c\/li\u003e\n\u003cli\u003eArchitecture, access boundaries, integration design and exception-handling plan\u003c\/li\u003e\n\u003cli\u003eRepresentative evaluation cases and an agreed acceptance scorecard\u003c\/li\u003e\n\u003cli\u003eWorking workflow software, scoped integrations and operator review queue\u003c\/li\u003e\n\u003cli\u003eMonitoring, audit events, deployment runbook, team training and source-code handover\u003c\/li\u003e\n\u003c\/ul\u003e\u003ch3\u003eDelivery and acceptance\u003c\/h3\u003e\u003cp\u003eForward deployed engineers work alongside your operators, with an AI engineer, integration engineer and shared quality and security support. The team reviews progress weekly, tests representative normal and exception cases, and documents limitations. Release follows owner acceptance, access-control testing and a demonstrated pause or rollback process.\u003c\/p\u003e\u003ch3\u003eHow value is measured\u003c\/h3\u003e\u003cp\u003ePrimary measures: Latency; offline task accuracy. Establish a baseline before changes, then compare a controlled pilot or agreed pre\/post period. Track quality, adoption and attributable benefits alongside labor time and ongoing model costs. Targets and acceptance thresholds are agreed during discovery; financial or operational results are not guaranteed.\u003c\/p\u003e\u003ch3\u003eInvestment and timing\u003c\/h3\u003e\u003cp\u003eEstimated engagement: USD $175,000–$400,000. The listed product price of $175,000 is the starting professional services fee, not a deposit or a guaranteed fixed quote. Indicative delivery: 8-16 weeks after prerequisites are available. Final scope, milestones and commercial terms are agreed in a statement of work. Model\/API usage, cloud infrastructure, hardware, third-party licenses, taxes, major data remediation and ongoing managed support are excluded unless expressly included.\u003c\/p\u003e\u003ch3\u003eGetting started\u003c\/h3\u003e\u003cp\u003eProvide a named process owner, authorized access to representative data, integration documentation and operator time for review. Regulated or sensitive workflows require applicable privacy and security approvals before data access. The first working session confirms the use case, baseline, approval boundaries and the smallest valuable delivery scope.\u003c\/p\u003e","brand":"Corefinery","offers":[{"title":"Default Title","offer_id":68850840076482,"sku":"CF-AI-049","price":175000.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0798\/2101\/3186\/files\/Platinum_pathway_to_what_s_next-2.png?v=1794072948"},{"product_id":"nergy-and-utilities-asset-maintenance-prioritizer","title":"Energy: Asset maintenance prioritizer","description":"\u003cp\u003eAsset risks compete for limited crews. Corefinery works with your energy and utilities team to turn this bottleneck into a practical, measurable workflow. The engagement focuses on summarize condition evidence and rank review candidates rather than a standalone demonstration.\u003c\/p\u003e\u003ch3\u003eWhat the engagement covers\u003c\/h3\u003e\u003cp\u003eOne production workflow; up to two integrations; one business unit. We map the current process with the people doing the work, identify decision rights and exceptions, and agree which actions require approval. The workflow is designed around existing systems and an accountable business owner.\u003c\/p\u003e\u003ch3\u003eWorkflow and data\u003c\/h3\u003e\u003cp\u003eSource inputs: Client-authorized source-system records; approved operating policies; representative task examples; integration documentation. Summarize condition evidence and rank review candidates. Every recommendation should retain its supporting context so an operator can inspect the evidence and correct incomplete or inaccurate output.\u003c\/p\u003e\u003ch3\u003eEngineering approach\u003c\/h3\u003e\u003cp\u003eDeterministic components: Asset criticality; inspection intervals; crew qualifications. These rules control calculations, eligibility and permitted actions. Non-deterministic AI handles interpretation, retrieval and draft recommendations; it does not replace the policy engine. Consequential actions require an authorized human decision.\u003c\/p\u003e\u003cp\u003eModel selection compares task quality, latency, privacy requirements and cost. Claude or OpenAI can support complex language tasks; a specialized small language model can handle narrower classification or retrieval when it meets the same evaluation criteria. Adaptation, quantization and private or edge deployment are considered only when supported by data rights, infrastructure and measured performance. Hosted vendor models remain subject to vendor deployment options and terms.\u003c\/p\u003e\u003ch3\u003eDeliverables\u003c\/h3\u003e\u003cul\u003e\n\u003cli\u003eCurrent-state workflow map, baseline metrics and prioritized requirements\u003c\/li\u003e\n\u003cli\u003eArchitecture, access boundaries, integration design and exception-handling plan\u003c\/li\u003e\n\u003cli\u003eRepresentative evaluation cases and an agreed acceptance scorecard\u003c\/li\u003e\n\u003cli\u003eWorking workflow software, scoped integrations and operator review queue\u003c\/li\u003e\n\u003cli\u003eMonitoring, audit events, deployment runbook, team training and source-code handover\u003c\/li\u003e\n\u003c\/ul\u003e\u003ch3\u003eDelivery and acceptance\u003c\/h3\u003e\u003cp\u003eForward deployed engineers work alongside your operators, with an AI engineer, integration engineer and shared quality and security support. The team reviews progress weekly, tests representative normal and exception cases, and documents limitations. Release follows owner acceptance, access-control testing and a demonstrated pause or rollback process.\u003c\/p\u003e\u003ch3\u003eHow value is measured\u003c\/h3\u003e\u003cp\u003ePrimary measures: Inspection backlog; maintenance planning hours. Establish a baseline before changes, then compare a controlled pilot or agreed pre\/post period. Track quality, adoption and attributable benefits alongside labor time and ongoing model costs. Targets and acceptance thresholds are agreed during discovery; financial or operational results are not guaranteed.\u003c\/p\u003e\u003ch3\u003eInvestment and timing\u003c\/h3\u003e\u003cp\u003eEstimated engagement: USD $40,000–$90,000. The listed product price of $40,000 is the starting professional services fee, not a deposit or a guaranteed fixed quote. Indicative delivery: 8-16 weeks after prerequisites are available. Final scope, milestones and commercial terms are agreed in a statement of work. Model\/API usage, cloud infrastructure, hardware, third-party licenses, taxes, major data remediation and ongoing managed support are excluded unless expressly included.\u003c\/p\u003e\u003ch3\u003eGetting started\u003c\/h3\u003e\u003cp\u003eProvide a named process owner, authorized access to representative data, integration documentation and operator time for review. Regulated or sensitive workflows require applicable privacy and security approvals before data access. The first working session confirms the use case, baseline, approval boundaries and the smallest valuable delivery scope.\u003c\/p\u003e","brand":"Corefinery","offers":[{"title":"Default Title","offer_id":68850842108098,"sku":"CF-AI-052","price":40000.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0798\/2101\/3186\/files\/Platinum_pathway_to_what_s_next-2.png?v=1794072948"},{"product_id":"nergy-and-utilities-outage-coordination-copilot","title":"Energy: Outage coordination copilot","description":"\u003cp\u003eOutage response information is fragmented. Corefinery works with your energy and utilities team to turn this bottleneck into a practical, measurable workflow. The engagement focuses on summarize incident updates and draft coordination briefs rather than a standalone demonstration.\u003c\/p\u003e\u003ch3\u003eWhat the engagement covers\u003c\/h3\u003e\u003cp\u003eOne production workflow; up to two integrations; one business unit. We map the current process with the people doing the work, identify decision rights and exceptions, and agree which actions require approval. The workflow is designed around existing systems and an accountable business owner.\u003c\/p\u003e\u003ch3\u003eWorkflow and data\u003c\/h3\u003e\u003cp\u003eSource inputs: Client-authorized source-system records; approved operating policies; representative task examples; integration documentation. Summarize incident updates and draft coordination briefs. Every recommendation should retain its supporting context so an operator can inspect the evidence and correct incomplete or inaccurate output.\u003c\/p\u003e\u003ch3\u003eEngineering approach\u003c\/h3\u003e\u003cp\u003eDeterministic components: Dispatch authorization; safety procedures; restoration prerequisites. These rules control calculations, eligibility and permitted actions. Non-deterministic AI handles interpretation, retrieval and draft recommendations; it does not replace the policy engine. Consequential actions require an authorized human decision.\u003c\/p\u003e\u003cp\u003eModel selection compares task quality, latency, privacy requirements and cost. Claude or OpenAI can support complex language tasks; a specialized small language model can handle narrower classification or retrieval when it meets the same evaluation criteria. Adaptation, quantization and private or edge deployment are considered only when supported by data rights, infrastructure and measured performance. Hosted vendor models remain subject to vendor deployment options and terms.\u003c\/p\u003e\u003ch3\u003eDeliverables\u003c\/h3\u003e\u003cul\u003e\n\u003cli\u003eCurrent-state workflow map, baseline metrics and prioritized requirements\u003c\/li\u003e\n\u003cli\u003eArchitecture, access boundaries, integration design and exception-handling plan\u003c\/li\u003e\n\u003cli\u003eRepresentative evaluation cases and an agreed acceptance scorecard\u003c\/li\u003e\n\u003cli\u003eWorking workflow software, scoped integrations and operator review queue\u003c\/li\u003e\n\u003cli\u003eMonitoring, audit events, deployment runbook, team training and source-code handover\u003c\/li\u003e\n\u003c\/ul\u003e\u003ch3\u003eDelivery and acceptance\u003c\/h3\u003e\u003cp\u003eForward deployed engineers work alongside your operators, with an AI engineer, integration engineer and shared quality and security support. The team reviews progress weekly, tests representative normal and exception cases, and documents limitations. Release follows owner acceptance, access-control testing and a demonstrated pause or rollback process.\u003c\/p\u003e\u003ch3\u003eHow value is measured\u003c\/h3\u003e\u003cp\u003ePrimary measures: Brief preparation time; handoff omissions. Establish a baseline before changes, then compare a controlled pilot or agreed pre\/post period. Track quality, adoption and attributable benefits alongside labor time and ongoing model costs. Targets and acceptance thresholds are agreed during discovery; financial or operational results are not guaranteed.\u003c\/p\u003e\u003ch3\u003eInvestment and timing\u003c\/h3\u003e\u003cp\u003eEstimated engagement: USD $60,000–$140,000. The listed product price of $60,000 is the starting professional services fee, not a deposit or a guaranteed fixed quote. Indicative delivery: 8-16 weeks after prerequisites are available. Final scope, milestones and commercial terms are agreed in a statement of work. Model\/API usage, cloud infrastructure, hardware, third-party licenses, taxes, major data remediation and ongoing managed support are excluded unless expressly included.\u003c\/p\u003e\u003ch3\u003eGetting started\u003c\/h3\u003e\u003cp\u003eProvide a named process owner, authorized access to representative data, integration documentation and operator time for review. Regulated or sensitive workflows require applicable privacy and security approvals before data access. The first working session confirms the use case, baseline, approval boundaries and the smallest valuable delivery scope.\u003c\/p\u003e","brand":"Corefinery","offers":[{"title":"Default Title","offer_id":68850842304706,"sku":"CF-AI-053","price":60000.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0798\/2101\/3186\/files\/Platinum_pathway_to_what_s_next-2.png?v=1794072948"},{"product_id":"nergy-and-utilities-field-service-planning-engine","title":"Energy: Field service planning engine","description":"\u003cp\u003eTravel and skill mismatches reduce productivity. Corefinery works with your energy and utilities team to turn this bottleneck into a practical, measurable workflow. The engagement focuses on explain scheduling exceptions and propose dispatch options rather than a standalone demonstration.\u003c\/p\u003e\u003ch3\u003eWhat the engagement covers\u003c\/h3\u003e\u003cp\u003eOne production workflow; up to two integrations; one business unit. We map the current process with the people doing the work, identify decision rights and exceptions, and agree which actions require approval. The workflow is designed around existing systems and an accountable business owner.\u003c\/p\u003e\u003ch3\u003eWorkflow and data\u003c\/h3\u003e\u003cp\u003eSource inputs: Client-authorized source-system records; approved operating policies; representative task examples; integration documentation. Explain scheduling exceptions and propose dispatch options. Every recommendation should retain its supporting context so an operator can inspect the evidence and correct incomplete or inaccurate output.\u003c\/p\u003e\u003ch3\u003eEngineering approach\u003c\/h3\u003e\u003cp\u003eDeterministic components: Crew certifications; geography; work windows; rest limits. These rules control calculations, eligibility and permitted actions. Non-deterministic AI handles interpretation, retrieval and draft recommendations; it does not replace the policy engine. Consequential actions require an authorized human decision.\u003c\/p\u003e\u003cp\u003eModel selection compares task quality, latency, privacy requirements and cost. Claude or OpenAI can support complex language tasks; a specialized small language model can handle narrower classification or retrieval when it meets the same evaluation criteria. Adaptation, quantization and private or edge deployment are considered only when supported by data rights, infrastructure and measured performance. Hosted vendor models remain subject to vendor deployment options and terms.\u003c\/p\u003e\u003ch3\u003eDeliverables\u003c\/h3\u003e\u003cul\u003e\n\u003cli\u003eCurrent-state workflow map, baseline metrics and prioritized requirements\u003c\/li\u003e\n\u003cli\u003eArchitecture, access boundaries, integration design and exception-handling plan\u003c\/li\u003e\n\u003cli\u003eRepresentative evaluation cases and an agreed acceptance scorecard\u003c\/li\u003e\n\u003cli\u003eWorking workflow software, scoped integrations and operator review queue\u003c\/li\u003e\n\u003cli\u003eMonitoring, audit events, deployment runbook, team training and source-code handover\u003c\/li\u003e\n\u003c\/ul\u003e\u003ch3\u003eDelivery and acceptance\u003c\/h3\u003e\u003cp\u003eForward deployed engineers work alongside your operators, with an AI engineer, integration engineer and shared quality and security support. The team reviews progress weekly, tests representative normal and exception cases, and documents limitations. Release follows owner acceptance, access-control testing and a demonstrated pause or rollback process.\u003c\/p\u003e\u003ch3\u003eHow value is measured\u003c\/h3\u003e\u003cp\u003ePrimary measures: Travel hours; on-time completion. Establish a baseline before changes, then compare a controlled pilot or agreed pre\/post period. Track quality, adoption and attributable benefits alongside labor time and ongoing model costs. Targets and acceptance thresholds are agreed during discovery; financial or operational results are not guaranteed.\u003c\/p\u003e\u003ch3\u003eInvestment and timing\u003c\/h3\u003e\u003cp\u003eEstimated engagement: USD $75,000–$180,000. The listed product price of $75,000 is the starting professional services fee, not a deposit or a guaranteed fixed quote. Indicative delivery: 8-16 weeks after prerequisites are available. Final scope, milestones and commercial terms are agreed in a statement of work. Model\/API usage, cloud infrastructure, hardware, third-party licenses, taxes, major data remediation and ongoing managed support are excluded unless expressly included.\u003c\/p\u003e\u003ch3\u003eGetting started\u003c\/h3\u003e\u003cp\u003eProvide a named process owner, authorized access to representative data, integration documentation and operator time for review. Regulated or sensitive workflows require applicable privacy and security approvals before data access. The first working session confirms the use case, baseline, approval boundaries and the smallest valuable delivery scope.\u003c\/p\u003e","brand":"Corefinery","offers":[{"title":"Default Title","offer_id":68850842337474,"sku":"CF-AI-054","price":75000.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0798\/2101\/3186\/files\/Platinum_pathway_to_what_s_next-2.png?v=1794072948"},{"product_id":"nergy-and-utilities-interconnection-application-assistant","title":"Energy: Interconnection application assistant","description":"\u003cp\u003eIncomplete submissions slow project reviews. Corefinery works with your energy and utilities team to turn this bottleneck into a practical, measurable workflow. The engagement focuses on extract application facts and identify missing evidence rather than a standalone demonstration.\u003c\/p\u003e\u003ch3\u003eWhat the engagement covers\u003c\/h3\u003e\u003cp\u003eOne production workflow; up to two integrations; one business unit. We map the current process with the people doing the work, identify decision rights and exceptions, and agree which actions require approval. The workflow is designed around existing systems and an accountable business owner.\u003c\/p\u003e\u003ch3\u003eWorkflow and data\u003c\/h3\u003e\u003cp\u003eSource inputs: Client-authorized source-system records; approved operating policies; representative task examples; integration documentation. Extract application facts and identify missing evidence. Every recommendation should retain its supporting context so an operator can inspect the evidence and correct incomplete or inaccurate output.\u003c\/p\u003e\u003ch3\u003eEngineering approach\u003c\/h3\u003e\u003cp\u003eDeterministic components: Required forms; equipment ratings; engineering approval. These rules control calculations, eligibility and permitted actions. Non-deterministic AI handles interpretation, retrieval and draft recommendations; it does not replace the policy engine. Consequential actions require an authorized human decision.\u003c\/p\u003e\u003cp\u003eModel selection compares task quality, latency, privacy requirements and cost. Claude or OpenAI can support complex language tasks; a specialized small language model can handle narrower classification or retrieval when it meets the same evaluation criteria. Adaptation, quantization and private or edge deployment are considered only when supported by data rights, infrastructure and measured performance. Hosted vendor models remain subject to vendor deployment options and terms.\u003c\/p\u003e\u003ch3\u003eDeliverables\u003c\/h3\u003e\u003cul\u003e\n\u003cli\u003eCurrent-state workflow map, baseline metrics and prioritized requirements\u003c\/li\u003e\n\u003cli\u003eArchitecture, access boundaries, integration design and exception-handling plan\u003c\/li\u003e\n\u003cli\u003eRepresentative evaluation cases and an agreed acceptance scorecard\u003c\/li\u003e\n\u003cli\u003eWorking workflow software, scoped integrations and operator review queue\u003c\/li\u003e\n\u003cli\u003eMonitoring, audit events, deployment runbook, team training and source-code handover\u003c\/li\u003e\n\u003c\/ul\u003e\u003ch3\u003eDelivery and acceptance\u003c\/h3\u003e\u003cp\u003eForward deployed engineers work alongside your operators, with an AI engineer, integration engineer and shared quality and security support. The team reviews progress weekly, tests representative normal and exception cases, and documents limitations. Release follows owner acceptance, access-control testing and a demonstrated pause or rollback process.\u003c\/p\u003e\u003ch3\u003eHow value is measured\u003c\/h3\u003e\u003cp\u003ePrimary measures: Review time; resubmission rate. Establish a baseline before changes, then compare a controlled pilot or agreed pre\/post period. Track quality, adoption and attributable benefits alongside labor time and ongoing model costs. Targets and acceptance thresholds are agreed during discovery; financial or operational results are not guaranteed.\u003c\/p\u003e\u003ch3\u003eInvestment and timing\u003c\/h3\u003e\u003cp\u003eEstimated engagement: USD $90,000–$225,000. The listed product price of $90,000 is the starting professional services fee, not a deposit or a guaranteed fixed quote. Indicative delivery: 8-16 weeks after prerequisites are available. Final scope, milestones and commercial terms are agreed in a statement of work. Model\/API usage, cloud infrastructure, hardware, third-party licenses, taxes, major data remediation and ongoing managed support are excluded unless expressly included.\u003c\/p\u003e\u003ch3\u003eGetting started\u003c\/h3\u003e\u003cp\u003eProvide a named process owner, authorized access to representative data, integration documentation and operator time for review. Regulated or sensitive workflows require applicable privacy and security approvals before data access. The first working session confirms the use case, baseline, approval boundaries and the smallest valuable delivery scope.\u003c\/p\u003e","brand":"Corefinery","offers":[{"title":"Default Title","offer_id":68850845712578,"sku":"CF-AI-055","price":90000.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0798\/2101\/3186\/files\/Platinum_pathway_to_what_s_next-2.png?v=1794072948"},{"product_id":"nergy-and-utilities-energy-trading-research-copilot","title":"Energy: Energy trading research copilot","description":"\u003cp\u003eAnalysts manually reconcile market narratives. Corefinery works with your energy and utilities team to turn this bottleneck into a practical, measurable workflow. The engagement focuses on summarize authorized market information and draft scenario briefs rather than a standalone demonstration.\u003c\/p\u003e\u003ch3\u003eWhat the engagement covers\u003c\/h3\u003e\u003cp\u003eOne production workflow; up to two integrations; one business unit. We map the current process with the people doing the work, identify decision rights and exceptions, and agree which actions require approval. The workflow is designed around existing systems and an accountable business owner.\u003c\/p\u003e\u003ch3\u003eWorkflow and data\u003c\/h3\u003e\u003cp\u003eSource inputs: Client-authorized source-system records; approved operating policies; representative task examples; integration documentation. Summarize authorized market information and draft scenario briefs. Every recommendation should retain its supporting context so an operator can inspect the evidence and correct incomplete or inaccurate output.\u003c\/p\u003e\u003ch3\u003eEngineering approach\u003c\/h3\u003e\u003cp\u003eDeterministic components: Position limits; approved data access; no autonomous trade release. These rules control calculations, eligibility and permitted actions. Non-deterministic AI handles interpretation, retrieval and draft recommendations; it does not replace the policy engine. Consequential actions require an authorized human decision.\u003c\/p\u003e\u003cp\u003eModel selection compares task quality, latency, privacy requirements and cost. Claude or OpenAI can support complex language tasks; a specialized small language model can handle narrower classification or retrieval when it meets the same evaluation criteria. Adaptation, quantization and private or edge deployment are considered only when supported by data rights, infrastructure and measured performance. Hosted vendor models remain subject to vendor deployment options and terms.\u003c\/p\u003e\u003ch3\u003eDeliverables\u003c\/h3\u003e\u003cul\u003e\n\u003cli\u003eCurrent-state workflow map, baseline metrics and prioritized requirements\u003c\/li\u003e\n\u003cli\u003eArchitecture, access boundaries, integration design and exception-handling plan\u003c\/li\u003e\n\u003cli\u003eRepresentative evaluation cases and an agreed acceptance scorecard\u003c\/li\u003e\n\u003cli\u003eWorking workflow software, scoped integrations and operator review queue\u003c\/li\u003e\n\u003cli\u003eMonitoring, audit events, deployment runbook, team training and source-code handover\u003c\/li\u003e\n\u003c\/ul\u003e\u003ch3\u003eDelivery and acceptance\u003c\/h3\u003e\u003cp\u003eForward deployed engineers work alongside your operators, with an AI engineer, integration engineer and shared quality and security support. The team reviews progress weekly, tests representative normal and exception cases, and documents limitations. Release follows owner acceptance, access-control testing and a demonstrated pause or rollback process.\u003c\/p\u003e\u003ch3\u003eHow value is measured\u003c\/h3\u003e\u003cp\u003ePrimary measures: Research hours; source coverage. Establish a baseline before changes, then compare a controlled pilot or agreed pre\/post period. Track quality, adoption and attributable benefits alongside labor time and ongoing model costs. Targets and acceptance thresholds are agreed during discovery; financial or operational results are not guaranteed.\u003c\/p\u003e\u003ch3\u003eInvestment and timing\u003c\/h3\u003e\u003cp\u003eEstimated engagement: USD $100,000–$250,000. The listed product price of $100,000 is the starting professional services fee, not a deposit or a guaranteed fixed quote. Indicative delivery: 8-16 weeks after prerequisites are available. Final scope, milestones and commercial terms are agreed in a statement of work. Model\/API usage, cloud infrastructure, hardware, third-party licenses, taxes, major data remediation and ongoing managed support are excluded unless expressly included.\u003c\/p\u003e\u003ch3\u003eGetting started\u003c\/h3\u003e\u003cp\u003eProvide a named process owner, authorized access to representative data, integration documentation and operator time for review. Regulated or sensitive workflows require applicable privacy and security approvals before data access. The first working session confirms the use case, baseline, approval boundaries and the smallest valuable delivery scope.\u003c\/p\u003e","brand":"Corefinery","offers":[{"title":"Default Title","offer_id":68850845745346,"sku":"CF-AI-056","price":100000.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0798\/2101\/3186\/files\/Platinum_pathway_to_what_s_next-2.png?v=1794072948"},{"product_id":"nergy-and-utilities-utility-customer-resolution","title":"Energy: Utility customer resolution","description":"\u003cp\u003eBilling and service requests require repeated investigation. Corefinery works with your energy and utilities team to turn this bottleneck into a practical, measurable workflow. The engagement focuses on explain billing exceptions and draft service responses rather than a standalone demonstration.\u003c\/p\u003e\u003ch3\u003eWhat the engagement covers\u003c\/h3\u003e\u003cp\u003eOne production workflow; up to two integrations; one business unit. We map the current process with the people doing the work, identify decision rights and exceptions, and agree which actions require approval. The workflow is designed around existing systems and an accountable business owner.\u003c\/p\u003e\u003ch3\u003eWorkflow and data\u003c\/h3\u003e\u003cp\u003eSource inputs: Client-authorized source-system records; approved operating policies; representative task examples; integration documentation. Explain billing exceptions and draft service responses. Every recommendation should retain its supporting context so an operator can inspect the evidence and correct incomplete or inaccurate output.\u003c\/p\u003e\u003ch3\u003eEngineering approach\u003c\/h3\u003e\u003cp\u003eDeterministic components: Tariff calculations; account verification; adjustment limits. These rules control calculations, eligibility and permitted actions. Non-deterministic AI handles interpretation, retrieval and draft recommendations; it does not replace the policy engine. Consequential actions require an authorized human decision.\u003c\/p\u003e\u003cp\u003eModel selection compares task quality, latency, privacy requirements and cost. Claude or OpenAI can support complex language tasks; a specialized small language model can handle narrower classification or retrieval when it meets the same evaluation criteria. Adaptation, quantization and private or edge deployment are considered only when supported by data rights, infrastructure and measured performance. Hosted vendor models remain subject to vendor deployment options and terms.\u003c\/p\u003e\u003ch3\u003eDeliverables\u003c\/h3\u003e\u003cul\u003e\n\u003cli\u003eCurrent-state workflow map, baseline metrics and prioritized requirements\u003c\/li\u003e\n\u003cli\u003eArchitecture, access boundaries, integration design and exception-handling plan\u003c\/li\u003e\n\u003cli\u003eRepresentative evaluation cases and an agreed acceptance scorecard\u003c\/li\u003e\n\u003cli\u003eWorking workflow software, scoped integrations and operator review queue\u003c\/li\u003e\n\u003cli\u003eMonitoring, audit events, deployment runbook, team training and source-code handover\u003c\/li\u003e\n\u003c\/ul\u003e\u003ch3\u003eDelivery and acceptance\u003c\/h3\u003e\u003cp\u003eForward deployed engineers work alongside your operators, with an AI engineer, integration engineer and shared quality and security support. The team reviews progress weekly, tests representative normal and exception cases, and documents limitations. Release follows owner acceptance, access-control testing and a demonstrated pause or rollback process.\u003c\/p\u003e\u003ch3\u003eHow value is measured\u003c\/h3\u003e\u003cp\u003ePrimary measures: Resolution time; repeat calls. Establish a baseline before changes, then compare a controlled pilot or agreed pre\/post period. Track quality, adoption and attributable benefits alongside labor time and ongoing model costs. Targets and acceptance thresholds are agreed during discovery; financial or operational results are not guaranteed.\u003c\/p\u003e\u003ch3\u003eInvestment and timing\u003c\/h3\u003e\u003cp\u003eEstimated engagement: USD $125,000–$300,000. The listed product price of $125,000 is the starting professional services fee, not a deposit or a guaranteed fixed quote. Indicative delivery: 8-16 weeks after prerequisites are available. Final scope, milestones and commercial terms are agreed in a statement of work. Model\/API usage, cloud infrastructure, hardware, third-party licenses, taxes, major data remediation and ongoing managed support are excluded unless expressly included.\u003c\/p\u003e\u003ch3\u003eGetting started\u003c\/h3\u003e\u003cp\u003eProvide a named process owner, authorized access to representative data, integration documentation and operator time for review. Regulated or sensitive workflows require applicable privacy and security approvals before data access. The first working session confirms the use case, baseline, approval boundaries and the smallest valuable delivery scope.\u003c\/p\u003e","brand":"Corefinery","offers":[{"title":"Default Title","offer_id":68850845941954,"sku":"CF-AI-057","price":125000.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0798\/2101\/3186\/files\/Platinum_pathway_to_what_s_next-2.png?v=1794072948"},{"product_id":"nergy-and-utilities-capital-project-evidence-workbench","title":"Energy: Capital project evidence workbench","description":"\u003cp\u003eInfrastructure reviews depend on scattered documents. Corefinery works with your energy and utilities team to turn this bottleneck into a practical, measurable workflow. The engagement focuses on summarize project risks and assemble evidence packs rather than a standalone demonstration.\u003c\/p\u003e\u003ch3\u003eWhat the engagement covers\u003c\/h3\u003e\u003cp\u003eOne production workflow; up to two integrations; one business unit. We map the current process with the people doing the work, identify decision rights and exceptions, and agree which actions require approval. The workflow is designed around existing systems and an accountable business owner.\u003c\/p\u003e\u003ch3\u003eWorkflow and data\u003c\/h3\u003e\u003cp\u003eSource inputs: Client-authorized source-system records; approved operating policies; representative task examples; integration documentation. Summarize project risks and assemble evidence packs. Every recommendation should retain its supporting context so an operator can inspect the evidence and correct incomplete or inaccurate output.\u003c\/p\u003e\u003ch3\u003eEngineering approach\u003c\/h3\u003e\u003cp\u003eDeterministic components: Milestone gates; budget reconciliation; change authority. These rules control calculations, eligibility and permitted actions. Non-deterministic AI handles interpretation, retrieval and draft recommendations; it does not replace the policy engine. Consequential actions require an authorized human decision.\u003c\/p\u003e\u003cp\u003eModel selection compares task quality, latency, privacy requirements and cost. Claude or OpenAI can support complex language tasks; a specialized small language model can handle narrower classification or retrieval when it meets the same evaluation criteria. Adaptation, quantization and private or edge deployment are considered only when supported by data rights, infrastructure and measured performance. Hosted vendor models remain subject to vendor deployment options and terms.\u003c\/p\u003e\u003ch3\u003eDeliverables\u003c\/h3\u003e\u003cul\u003e\n\u003cli\u003eCurrent-state workflow map, baseline metrics and prioritized requirements\u003c\/li\u003e\n\u003cli\u003eArchitecture, access boundaries, integration design and exception-handling plan\u003c\/li\u003e\n\u003cli\u003eRepresentative evaluation cases and an agreed acceptance scorecard\u003c\/li\u003e\n\u003cli\u003eWorking workflow software, scoped integrations and operator review queue\u003c\/li\u003e\n\u003cli\u003eMonitoring, audit events, deployment runbook, team training and source-code handover\u003c\/li\u003e\n\u003c\/ul\u003e\u003ch3\u003eDelivery and acceptance\u003c\/h3\u003e\u003cp\u003eForward deployed engineers work alongside your operators, with an AI engineer, integration engineer and shared quality and security support. The team reviews progress weekly, tests representative normal and exception cases, and documents limitations. Release follows owner acceptance, access-control testing and a demonstrated pause or rollback process.\u003c\/p\u003e\u003ch3\u003eHow value is measured\u003c\/h3\u003e\u003cp\u003ePrimary measures: Review preparation hours; unresolved exceptions. Establish a baseline before changes, then compare a controlled pilot or agreed pre\/post period. Track quality, adoption and attributable benefits alongside labor time and ongoing model costs. Targets and acceptance thresholds are agreed during discovery; financial or operational results are not guaranteed.\u003c\/p\u003e\u003ch3\u003eInvestment and timing\u003c\/h3\u003e\u003cp\u003eEstimated engagement: USD $150,000–$350,000. The listed product price of $150,000 is the starting professional services fee, not a deposit or a guaranteed fixed quote. Indicative delivery: 8-16 weeks after prerequisites are available. Final scope, milestones and commercial terms are agreed in a statement of work. Model\/API usage, cloud infrastructure, hardware, third-party licenses, taxes, major data remediation and ongoing managed support are excluded unless expressly included.\u003c\/p\u003e\u003ch3\u003eGetting started\u003c\/h3\u003e\u003cp\u003eProvide a named process owner, authorized access to representative data, integration documentation and operator time for review. Regulated or sensitive workflows require applicable privacy and security approvals before data access. The first working session confirms the use case, baseline, approval boundaries and the smallest valuable delivery scope.\u003c\/p\u003e","brand":"Corefinery","offers":[{"title":"Default Title","offer_id":68850846236866,"sku":"CF-AI-058","price":150000.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0798\/2101\/3186\/files\/Platinum_pathway_to_what_s_next-2.png?v=1794072948"},{"product_id":"nergy-and-utilities-private-slm-asset-knowledge-engine","title":"Energy: Private SLM asset knowledge engine","description":"\u003cp\u003eRemote teams need fast controlled procedure access. Corefinery works with your energy and utilities team to turn this bottleneck into a practical, measurable workflow. The engagement focuses on retrieve maintenance instructions using compact models rather than a standalone demonstration.\u003c\/p\u003e\u003ch3\u003eWhat the engagement covers\u003c\/h3\u003e\u003cp\u003eOne production workflow; up to two integrations; one business unit. We map the current process with the people doing the work, identify decision rights and exceptions, and agree which actions require approval. The workflow is designed around existing systems and an accountable business owner.\u003c\/p\u003e\u003ch3\u003eWorkflow and data\u003c\/h3\u003e\u003cp\u003eSource inputs: Client-authorized source-system records; approved operating policies; representative task examples; integration documentation. Retrieve maintenance instructions using compact models. Every recommendation should retain its supporting context so an operator can inspect the evidence and correct incomplete or inaccurate output.\u003c\/p\u003e\u003ch3\u003eEngineering approach\u003c\/h3\u003e\u003cp\u003eDeterministic components: Asset permissions; procedure versions; offline boundaries. These rules control calculations, eligibility and permitted actions. Non-deterministic AI handles interpretation, retrieval and draft recommendations; it does not replace the policy engine. Consequential actions require an authorized human decision.\u003c\/p\u003e\u003cp\u003eModel selection compares task quality, latency, privacy requirements and cost. Claude or OpenAI can support complex language tasks; a specialized small language model can handle narrower classification or retrieval when it meets the same evaluation criteria. Adaptation, quantization and private or edge deployment are considered only when supported by data rights, infrastructure and measured performance. Hosted vendor models remain subject to vendor deployment options and terms.\u003c\/p\u003e\u003ch3\u003eDeliverables\u003c\/h3\u003e\u003cul\u003e\n\u003cli\u003eCurrent-state workflow map, baseline metrics and prioritized requirements\u003c\/li\u003e\n\u003cli\u003eArchitecture, access boundaries, integration design and exception-handling plan\u003c\/li\u003e\n\u003cli\u003eRepresentative evaluation cases and an agreed acceptance scorecard\u003c\/li\u003e\n\u003cli\u003eWorking workflow software, scoped integrations and operator review queue\u003c\/li\u003e\n\u003cli\u003eMonitoring, audit events, deployment runbook, team training and source-code handover\u003c\/li\u003e\n\u003c\/ul\u003e\u003ch3\u003eDelivery and acceptance\u003c\/h3\u003e\u003cp\u003eForward deployed engineers work alongside your operators, with an AI engineer, integration engineer and shared quality and security support. The team reviews progress weekly, tests representative normal and exception cases, and documents limitations. Release follows owner acceptance, access-control testing and a demonstrated pause or rollback process.\u003c\/p\u003e\u003ch3\u003eHow value is measured\u003c\/h3\u003e\u003cp\u003ePrimary measures: Response latency; cited-answer accuracy. Establish a baseline before changes, then compare a controlled pilot or agreed pre\/post period. Track quality, adoption and attributable benefits alongside labor time and ongoing model costs. Targets and acceptance thresholds are agreed during discovery; financial or operational results are not guaranteed.\u003c\/p\u003e\u003ch3\u003eInvestment and timing\u003c\/h3\u003e\u003cp\u003eEstimated engagement: USD $175,000–$400,000. The listed product price of $175,000 is the starting professional services fee, not a deposit or a guaranteed fixed quote. Indicative delivery: 8-16 weeks after prerequisites are available. Final scope, milestones and commercial terms are agreed in a statement of work. Model\/API usage, cloud infrastructure, hardware, third-party licenses, taxes, major data remediation and ongoing managed support are excluded unless expressly included.\u003c\/p\u003e\u003ch3\u003eGetting started\u003c\/h3\u003e\u003cp\u003eProvide a named process owner, authorized access to representative data, integration documentation and operator time for review. Regulated or sensitive workflows require applicable privacy and security approvals before data access. The first working session confirms the use case, baseline, approval boundaries and the smallest valuable delivery scope.\u003c\/p\u003e","brand":"Corefinery","offers":[{"title":"Default Title","offer_id":68850849611970,"sku":"CF-AI-059","price":175000.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0798\/2101\/3186\/files\/Platinum_pathway_to_what_s_next-2.png?v=1794072948"},{"product_id":"ogistics-and-transportation-shipment-exception-orchestrator","title":"Logistics: Shipment exception orchestrator","description":"\u003cp\u003eDisruptions trigger manual coordination. Corefinery works with your logistics and transportation team to turn this bottleneck into a practical, measurable workflow. The engagement focuses on interpret disruption messages and recommend recovery options rather than a standalone demonstration.\u003c\/p\u003e\u003ch3\u003eWhat the engagement covers\u003c\/h3\u003e\u003cp\u003eOne production workflow; up to two integrations; one business unit. We map the current process with the people doing the work, identify decision rights and exceptions, and agree which actions require approval. The workflow is designed around existing systems and an accountable business owner.\u003c\/p\u003e\u003ch3\u003eWorkflow and data\u003c\/h3\u003e\u003cp\u003eSource inputs: Client-authorized source-system records; approved operating policies; representative task examples; integration documentation. Interpret disruption messages and recommend recovery options. Every recommendation should retain its supporting context so an operator can inspect the evidence and correct incomplete or inaccurate output.\u003c\/p\u003e\u003ch3\u003eEngineering approach\u003c\/h3\u003e\u003cp\u003eDeterministic components: Delivery commitments; carrier limits; dispatch approval. These rules control calculations, eligibility and permitted actions. Non-deterministic AI handles interpretation, retrieval and draft recommendations; it does not replace the policy engine. Consequential actions require an authorized human decision.\u003c\/p\u003e\u003cp\u003eModel selection compares task quality, latency, privacy requirements and cost. Claude or OpenAI can support complex language tasks; a specialized small language model can handle narrower classification or retrieval when it meets the same evaluation criteria. Adaptation, quantization and private or edge deployment are considered only when supported by data rights, infrastructure and measured performance. Hosted vendor models remain subject to vendor deployment options and terms.\u003c\/p\u003e\u003ch3\u003eDeliverables\u003c\/h3\u003e\u003cul\u003e\n\u003cli\u003eCurrent-state workflow map, baseline metrics and prioritized requirements\u003c\/li\u003e\n\u003cli\u003eArchitecture, access boundaries, integration design and exception-handling plan\u003c\/li\u003e\n\u003cli\u003eRepresentative evaluation cases and an agreed acceptance scorecard\u003c\/li\u003e\n\u003cli\u003eWorking workflow software, scoped integrations and operator review queue\u003c\/li\u003e\n\u003cli\u003eMonitoring, audit events, deployment runbook, team training and source-code handover\u003c\/li\u003e\n\u003c\/ul\u003e\u003ch3\u003eDelivery and acceptance\u003c\/h3\u003e\u003cp\u003eForward deployed engineers work alongside your operators, with an AI engineer, integration engineer and shared quality and security support. The team reviews progress weekly, tests representative normal and exception cases, and documents limitations. Release follows owner acceptance, access-control testing and a demonstrated pause or rollback process.\u003c\/p\u003e\u003ch3\u003eHow value is measured\u003c\/h3\u003e\u003cp\u003ePrimary measures: Exception resolution time; on-time delivery. Establish a baseline before changes, then compare a controlled pilot or agreed pre\/post period. Track quality, adoption and attributable benefits alongside labor time and ongoing model costs. Targets and acceptance thresholds are agreed during discovery; financial or operational results are not guaranteed.\u003c\/p\u003e\u003ch3\u003eInvestment and timing\u003c\/h3\u003e\u003cp\u003eEstimated engagement: USD $40,000–$90,000. The listed product price of $40,000 is the starting professional services fee, not a deposit or a guaranteed fixed quote. Indicative delivery: 8-16 weeks after prerequisites are available. Final scope, milestones and commercial terms are agreed in a statement of work. Model\/API usage, cloud infrastructure, hardware, third-party licenses, taxes, major data remediation and ongoing managed support are excluded unless expressly included.\u003c\/p\u003e\u003ch3\u003eGetting started\u003c\/h3\u003e\u003cp\u003eProvide a named process owner, authorized access to representative data, integration documentation and operator time for review. Regulated or sensitive workflows require applicable privacy and security approvals before data access. The first working session confirms the use case, baseline, approval boundaries and the smallest valuable delivery scope.\u003c\/p\u003e","brand":"Corefinery","offers":[{"title":"Default Title","offer_id":68850850660546,"sku":"CF-AI-062","price":40000.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0798\/2101\/3186\/files\/Platinum_pathway_to_what_s_next-2.png?v=1794072948"},{"product_id":"ogistics-and-transportation-freight-procurement-copilot","title":"Logistics: Freight procurement copilot","description":"\u003cp\u003eLane bids are difficult to compare. Corefinery works with your logistics and transportation team to turn this bottleneck into a practical, measurable workflow. The engagement focuses on summarize bid tradeoffs and draft carrier negotiations rather than a standalone demonstration.\u003c\/p\u003e\u003ch3\u003eWhat the engagement covers\u003c\/h3\u003e\u003cp\u003eOne production workflow; up to two integrations; one business unit. We map the current process with the people doing the work, identify decision rights and exceptions, and agree which actions require approval. The workflow is designed around existing systems and an accountable business owner.\u003c\/p\u003e\u003ch3\u003eWorkflow and data\u003c\/h3\u003e\u003cp\u003eSource inputs: Client-authorized source-system records; approved operating policies; representative task examples; integration documentation. Summarize bid tradeoffs and draft carrier negotiations. Every recommendation should retain its supporting context so an operator can inspect the evidence and correct incomplete or inaccurate output.\u003c\/p\u003e\u003ch3\u003eEngineering approach\u003c\/h3\u003e\u003cp\u003eDeterministic components: Rate normalization; service requirements; award approval. These rules control calculations, eligibility and permitted actions. Non-deterministic AI handles interpretation, retrieval and draft recommendations; it does not replace the policy engine. Consequential actions require an authorized human decision.\u003c\/p\u003e\u003cp\u003eModel selection compares task quality, latency, privacy requirements and cost. Claude or OpenAI can support complex language tasks; a specialized small language model can handle narrower classification or retrieval when it meets the same evaluation criteria. Adaptation, quantization and private or edge deployment are considered only when supported by data rights, infrastructure and measured performance. Hosted vendor models remain subject to vendor deployment options and terms.\u003c\/p\u003e\u003ch3\u003eDeliverables\u003c\/h3\u003e\u003cul\u003e\n\u003cli\u003eCurrent-state workflow map, baseline metrics and prioritized requirements\u003c\/li\u003e\n\u003cli\u003eArchitecture, access boundaries, integration design and exception-handling plan\u003c\/li\u003e\n\u003cli\u003eRepresentative evaluation cases and an agreed acceptance scorecard\u003c\/li\u003e\n\u003cli\u003eWorking workflow software, scoped integrations and operator review queue\u003c\/li\u003e\n\u003cli\u003eMonitoring, audit events, deployment runbook, team training and source-code handover\u003c\/li\u003e\n\u003c\/ul\u003e\u003ch3\u003eDelivery and acceptance\u003c\/h3\u003e\u003cp\u003eForward deployed engineers work alongside your operators, with an AI engineer, integration engineer and shared quality and security support. The team reviews progress weekly, tests representative normal and exception cases, and documents limitations. Release follows owner acceptance, access-control testing and a demonstrated pause or rollback process.\u003c\/p\u003e\u003ch3\u003eHow value is measured\u003c\/h3\u003e\u003cp\u003ePrimary measures: Sourcing hours; awarded lane cost. Establish a baseline before changes, then compare a controlled pilot or agreed pre\/post period. Track quality, adoption and attributable benefits alongside labor time and ongoing model costs. Targets and acceptance thresholds are agreed during discovery; financial or operational results are not guaranteed.\u003c\/p\u003e\u003ch3\u003eInvestment and timing\u003c\/h3\u003e\u003cp\u003eEstimated engagement: USD $60,000–$140,000. The listed product price of $60,000 is the starting professional services fee, not a deposit or a guaranteed fixed quote. Indicative delivery: 8-16 weeks after prerequisites are available. Final scope, milestones and commercial terms are agreed in a statement of work. Model\/API usage, cloud infrastructure, hardware, third-party licenses, taxes, major data remediation and ongoing managed support are excluded unless expressly included.\u003c\/p\u003e\u003ch3\u003eGetting started\u003c\/h3\u003e\u003cp\u003eProvide a named process owner, authorized access to representative data, integration documentation and operator time for review. Regulated or sensitive workflows require applicable privacy and security approvals before data access. The first working session confirms the use case, baseline, approval boundaries and the smallest valuable delivery scope.\u003c\/p\u003e","brand":"Corefinery","offers":[{"title":"Default Title","offer_id":68850850791618,"sku":"CF-AI-063","price":60000.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0798\/2101\/3186\/files\/Platinum_pathway_to_what_s_next-2.png?v=1794072948"}],"url":"https:\/\/corefinery.com\/collections\/intelligent-workflows-agents.oembed","provider":"Corefinery","version":"1.0","type":"link"}