{"title":"Technology \u0026 telecommunications","description":"\u003cp\u003eConnect intelligence across software delivery, infrastructure and customer operations. Help teams resolve incidents, manage costs and build better products and services.\u003c\/p\u003e","products":[{"product_id":"echnology-and-telecommunications-ai-value-and-deployment-assessment","title":"Technology: AI value and deployment assessment","description":"\u003cp\u003eIdentify where AI can create practical value in technology and telecommunications before committing to implementation. Corefinery reviews operating friction, data readiness and delivery constraints, then compares Claude, OpenAI and specialized small language model approaches against representative tasks.\u003c\/p\u003e\u003ch3\u003eWhat the engagement covers\u003c\/h3\u003e\u003cp\u003eOne business unit; up to five workflow candidates; two vendor deployment options and one SLM option. 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. Evaluate Claude and OpenAI deployment options plus SLM feasibility. 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: Data access approval; weighted scoring; security checklist. 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\u003eVendor and SLM comparison, business case, cost assumptions and a sequenced 90-day implementation roadmap\u003c\/li\u003e\n\u003c\/ul\u003e\u003ch3\u003eDelivery and acceptance\u003c\/h3\u003e\u003cp\u003eA senior architect and domain lead conduct the assessment with process owners. The team reviews progress weekly, tests representative normal and exception cases, and documents limitations. Implementation begins under a separately agreed scope.\u003c\/p\u003e\u003ch3\u003eHow value is measured\u003c\/h3\u003e\u003cp\u003ePrimary measures: Baseline coverage; validated business cases. 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 $10,000–$25,000. The listed product price of $10,000 is the starting professional services fee, not a deposit or a guaranteed fixed quote. Indicative delivery: 2-3 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":68850857705666,"sku":"CF-AI-071","price":10000.0,"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":"echnology-and-telecommunications-software-delivery-copilot","title":"Technology: Software delivery copilot","description":"\u003cp\u003eEngineers lose time understanding unfamiliar systems. Corefinery works with your technology and telecommunications team to turn this bottleneck into a practical, measurable workflow. The engagement focuses on explain code paths and draft tested changes 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 code paths and draft tested changes. 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: Test gates; code review; deployment 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: Lead time; escaped 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 $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":68850857738434,"sku":"CF-AI-072","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":"echnology-and-telecommunications-network-incident-investigator","title":"Technology: Network incident investigator","description":"\u003cp\u003eEngineers correlate noisy incident evidence. Corefinery works with your technology and telecommunications team to turn this bottleneck into a practical, measurable workflow. The engagement focuses on build incident timelines and suggest diagnostic 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. Build incident timelines and suggest diagnostic 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: Alert correlation rules; access controls; change 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: Time to diagnosis; false escalation 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":68850857771202,"sku":"CF-AI-073","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":"echnology-and-telecommunications-customer-support-engineering-assistant","title":"Technology: Customer support engineering assistant","description":"\u003cp\u003eComplex support cases require repeated research. Corefinery works with your technology and telecommunications team to turn this bottleneck into a practical, measurable workflow. The engagement focuses on retrieve technical evidence and draft resolution 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 technical evidence and draft resolution 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: Tenant isolation; entitlement checks; response 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: Resolution time; reopened cases. 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":68850858688706,"sku":"CF-AI-074","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":"echnology-and-telecommunications-cloud-cost-action-planner","title":"Technology: Cloud cost action planner","description":"\u003cp\u003eWaste is visible but hard to remediate. Corefinery works with your technology and telecommunications team to turn this bottleneck into a practical, measurable workflow. The engagement focuses on explain spend anomalies and propose rightsizing 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 spend anomalies and propose rightsizing 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: Budget rules; workload dependencies; change windows. 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: Verified savings; recommendation acceptance. 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":68850858721474,"sku":"CF-AI-075","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":"echnology-and-telecommunications-enterprise-migration-factory","title":"Technology: Enterprise migration factory","description":"\u003cp\u003eLegacy migrations require manual dependency analysis. Corefinery works with your technology and telecommunications team to turn this bottleneck into a practical, measurable workflow. The engagement focuses on map dependencies and draft migration changes 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 dependencies and draft migration changes. 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: Compatibility checks; migration tests; rollback 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: Migration throughput; failed cutovers. 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":68850858754242,"sku":"CF-AI-076","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":"echnology-and-telecommunications-security-investigation-copilot","title":"Technology: Security investigation copilot","description":"\u003cp\u003eAnalysts manually summarize fragmented alerts. Corefinery works with your technology and telecommunications team to turn this bottleneck into a practical, measurable workflow. The engagement focuses on summarize telemetry and recommend investigation 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. Summarize telemetry and recommend investigation 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: Evidence preservation; isolation authority; incident policy. 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: Analyst time; review-confirmed 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":68850859180226,"sku":"CF-AI-077","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":"echnology-and-telecommunications-revenue-operations-intelligence","title":"Technology: Revenue operations intelligence","description":"\u003cp\u003eDeal signals and next steps are fragmented. Corefinery works with your technology and telecommunications team to turn this bottleneck into a practical, measurable workflow. The engagement focuses on summarize accounts and draft evidence-based next 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 accounts and draft evidence-based next 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: CRM stage rules; discount authority; consent 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: CRM completeness; sales preparation 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 $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":68850859245762,"sku":"CF-AI-078","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":"echnology-and-telecommunications-private-slm-developer-platform","title":"Technology: Private SLM developer platform","description":"\u003cp\u003eRoutine engineering tasks need controlled economics. Corefinery works with your technology and telecommunications team to turn this bottleneck into a practical, measurable workflow. The engagement focuses on route code classification and retrieval to 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. Route code classification and retrieval to 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: Repository permissions; schema validation; test enforcement. 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 accepted task; latency. 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":68850859278530,"sku":"CF-AI-079","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":"echnology-and-telecommunications-enterprise-ai-workflow-transformation","title":"Technology: Enterprise AI workflow transformation","description":"\u003cp\u003eDisconnected workflows limit enterprise value in technology and telecommunications. Corefinery works with your technology and telecommunications team to turn this bottleneck into a practical, measurable workflow. The engagement focuses on coordinate bounded agents across prioritized workflows rather than a standalone demonstration.\u003c\/p\u003e\u003ch3\u003eWhat the engagement covers\u003c\/h3\u003e\u003cp\u003eUp to three production workflows; three integrations; two business units; phased rollout. 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. Coordinate bounded agents across prioritized workflows. 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: Policy engine; transaction validation; approval matrix; rollback 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: Verified net value; adoption; task success. 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 $500,000–$1,000,000. The listed product price of $500,000 is the starting professional services fee, not a deposit or a guaranteed fixed quote. Indicative delivery: 20-36 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":68850859311298,"sku":"CF-AI-080","price":500000.0,"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":"technology-ai-product-evaluation-laboratory","title":"Technology: AI product evaluation laboratory","description":"\u003cp\u003eAI features ship without representative quality evidence. Corefinery works with your technology and telecommunications team to turn this bottleneck into a practical, measurable workflow. The engagement focuses on analyze failure patterns and generate candidate test cases rather than a standalone demonstration.\u003c\/p\u003e\u003ch3\u003eWhat the engagement covers\u003c\/h3\u003e\u003cp\u003eOne prioritized production workflow; up to two source-system integrations; one business unit; staged operator rollout. 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: Task datasets; model outputs; reviewer annotations. Analyze failure patterns and generate candidate test cases. 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: Dataset splits; scoring thresholds; 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: Task success; regression escape 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 $50,000–$125,000. The listed product price of $50,000 is the starting professional services fee, not a deposit or a guaranteed fixed quote. Indicative delivery: 8-20 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":68862001512642,"sku":"CF-AI-171","price":50000.0,"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":"technology-enterprise-knowledge-migration-studio","title":"Technology: Enterprise knowledge migration studio","description":"\u003cp\u003eLegacy knowledge is duplicated and hard to retrieve. Corefinery works with your technology and telecommunications team to turn this bottleneck into a practical, measurable workflow. The engagement focuses on classify documents and suggest knowledge organization rather than a standalone demonstration.\u003c\/p\u003e\u003ch3\u003eWhat the engagement covers\u003c\/h3\u003e\u003cp\u003eOne prioritized production workflow; up to two source-system integrations; one business unit; staged operator rollout. 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: Authorized repositories; document metadata; ownership lists. Classify documents and suggest knowledge organization. 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: Access inheritance; deduplication rules; retention. 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: Migration completeness; retrieval 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 $75,000–$175,000. The listed product price of $75,000 is the starting professional services fee, not a deposit or a guaranteed fixed quote. Indicative delivery: 8-20 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":68862002168002,"sku":"CF-AI-172","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":"technology-telecom-order-fallout-resolver","title":"Technology: Telecom order fallout resolver","description":"\u003cp\u003eComplex provisioning orders stall in exception queues. Corefinery works with your technology and telecommunications team to turn this bottleneck into a practical, measurable workflow. The engagement focuses on summarize fallout causes and propose resolution steps rather than a standalone demonstration.\u003c\/p\u003e\u003ch3\u003eWhat the engagement covers\u003c\/h3\u003e\u003cp\u003eOne prioritized production workflow; up to two source-system integrations; one business unit; staged operator rollout. 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: Order events; service catalogs; provisioning statuses. Summarize fallout causes and propose resolution 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: Dependency checks; service eligibility; activation 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: Fallout age; order 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 $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: 8-20 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":68862003085506,"sku":"CF-AI-173","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":"technology-saas-customer-onboarding-navigator","title":"Technology: SaaS customer onboarding navigator","description":"\u003cp\u003eCustomers lose momentum across technical setup steps. Corefinery works with your technology and telecommunications team to turn this bottleneck into a practical, measurable workflow. The engagement focuses on draft tailored setup guidance and identify blockers rather than a standalone demonstration.\u003c\/p\u003e\u003ch3\u003eWhat the engagement covers\u003c\/h3\u003e\u003cp\u003eOne prioritized production workflow; up to two source-system integrations; one business unit; staged operator rollout. 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: Implementation plans; support records; product guides. Draft tailored setup guidance and identify blockers. 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 prerequisites; tenant permissions; milestone 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: Time to first value; onboarding 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 $120,000–$300,000. The listed product price of $120,000 is the starting professional services fee, not a deposit or a guaranteed fixed quote. Indicative delivery: 8-20 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":68862003740866,"sku":"CF-AI-174","price":120000.0,"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":"technology-product-feedback-prioritization-studio","title":"Technology: Product feedback prioritization studio","description":"\u003cp\u003eProduct teams cannot reconcile feedback at scale. Corefinery works with your technology and telecommunications team to turn this bottleneck into a practical, measurable workflow. The engagement focuses on cluster requests and draft evidence-backed opportunity briefs rather than a standalone demonstration.\u003c\/p\u003e\u003ch3\u003eWhat the engagement covers\u003c\/h3\u003e\u003cp\u003eOne prioritized production workflow; up to two source-system integrations; one business unit; staged operator rollout. 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: Consented feedback; support themes; roadmap items. Cluster requests and draft evidence-backed opportunity 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: Source provenance; account permissions; prioritization rubric. 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: Analysis time; linked feedback 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 $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: 8-20 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":68862003839170,"sku":"CF-AI-175","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":"technology-data-incident-response-assistant","title":"Technology: Data incident response assistant","description":"\u003cp\u003eData failures require repeated lineage investigation. Corefinery works with your technology and telecommunications team to turn this bottleneck into a practical, measurable workflow. The engagement focuses on summarize failure scope and propose diagnostic actions rather than a standalone demonstration.\u003c\/p\u003e\u003ch3\u003eWhat the engagement covers\u003c\/h3\u003e\u003cp\u003eOne prioritized production workflow; up to two source-system integrations; one business unit; staged operator rollout. 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: Pipeline events; data contracts; lineage records. Summarize failure scope and propose diagnostic 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: Schema checks; freshness thresholds; rollback 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: Time to diagnosis; incident recurrence. 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–$450,000. The listed product price of $175,000 is the starting professional services fee, not a deposit or a guaranteed fixed quote. Indicative delivery: 8-20 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":68862004527298,"sku":"CF-AI-176","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":"technology-telecom-field-installation-planner","title":"Technology: Telecom field installation planner","description":"\u003cp\u003eInstallation visits fail due to readiness gaps. Corefinery works with your technology and telecommunications team to turn this bottleneck into a practical, measurable workflow. The engagement focuses on summarize blockers and propose appointment sequencing rather than a standalone demonstration.\u003c\/p\u003e\u003ch3\u003eWhat the engagement covers\u003c\/h3\u003e\u003cp\u003eOne prioritized production workflow; up to two source-system integrations; one business unit; staged operator rollout. 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: Site surveys; equipment inventory; appointment calendars. Summarize blockers and propose appointment sequencing. 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 compatibility; technician skills; site readiness. 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: First-visit completion; repeat visits. 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 $200,000–$500,000. The listed product price of $200,000 is the starting professional services fee, not a deposit or a guaranteed fixed quote. Indicative delivery: 8-20 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":68862005772482,"sku":"CF-AI-177","price":200000.0,"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":"technology-ai-inference-cost-control-plane","title":"Technology: AI inference cost control plane","description":"\u003cp\u003eUncontrolled model routing creates expensive workloads. Corefinery works with your technology and telecommunications team to turn this bottleneck into a practical, measurable workflow. The engagement focuses on classify task complexity and recommend evaluated model routes rather than a standalone demonstration.\u003c\/p\u003e\u003ch3\u003eWhat the engagement covers\u003c\/h3\u003e\u003cp\u003eOne prioritized production workflow; up to two source-system integrations; one business unit; staged operator rollout. 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: Usage logs; approved model inventory; task evaluations. Classify task complexity and recommend evaluated model routes. 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: Budgets; rate limits; routing policy; quality floors. 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 successful task; latency. 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 $250,000–$600,000. The listed product price of $250,000 is the starting professional services fee, not a deposit or a guaranteed fixed quote. Indicative delivery: 8-20 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":68862005870786,"sku":"CF-AI-178","price":250000.0,"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":"technology-claude-enterprise-workflow-deployment","title":"Technology: Claude enterprise workflow deployment","description":"\u003cp\u003eTeams need a governed path from Claude experimentation to one usable industry workflow. Corefinery works with your technology and telecommunications team to turn this bottleneck into a practical, measurable workflow. The engagement focuses on use Claude to retrieve evidence, interpret requests and draft multi-step work proposals rather than a standalone demonstration.\u003c\/p\u003e\u003ch3\u003eWhat the engagement covers\u003c\/h3\u003e\u003cp\u003eOne prioritized production workflow; up to two source-system integrations; one business unit; staged operator rollout. 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-approved knowledge sources; representative tasks; identity directory; authorized tool endpoints. Use Claude to retrieve evidence, interpret requests and draft multi-step work proposals. 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: SSO and role checks; tool argument validation; spend caps; action approval; audit logs. 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: Accepted task completion; grounded answer accuracy; cost per task. 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–$350,000. 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":68862006591682,"sku":"CF-AI-179","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":"technology-openai-production-agent-engineering","title":"Technology: OpenAI production agent engineering","description":"\u003cp\u003ePromising OpenAI prototypes lack production integration and accountable operating controls. Corefinery works with your technology and telecommunications team to turn this bottleneck into a practical, measurable workflow. The engagement focuses on use OpenAI APIs to interpret requests, retrieve context and propose bounded tool actions rather than a standalone demonstration.\u003c\/p\u003e\u003ch3\u003eWhat the engagement covers\u003c\/h3\u003e\u003cp\u003eOne prioritized production workflow; up to two source-system integrations; one business unit; staged operator rollout. 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-approved documents; evaluation cases; identity directory; authorized system interfaces. Use OpenAI APIs to interpret requests, retrieve context and propose bounded tool 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: Schema validation; entitlement checks; idempotency controls; approval gates; rollback. 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: End-to-end task success; action error rate; p95 latency. 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–$400,000. The listed product price of $125,000 is the starting professional services fee, not a deposit or a guaranteed fixed quote. Indicative delivery: 8-18 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":68862007836866,"sku":"CF-AI-180","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"}],"url":"https:\/\/corefinery.com\/collections\/technology-telecommunications.oembed","provider":"Corefinery","version":"1.0","type":"link"}