{"title":"Logistics \u0026 transportation","description":"\u003cp\u003eBring intelligence to the movement of goods. Coordinate shipments, warehouse capacity, fleets and exceptions to support more reliable delivery and efficient operations.\u003c\/p\u003e","products":[{"product_id":"ogistics-and-transportation-ai-value-and-deployment-assessment","title":"Logistics: AI value and deployment assessment","description":"\u003cp\u003eIdentify where AI can create practical value in logistics and transportation 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":68850849841346,"sku":"CF-AI-061","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":"ogistics-and-transportation-shipment-exception-orchestrator","title":"Logistics: Shipment exception orchestrator","description":"\u003cp\u003eDisruptions trigger manual coordination. Corefinery works with your logistics and transportation team to turn this bottleneck into a practical, measurable workflow. The engagement focuses on interpret disruption messages and recommend recovery options rather than a standalone demonstration.\u003c\/p\u003e\u003ch3\u003eWhat the engagement covers\u003c\/h3\u003e\u003cp\u003eOne production workflow; up to two integrations; one business unit. We map the current process with the people doing the work, identify decision rights and exceptions, and agree which actions require approval. The workflow is designed around existing systems and an accountable business owner.\u003c\/p\u003e\u003ch3\u003eWorkflow and data\u003c\/h3\u003e\u003cp\u003eSource inputs: Client-authorized source-system records; approved operating policies; representative task examples; integration documentation. Interpret disruption messages and recommend recovery options. Every recommendation should retain its supporting context so an operator can inspect the evidence and correct incomplete or inaccurate output.\u003c\/p\u003e\u003ch3\u003eEngineering approach\u003c\/h3\u003e\u003cp\u003eDeterministic components: Delivery commitments; carrier limits; dispatch approval. These rules control calculations, eligibility and permitted actions. Non-deterministic AI handles interpretation, retrieval and draft recommendations; it does not replace the policy engine. Consequential actions require an authorized human decision.\u003c\/p\u003e\u003cp\u003eModel selection compares task quality, latency, privacy requirements and cost. Claude or OpenAI can support complex language tasks; a specialized small language model can handle narrower classification or retrieval when it meets the same evaluation criteria. Adaptation, quantization and private or edge deployment are considered only when supported by data rights, infrastructure and measured performance. Hosted vendor models remain subject to vendor deployment options and terms.\u003c\/p\u003e\u003ch3\u003eDeliverables\u003c\/h3\u003e\u003cul\u003e\n\u003cli\u003eCurrent-state workflow map, baseline metrics and prioritized requirements\u003c\/li\u003e\n\u003cli\u003eArchitecture, access boundaries, integration design and exception-handling plan\u003c\/li\u003e\n\u003cli\u003eRepresentative evaluation cases and an agreed acceptance scorecard\u003c\/li\u003e\n\u003cli\u003eWorking workflow software, scoped integrations and operator review queue\u003c\/li\u003e\n\u003cli\u003eMonitoring, audit events, deployment runbook, team training and source-code handover\u003c\/li\u003e\n\u003c\/ul\u003e\u003ch3\u003eDelivery and acceptance\u003c\/h3\u003e\u003cp\u003eForward deployed engineers work alongside your operators, with an AI engineer, integration engineer and shared quality and security support. The team reviews progress weekly, tests representative normal and exception cases, and documents limitations. Release follows owner acceptance, access-control testing and a demonstrated pause or rollback process.\u003c\/p\u003e\u003ch3\u003eHow value is measured\u003c\/h3\u003e\u003cp\u003ePrimary measures: Exception resolution time; on-time delivery. Establish a baseline before changes, then compare a controlled pilot or agreed pre\/post period. Track quality, adoption and attributable benefits alongside labor time and ongoing model costs. Targets and acceptance thresholds are agreed during discovery; financial or operational results are not guaranteed.\u003c\/p\u003e\u003ch3\u003eInvestment and timing\u003c\/h3\u003e\u003cp\u003eEstimated engagement: USD $40,000–$90,000. The listed product price of $40,000 is the starting professional services fee, not a deposit or a guaranteed fixed quote. Indicative delivery: 8-16 weeks after prerequisites are available. Final scope, milestones and commercial terms are agreed in a statement of work. Model\/API usage, cloud infrastructure, hardware, third-party licenses, taxes, major data remediation and ongoing managed support are excluded unless expressly included.\u003c\/p\u003e\u003ch3\u003eGetting started\u003c\/h3\u003e\u003cp\u003eProvide a named process owner, authorized access to representative data, integration documentation and operator time for review. Regulated or sensitive workflows require applicable privacy and security approvals before data access. The first working session confirms the use case, baseline, approval boundaries and the smallest valuable delivery scope.\u003c\/p\u003e","brand":"Corefinery","offers":[{"title":"Default Title","offer_id":68850850660546,"sku":"CF-AI-062","price":40000.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0798\/2101\/3186\/files\/Platinum_pathway_to_what_s_next-2.png?v=1794072948"},{"product_id":"ogistics-and-transportation-freight-procurement-copilot","title":"Logistics: Freight procurement copilot","description":"\u003cp\u003eLane bids are difficult to compare. Corefinery works with your logistics and transportation team to turn this bottleneck into a practical, measurable workflow. The engagement focuses on summarize bid tradeoffs and draft carrier negotiations rather than a standalone demonstration.\u003c\/p\u003e\u003ch3\u003eWhat the engagement covers\u003c\/h3\u003e\u003cp\u003eOne production workflow; up to two integrations; one business unit. We map the current process with the people doing the work, identify decision rights and exceptions, and agree which actions require approval. The workflow is designed around existing systems and an accountable business owner.\u003c\/p\u003e\u003ch3\u003eWorkflow and data\u003c\/h3\u003e\u003cp\u003eSource inputs: Client-authorized source-system records; approved operating policies; representative task examples; integration documentation. Summarize bid tradeoffs and draft carrier negotiations. Every recommendation should retain its supporting context so an operator can inspect the evidence and correct incomplete or inaccurate output.\u003c\/p\u003e\u003ch3\u003eEngineering approach\u003c\/h3\u003e\u003cp\u003eDeterministic components: Rate normalization; service requirements; award approval. These rules control calculations, eligibility and permitted actions. Non-deterministic AI handles interpretation, retrieval and draft recommendations; it does not replace the policy engine. Consequential actions require an authorized human decision.\u003c\/p\u003e\u003cp\u003eModel selection compares task quality, latency, privacy requirements and cost. Claude or OpenAI can support complex language tasks; a specialized small language model can handle narrower classification or retrieval when it meets the same evaluation criteria. Adaptation, quantization and private or edge deployment are considered only when supported by data rights, infrastructure and measured performance. Hosted vendor models remain subject to vendor deployment options and terms.\u003c\/p\u003e\u003ch3\u003eDeliverables\u003c\/h3\u003e\u003cul\u003e\n\u003cli\u003eCurrent-state workflow map, baseline metrics and prioritized requirements\u003c\/li\u003e\n\u003cli\u003eArchitecture, access boundaries, integration design and exception-handling plan\u003c\/li\u003e\n\u003cli\u003eRepresentative evaluation cases and an agreed acceptance scorecard\u003c\/li\u003e\n\u003cli\u003eWorking workflow software, scoped integrations and operator review queue\u003c\/li\u003e\n\u003cli\u003eMonitoring, audit events, deployment runbook, team training and source-code handover\u003c\/li\u003e\n\u003c\/ul\u003e\u003ch3\u003eDelivery and acceptance\u003c\/h3\u003e\u003cp\u003eForward deployed engineers work alongside your operators, with an AI engineer, integration engineer and shared quality and security support. The team reviews progress weekly, tests representative normal and exception cases, and documents limitations. Release follows owner acceptance, access-control testing and a demonstrated pause or rollback process.\u003c\/p\u003e\u003ch3\u003eHow value is measured\u003c\/h3\u003e\u003cp\u003ePrimary measures: Sourcing hours; awarded lane cost. Establish a baseline before changes, then compare a controlled pilot or agreed pre\/post period. Track quality, adoption and attributable benefits alongside labor time and ongoing model costs. Targets and acceptance thresholds are agreed during discovery; financial or operational results are not guaranteed.\u003c\/p\u003e\u003ch3\u003eInvestment and timing\u003c\/h3\u003e\u003cp\u003eEstimated engagement: USD $60,000–$140,000. The listed product price of $60,000 is the starting professional services fee, not a deposit or a guaranteed fixed quote. Indicative delivery: 8-16 weeks after prerequisites are available. Final scope, milestones and commercial terms are agreed in a statement of work. Model\/API usage, cloud infrastructure, hardware, third-party licenses, taxes, major data remediation and ongoing managed support are excluded unless expressly included.\u003c\/p\u003e\u003ch3\u003eGetting started\u003c\/h3\u003e\u003cp\u003eProvide a named process owner, authorized access to representative data, integration documentation and operator time for review. Regulated or sensitive workflows require applicable privacy and security approvals before data access. The first working session confirms the use case, baseline, approval boundaries and the smallest valuable delivery scope.\u003c\/p\u003e","brand":"Corefinery","offers":[{"title":"Default Title","offer_id":68850850791618,"sku":"CF-AI-063","price":60000.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0798\/2101\/3186\/files\/Platinum_pathway_to_what_s_next-2.png?v=1794072948"},{"product_id":"ogistics-and-transportation-warehouse-labor-planner","title":"Logistics: Warehouse labor planner","description":"\u003cp\u003eLabor is misaligned with workload. Corefinery works with your logistics and transportation team to turn this bottleneck into a practical, measurable workflow. The engagement focuses on explain demand deviations and propose staffing scenarios rather than a standalone demonstration.\u003c\/p\u003e\u003ch3\u003eWhat the engagement covers\u003c\/h3\u003e\u003cp\u003eOne production workflow; up to two integrations; one business unit. We map the current process with the people doing the work, identify decision rights and exceptions, and agree which actions require approval. The workflow is designed around existing systems and an accountable business owner.\u003c\/p\u003e\u003ch3\u003eWorkflow and data\u003c\/h3\u003e\u003cp\u003eSource inputs: Client-authorized source-system records; approved operating policies; representative task examples; integration documentation. Explain demand deviations and propose staffing scenarios. Every recommendation should retain its supporting context so an operator can inspect the evidence and correct incomplete or inaccurate output.\u003c\/p\u003e\u003ch3\u003eEngineering approach\u003c\/h3\u003e\u003cp\u003eDeterministic components: Shift rules; certifications; station capacities. 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: Units per labor hour; overtime. 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":68850850824386,"sku":"CF-AI-064","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":"ogistics-and-transportation-customs-documentation-assistant","title":"Logistics: Customs documentation assistant","description":"\u003cp\u003eIncomplete paperwork causes border delays. Corefinery works with your logistics and transportation team to turn this bottleneck into a practical, measurable workflow. The engagement focuses on extract shipment data and flag documentation inconsistencies rather than a standalone demonstration.\u003c\/p\u003e\u003ch3\u003eWhat the engagement covers\u003c\/h3\u003e\u003cp\u003eOne production workflow; up to two integrations; one business unit. We map the current process with the people doing the work, identify decision rights and exceptions, and agree which actions require approval. The workflow is designed around existing systems and an accountable business owner.\u003c\/p\u003e\u003ch3\u003eWorkflow and data\u003c\/h3\u003e\u003cp\u003eSource inputs: Client-authorized source-system records; approved operating policies; representative task examples; integration documentation. Extract shipment data and flag documentation inconsistencies. 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 fields; approved classification references; broker 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: Documentation time; correction rate. Establish a baseline before changes, then compare a controlled pilot or agreed pre\/post period. Track quality, adoption and attributable benefits alongside labor time and ongoing model costs. Targets and acceptance thresholds are agreed during discovery; financial or operational results are not guaranteed.\u003c\/p\u003e\u003ch3\u003eInvestment and timing\u003c\/h3\u003e\u003cp\u003eEstimated engagement: USD $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":68850852757698,"sku":"CF-AI-065","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":"ogistics-and-transportation-fleet-maintenance-coordinator","title":"Logistics: Fleet maintenance coordinator","description":"\u003cp\u003eMaintenance disrupts route availability. Corefinery works with your logistics and transportation team to turn this bottleneck into a practical, measurable workflow. The engagement focuses on summarize service history and suggest maintenance priorities rather than a standalone demonstration.\u003c\/p\u003e\u003ch3\u003eWhat the engagement covers\u003c\/h3\u003e\u003cp\u003eOne production workflow; up to two integrations; one business unit. We map the current process with the people doing the work, identify decision rights and exceptions, and agree which actions require approval. The workflow is designed around existing systems and an accountable business owner.\u003c\/p\u003e\u003ch3\u003eWorkflow and data\u003c\/h3\u003e\u003cp\u003eSource inputs: Client-authorized source-system records; approved operating policies; representative task examples; integration documentation. Summarize service history and suggest maintenance priorities. Every recommendation should retain its supporting context so an operator can inspect the evidence and correct incomplete or inaccurate output.\u003c\/p\u003e\u003ch3\u003eEngineering approach\u003c\/h3\u003e\u003cp\u003eDeterministic components: Service intervals; vehicle restrictions; technician availability. These rules control calculations, eligibility and permitted actions. Non-deterministic AI handles interpretation, retrieval and draft recommendations; it does not replace the policy engine. Consequential actions require an authorized human decision.\u003c\/p\u003e\u003cp\u003eModel selection compares task quality, latency, privacy requirements and cost. Claude or OpenAI can support complex language tasks; a specialized small language model can handle narrower classification or retrieval when it meets the same evaluation criteria. Adaptation, quantization and private or edge deployment are considered only when supported by data rights, infrastructure and measured performance. Hosted vendor models remain subject to vendor deployment options and terms.\u003c\/p\u003e\u003ch3\u003eDeliverables\u003c\/h3\u003e\u003cul\u003e\n\u003cli\u003eCurrent-state workflow map, baseline metrics and prioritized requirements\u003c\/li\u003e\n\u003cli\u003eArchitecture, access boundaries, integration design and exception-handling plan\u003c\/li\u003e\n\u003cli\u003eRepresentative evaluation cases and an agreed acceptance scorecard\u003c\/li\u003e\n\u003cli\u003eWorking workflow software, scoped integrations and operator review queue\u003c\/li\u003e\n\u003cli\u003eMonitoring, audit events, deployment runbook, team training and source-code handover\u003c\/li\u003e\n\u003c\/ul\u003e\u003ch3\u003eDelivery and acceptance\u003c\/h3\u003e\u003cp\u003eForward deployed engineers work alongside your operators, with an AI engineer, integration engineer and shared quality and security support. The team reviews progress weekly, tests representative normal and exception cases, and documents limitations. Release follows owner acceptance, access-control testing and a demonstrated pause or rollback process.\u003c\/p\u003e\u003ch3\u003eHow value is measured\u003c\/h3\u003e\u003cp\u003ePrimary measures: Vehicle availability; planning time. Establish a baseline before changes, then compare a controlled pilot or agreed pre\/post period. Track quality, adoption and attributable benefits alongside labor time and ongoing model costs. Targets and acceptance thresholds are agreed during discovery; financial or operational results are not guaranteed.\u003c\/p\u003e\u003ch3\u003eInvestment and timing\u003c\/h3\u003e\u003cp\u003eEstimated engagement: USD $100,000–$250,000. The listed product price of $100,000 is the starting professional services fee, not a deposit or a guaranteed fixed quote. Indicative delivery: 8-16 weeks after prerequisites are available. Final scope, milestones and commercial terms are agreed in a statement of work. Model\/API usage, cloud infrastructure, hardware, third-party licenses, taxes, major data remediation and ongoing managed support are excluded unless expressly included.\u003c\/p\u003e\u003ch3\u003eGetting started\u003c\/h3\u003e\u003cp\u003eProvide a named process owner, authorized access to representative data, integration documentation and operator time for review. Regulated or sensitive workflows require applicable privacy and security approvals before data access. The first working session confirms the use case, baseline, approval boundaries and the smallest valuable delivery scope.\u003c\/p\u003e","brand":"Corefinery","offers":[{"title":"Default Title","offer_id":68850853773506,"sku":"CF-AI-066","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":"ogistics-and-transportation-delivery-capacity-optimizer","title":"Logistics: Delivery capacity optimizer","description":"\u003cp\u003eRoute changes create avoidable cost. Corefinery works with your logistics and transportation team to turn this bottleneck into a practical, measurable workflow. The engagement focuses on explain route exceptions and propose alternatives 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 route exceptions and propose alternatives. 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: Vehicle capacity; time windows; driving 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: Cost per stop; on-time completion. Establish a baseline before changes, then compare a controlled pilot or agreed pre\/post period. Track quality, adoption and attributable benefits alongside labor time and ongoing model costs. Targets and acceptance thresholds are agreed during discovery; financial or operational results are not guaranteed.\u003c\/p\u003e\u003ch3\u003eInvestment and timing\u003c\/h3\u003e\u003cp\u003eEstimated engagement: USD $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":68850854985922,"sku":"CF-AI-067","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":"ogistics-and-transportation-freight-invoice-recovery","title":"Logistics: Freight invoice recovery","description":"\u003cp\u003eBilling mismatches erode margin. Corefinery works with your logistics and transportation team to turn this bottleneck into a practical, measurable workflow. The engagement focuses on interpret accessorial notes and draft evidence-linked disputes rather than a standalone demonstration.\u003c\/p\u003e\u003ch3\u003eWhat the engagement covers\u003c\/h3\u003e\u003cp\u003eOne production workflow; up to two integrations; one business unit. We map the current process with the people doing the work, identify decision rights and exceptions, and agree which actions require approval. The workflow is designed around existing systems and an accountable business owner.\u003c\/p\u003e\u003ch3\u003eWorkflow and data\u003c\/h3\u003e\u003cp\u003eSource inputs: Client-authorized source-system records; approved operating policies; representative task examples; integration documentation. Interpret accessorial notes and draft evidence-linked disputes. 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: Contract rate calculations; duplicate checks; dispute 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: Recovered overcharges; audit hours. Establish a baseline before changes, then compare a controlled pilot or agreed pre\/post period. Track quality, adoption and attributable benefits alongside labor time and ongoing model costs. Targets and acceptance thresholds are agreed during discovery; financial or operational results are not guaranteed.\u003c\/p\u003e\u003ch3\u003eInvestment and timing\u003c\/h3\u003e\u003cp\u003eEstimated engagement: USD $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":68850856427714,"sku":"CF-AI-068","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":"ogistics-and-transportation-edge-slm-dispatch-assistant","title":"Logistics: Edge SLM dispatch assistant","description":"\u003cp\u003eDispatch teams need low-latency intent handling. Corefinery works with your logistics and transportation team to turn this bottleneck into a practical, measurable workflow. The engagement focuses on classify driver messages and retrieve operating procedures rather than a standalone demonstration.\u003c\/p\u003e\u003ch3\u003eWhat the engagement covers\u003c\/h3\u003e\u003cp\u003eOne production workflow; up to two integrations; one business unit. We map the current process with the people doing the work, identify decision rights and exceptions, and agree which actions require approval. The workflow is designed around existing systems and an accountable business owner.\u003c\/p\u003e\u003ch3\u003eWorkflow and data\u003c\/h3\u003e\u003cp\u003eSource inputs: Client-authorized source-system records; approved operating policies; representative task examples; integration documentation. Classify driver messages and retrieve operating procedures. Every recommendation should retain its supporting context so an operator can inspect the evidence and correct incomplete or inaccurate output.\u003c\/p\u003e\u003ch3\u003eEngineering approach\u003c\/h3\u003e\u003cp\u003eDeterministic components: Driver identity; action permissions; dispatch release gate. These rules control calculations, eligibility and permitted actions. Non-deterministic AI handles interpretation, retrieval and draft recommendations; it does not replace the policy engine. Consequential actions require an authorized human decision.\u003c\/p\u003e\u003cp\u003eModel selection compares task quality, latency, privacy requirements and cost. Claude or OpenAI can support complex language tasks; a specialized small language model can handle narrower classification or retrieval when it meets the same evaluation criteria. Adaptation, quantization and private or edge deployment are considered only when supported by data rights, infrastructure and measured performance. Hosted vendor models remain subject to vendor deployment options and terms.\u003c\/p\u003e\u003ch3\u003eDeliverables\u003c\/h3\u003e\u003cul\u003e\n\u003cli\u003eCurrent-state workflow map, baseline metrics and prioritized requirements\u003c\/li\u003e\n\u003cli\u003eArchitecture, access boundaries, integration design and exception-handling plan\u003c\/li\u003e\n\u003cli\u003eRepresentative evaluation cases and an agreed acceptance scorecard\u003c\/li\u003e\n\u003cli\u003eWorking workflow software, scoped integrations and operator review queue\u003c\/li\u003e\n\u003cli\u003eMonitoring, audit events, deployment runbook, team training and source-code handover\u003c\/li\u003e\n\u003c\/ul\u003e\u003ch3\u003eDelivery and acceptance\u003c\/h3\u003e\u003cp\u003eForward deployed engineers work alongside your operators, with an AI engineer, integration engineer and shared quality and security support. The team reviews progress weekly, tests representative normal and exception cases, and documents limitations. Release follows owner acceptance, access-control testing and a demonstrated pause or rollback process.\u003c\/p\u003e\u003ch3\u003eHow value is measured\u003c\/h3\u003e\u003cp\u003ePrimary measures: Message routing time; classification precision. Establish a baseline before changes, then compare a controlled pilot or agreed pre\/post period. Track quality, adoption and attributable benefits alongside labor time and ongoing model costs. Targets and acceptance thresholds are agreed during discovery; financial or operational results are not guaranteed.\u003c\/p\u003e\u003ch3\u003eInvestment and timing\u003c\/h3\u003e\u003cp\u003eEstimated engagement: USD $175,000–$400,000. The listed product price of $175,000 is the starting professional services fee, not a deposit or a guaranteed fixed quote. Indicative delivery: 8-16 weeks after prerequisites are available. Final scope, milestones and commercial terms are agreed in a statement of work. Model\/API usage, cloud infrastructure, hardware, third-party licenses, taxes, major data remediation and ongoing managed support are excluded unless expressly included.\u003c\/p\u003e\u003ch3\u003eGetting started\u003c\/h3\u003e\u003cp\u003eProvide a named process owner, authorized access to representative data, integration documentation and operator time for review. Regulated or sensitive workflows require applicable privacy and security approvals before data access. The first working session confirms the use case, baseline, approval boundaries and the smallest valuable delivery scope.\u003c\/p\u003e","brand":"Corefinery","offers":[{"title":"Default Title","offer_id":68850856755394,"sku":"CF-AI-069","price":175000.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0798\/2101\/3186\/files\/Platinum_pathway_to_what_s_next-2.png?v=1794072948"},{"product_id":"ogistics-and-transportation-enterprise-ai-workflow-transformation","title":"Logistics: Enterprise AI workflow transformation","description":"\u003cp\u003eDisconnected workflows limit enterprise value in logistics and transportation. Corefinery works with your logistics and transportation 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":68850856984770,"sku":"CF-AI-070","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":"logistics-port-and-terminal-flow-planner","title":"Logistics: Port and terminal flow planner","description":"\u003cp\u003eCongestion disrupts terminal resource allocation. Corefinery works with your logistics and transportation team to turn this bottleneck into a practical, measurable workflow. The engagement focuses on explain congestion drivers and propose operating scenarios 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: Arrival schedules; yard positions; equipment availability. Explain congestion drivers and propose operating scenarios. Every recommendation should retain its supporting context so an operator can inspect the evidence and correct incomplete or inaccurate output.\u003c\/p\u003e\u003ch3\u003eEngineering approach\u003c\/h3\u003e\u003cp\u003eDeterministic components: Berth capacity; handling limits; labor qualifications. These rules control calculations, eligibility and permitted actions. Non-deterministic AI handles interpretation, retrieval and draft recommendations; it does not replace the policy engine. Consequential actions require an authorized human decision.\u003c\/p\u003e\u003cp\u003eModel selection compares task quality, latency, privacy requirements and cost. Claude or OpenAI can support complex language tasks; a specialized small language model can handle narrower classification or retrieval when it meets the same evaluation criteria. Adaptation, quantization and private or edge deployment are considered only when supported by data rights, infrastructure and measured performance. Hosted vendor models remain subject to vendor deployment options and terms.\u003c\/p\u003e\u003ch3\u003eDeliverables\u003c\/h3\u003e\u003cul\u003e\n\u003cli\u003eCurrent-state workflow map, baseline metrics and prioritized requirements\u003c\/li\u003e\n\u003cli\u003eArchitecture, access boundaries, integration design and exception-handling plan\u003c\/li\u003e\n\u003cli\u003eRepresentative evaluation cases and an agreed acceptance scorecard\u003c\/li\u003e\n\u003cli\u003eWorking workflow software, scoped integrations and operator review queue\u003c\/li\u003e\n\u003cli\u003eMonitoring, audit events, deployment runbook, team training and source-code handover\u003c\/li\u003e\n\u003c\/ul\u003e\u003ch3\u003eDelivery and acceptance\u003c\/h3\u003e\u003cp\u003eForward deployed engineers work alongside your operators, with an AI engineer, integration engineer and shared quality and security support. The team reviews progress weekly, tests representative normal and exception cases, and documents limitations. Release follows owner acceptance, access-control testing and a demonstrated pause or rollback process.\u003c\/p\u003e\u003ch3\u003eHow value is measured\u003c\/h3\u003e\u003cp\u003ePrimary measures: Dwell time; planning hours. Establish a baseline before changes, then compare a controlled pilot or agreed pre\/post period. Track quality, adoption and attributable benefits alongside labor time and ongoing model costs. Targets and acceptance thresholds are agreed during discovery; financial or operational results are not guaranteed.\u003c\/p\u003e\u003ch3\u003eInvestment and timing\u003c\/h3\u003e\u003cp\u003eEstimated engagement: USD $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":68861988700354,"sku":"CF-AI-161","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":"logistics-cold-chain-exception-intelligence","title":"Logistics: Cold chain exception intelligence","description":"\u003cp\u003eTemperature exceptions require rapid evidence review. Corefinery works with your logistics and transportation team to turn this bottleneck into a practical, measurable workflow. The engagement focuses on summarize excursion evidence and draft investigation 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: Authorized sensor logs; shipment events; product requirements. Summarize excursion evidence and draft investigation 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: Temperature thresholds; chain of custody; release approval. These rules control calculations, eligibility and permitted actions. Non-deterministic AI handles interpretation, retrieval and draft recommendations; it does not replace the policy engine. Consequential actions require an authorized human decision.\u003c\/p\u003e\u003cp\u003eModel selection compares task quality, latency, privacy requirements and cost. Claude or OpenAI can support complex language tasks; a specialized small language model can handle narrower classification or retrieval when it meets the same evaluation criteria. Adaptation, quantization and private or edge deployment are considered only when supported by data rights, infrastructure and measured performance. Hosted vendor models remain subject to vendor deployment options and terms.\u003c\/p\u003e\u003ch3\u003eDeliverables\u003c\/h3\u003e\u003cul\u003e\n\u003cli\u003eCurrent-state workflow map, baseline metrics and prioritized requirements\u003c\/li\u003e\n\u003cli\u003eArchitecture, access boundaries, integration design and exception-handling plan\u003c\/li\u003e\n\u003cli\u003eRepresentative evaluation cases and an agreed acceptance scorecard\u003c\/li\u003e\n\u003cli\u003eWorking workflow software, scoped integrations and operator review queue\u003c\/li\u003e\n\u003cli\u003eMonitoring, audit events, deployment runbook, team training and source-code handover\u003c\/li\u003e\n\u003c\/ul\u003e\u003ch3\u003eDelivery and acceptance\u003c\/h3\u003e\u003cp\u003eForward deployed engineers work alongside your operators, with an AI engineer, integration engineer and shared quality and security support. The team reviews progress weekly, tests representative normal and exception cases, and documents limitations. Release follows owner acceptance, access-control testing and a demonstrated pause or rollback process.\u003c\/p\u003e\u003ch3\u003eHow value is measured\u003c\/h3\u003e\u003cp\u003ePrimary measures: Review time; evidence completeness. Establish a baseline before changes, then compare a controlled pilot or agreed pre\/post period. Track quality, adoption and attributable benefits alongside labor time and ongoing model costs. Targets and acceptance thresholds are agreed during discovery; financial or operational results are not guaranteed.\u003c\/p\u003e\u003ch3\u003eInvestment and timing\u003c\/h3\u003e\u003cp\u003eEstimated engagement: USD $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":68861989585090,"sku":"CF-AI-162","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":"logistics-carrier-onboarding-workbench","title":"Logistics: Carrier onboarding workbench","description":"\u003cp\u003eCarrier qualification depends on manual packets. Corefinery works with your logistics and transportation team to turn this bottleneck into a practical, measurable workflow. The engagement focuses on extract carrier facts and request missing evidence 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: Carrier documents; insurance records; service profiles. Extract carrier facts and request missing evidence. Every recommendation should retain its supporting context so an operator can inspect the evidence and correct incomplete or inaccurate output.\u003c\/p\u003e\u003ch3\u003eEngineering approach\u003c\/h3\u003e\u003cp\u003eDeterministic components: Required credentials; expiry dates; approval sequence. These rules control calculations, eligibility and permitted actions. Non-deterministic AI handles interpretation, retrieval and draft recommendations; it does not replace the policy engine. Consequential actions require an authorized human decision.\u003c\/p\u003e\u003cp\u003eModel selection compares task quality, latency, privacy requirements and cost. Claude or OpenAI can support complex language tasks; a specialized small language model can handle narrower classification or retrieval when it meets the same evaluation criteria. Adaptation, quantization and private or edge deployment are considered only when supported by data rights, infrastructure and measured performance. Hosted vendor models remain subject to vendor deployment options and terms.\u003c\/p\u003e\u003ch3\u003eDeliverables\u003c\/h3\u003e\u003cul\u003e\n\u003cli\u003eCurrent-state workflow map, baseline metrics and prioritized requirements\u003c\/li\u003e\n\u003cli\u003eArchitecture, access boundaries, integration design and exception-handling plan\u003c\/li\u003e\n\u003cli\u003eRepresentative evaluation cases and an agreed acceptance scorecard\u003c\/li\u003e\n\u003cli\u003eWorking workflow software, scoped integrations and operator review queue\u003c\/li\u003e\n\u003cli\u003eMonitoring, audit events, deployment runbook, team training and source-code handover\u003c\/li\u003e\n\u003c\/ul\u003e\u003ch3\u003eDelivery and acceptance\u003c\/h3\u003e\u003cp\u003eForward deployed engineers work alongside your operators, with an AI engineer, integration engineer and shared quality and security support. The team reviews progress weekly, tests representative normal and exception cases, and documents limitations. Release follows owner acceptance, access-control testing and a demonstrated pause or rollback process.\u003c\/p\u003e\u003ch3\u003eHow value is measured\u003c\/h3\u003e\u003cp\u003ePrimary measures: Onboarding time; packet completeness. Establish a baseline before changes, then compare a controlled pilot or agreed pre\/post period. Track quality, adoption and attributable benefits alongside labor time and ongoing model costs. Targets and acceptance thresholds are agreed during discovery; financial or operational results are not guaranteed.\u003c\/p\u003e\u003ch3\u003eInvestment and timing\u003c\/h3\u003e\u003cp\u003eEstimated engagement: USD $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":68861990240450,"sku":"CF-AI-163","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":"logistics-transportation-demand-scenario-studio","title":"Logistics: Transportation demand scenario studio","description":"\u003cp\u003eTeams lack reliable context for capacity planning. Corefinery works with your logistics and transportation team to turn this bottleneck into a practical, measurable workflow. The engagement focuses on explain demand changes and propose capacity scenarios 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: Historical volumes; bookings; commercial forecasts. Explain demand changes and propose capacity scenarios. Every recommendation should retain its supporting context so an operator can inspect the evidence and correct incomplete or inaccurate output.\u003c\/p\u003e\u003ch3\u003eEngineering approach\u003c\/h3\u003e\u003cp\u003eDeterministic components: Capacity bounds; calendar constraints; scenario formulas. 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: Forecast review hours; capacity utilization. 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":68861990404290,"sku":"CF-AI-164","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":"logistics-cargo-claims-evidence-assembler","title":"Logistics: Cargo claims evidence assembler","description":"\u003cp\u003eClaims recovery suffers from incomplete documentation. Corefinery works with your logistics and transportation team to turn this bottleneck into a practical, measurable workflow. The engagement focuses on assemble timelines and draft evidence-linked claim packets 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: Shipment records; damage reports; carrier contracts. Assemble timelines and draft evidence-linked claim packets. Every recommendation should retain its supporting context so an operator can inspect the evidence and correct incomplete or inaccurate output.\u003c\/p\u003e\u003ch3\u003eEngineering approach\u003c\/h3\u003e\u003cp\u003eDeterministic components: Claim deadlines; proof requirements; recovery 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: Packet preparation; recovery cycle 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: 8-20 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":68861991747778,"sku":"CF-AI-165","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":"logistics-driver-onboarding-learning-assistant","title":"Logistics: Driver onboarding learning assistant","description":"\u003cp\u003eNew drivers need role-specific operating knowledge. Corefinery works with your logistics and transportation team to turn this bottleneck into a practical, measurable workflow. The engagement focuses on generate guided practice and retrieve operating guidance 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: Approved driver guides; training content; onboarding tasks. Generate guided practice and retrieve operating guidance. Every recommendation should retain its supporting context so an operator can inspect the evidence and correct incomplete or inaccurate output.\u003c\/p\u003e\u003ch3\u003eEngineering approach\u003c\/h3\u003e\u003cp\u003eDeterministic components: License checks; training completion; supervisor sign-off. These rules control calculations, eligibility and permitted actions. Non-deterministic AI handles interpretation, retrieval and draft recommendations; it does not replace the policy engine. Consequential actions require an authorized human decision.\u003c\/p\u003e\u003cp\u003eModel selection compares task quality, latency, privacy requirements and cost. Claude or OpenAI can support complex language tasks; a specialized small language model can handle narrower classification or retrieval when it meets the same evaluation criteria. Adaptation, quantization and private or edge deployment are considered only when supported by data rights, infrastructure and measured performance. Hosted vendor models remain subject to vendor deployment options and terms.\u003c\/p\u003e\u003ch3\u003eDeliverables\u003c\/h3\u003e\u003cul\u003e\n\u003cli\u003eCurrent-state workflow map, baseline metrics and prioritized requirements\u003c\/li\u003e\n\u003cli\u003eArchitecture, access boundaries, integration design and exception-handling plan\u003c\/li\u003e\n\u003cli\u003eRepresentative evaluation cases and an agreed acceptance scorecard\u003c\/li\u003e\n\u003cli\u003eWorking workflow software, scoped integrations and operator review queue\u003c\/li\u003e\n\u003cli\u003eMonitoring, audit events, deployment runbook, team training and source-code handover\u003c\/li\u003e\n\u003c\/ul\u003e\u003ch3\u003eDelivery and acceptance\u003c\/h3\u003e\u003cp\u003eForward deployed engineers work alongside your operators, with an AI engineer, integration engineer and shared quality and security support. The team reviews progress weekly, tests representative normal and exception cases, and documents limitations. Release follows owner acceptance, access-control testing and a demonstrated pause or rollback process.\u003c\/p\u003e\u003ch3\u003eHow value is measured\u003c\/h3\u003e\u003cp\u003ePrimary measures: Time to readiness; assessment scores. 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":68861991813314,"sku":"CF-AI-166","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":"logistics-reverse-logistics-recovery-engine","title":"Logistics: Reverse logistics recovery engine","description":"\u003cp\u003eReturned goods move through inconsistent disposition paths. Corefinery works with your logistics and transportation team to turn this bottleneck into a practical, measurable workflow. The engagement focuses on summarize item condition and recommend review paths 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: Return records; inspection notes; resale channels. Summarize item condition and recommend review paths. 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: Disposition eligibility; recovery calculations; approval limits. These rules control calculations, eligibility and permitted actions. Non-deterministic AI handles interpretation, retrieval and draft recommendations; it does not replace the policy engine. Consequential actions require an authorized human decision.\u003c\/p\u003e\u003cp\u003eModel selection compares task quality, latency, privacy requirements and cost. Claude or OpenAI can support complex language tasks; a specialized small language model can handle narrower classification or retrieval when it meets the same evaluation criteria. Adaptation, quantization and private or edge deployment are considered only when supported by data rights, infrastructure and measured performance. Hosted vendor models remain subject to vendor deployment options and terms.\u003c\/p\u003e\u003ch3\u003eDeliverables\u003c\/h3\u003e\u003cul\u003e\n\u003cli\u003eCurrent-state workflow map, baseline metrics and prioritized requirements\u003c\/li\u003e\n\u003cli\u003eArchitecture, access boundaries, integration design and exception-handling plan\u003c\/li\u003e\n\u003cli\u003eRepresentative evaluation cases and an agreed acceptance scorecard\u003c\/li\u003e\n\u003cli\u003eWorking workflow software, scoped integrations and operator review queue\u003c\/li\u003e\n\u003cli\u003eMonitoring, audit events, deployment runbook, team training and source-code handover\u003c\/li\u003e\n\u003c\/ul\u003e\u003ch3\u003eDelivery and acceptance\u003c\/h3\u003e\u003cp\u003eForward deployed engineers work alongside your operators, with an AI engineer, integration engineer and shared quality and security support. The team reviews progress weekly, tests representative normal and exception cases, and documents limitations. Release follows owner acceptance, access-control testing and a demonstrated pause or rollback process.\u003c\/p\u003e\u003ch3\u003eHow value is measured\u003c\/h3\u003e\u003cp\u003ePrimary measures: Recovery value; disposition 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 $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":68861992468674,"sku":"CF-AI-167","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":"logistics-supply-chain-carbon-evidence-desk","title":"Logistics: Supply chain carbon evidence desk","description":"\u003cp\u003eEmissions reporting relies on inconsistent shipment inputs. Corefinery works with your logistics and transportation team to turn this bottleneck into a practical, measurable workflow. The engagement focuses on identify missing inputs and draft methodology explanations 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: Shipment distances; fuel records; approved factors. Identify missing inputs and draft methodology explanations. 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: Unit conversion; factor version; audit trail. 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: Data completeness; reporting hours. Establish a baseline before changes, then compare a controlled pilot or agreed pre\/post period. Track quality, adoption and attributable benefits alongside labor time and ongoing model costs. Targets and acceptance thresholds are agreed during discovery; financial or operational results are not guaranteed.\u003c\/p\u003e\u003ch3\u003eInvestment and timing\u003c\/h3\u003e\u003cp\u003eEstimated engagement: USD $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":68861993156802,"sku":"CF-AI-168","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":"logistics-claude-enterprise-workflow-deployment","title":"Logistics: Claude enterprise workflow deployment","description":"\u003cp\u003eTeams need a governed path from Claude experimentation to one usable industry workflow. Corefinery works with your logistics and transportation 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":68861993812162,"sku":"CF-AI-169","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":"logistics-openai-production-agent-engineering","title":"Logistics: OpenAI production agent engineering","description":"\u003cp\u003ePromising OpenAI prototypes lack production integration and accountable operating controls. Corefinery works with your logistics and transportation 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":68861994467522,"sku":"CF-AI-170","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\/logistics-transportation.oembed","provider":"Corefinery","version":"1.0","type":"link"}