{"title":"Retail \u0026 consumer goods","description":"\u003cp\u003e\u003cspan\u003eTurn \u003c\/span\u003e\u003cspan\u003ecustomer \u003c\/span\u003e\u003cspan\u003eintent \u003c\/span\u003e\u003cspan\u003einto \u003c\/span\u003e\u003cspan\u003esales \u003c\/span\u003e\u003cspan\u003eand \u003c\/span\u003e\u003cspan\u003edemand \u003c\/span\u003e\u003cspan\u003esignals \u003c\/span\u003e\u003cspan\u003einto \u003c\/span\u003e\u003cspan\u003esmarter \u003c\/span\u003e\u003cspan\u003einventory \u003c\/span\u003e\u003cspan\u003edecisions. \u003c\/span\u003e\u003cspan\u003eExplore \u003c\/span\u003e\u003cspan\u003eintelligent \u003c\/span\u003e\u003cspan\u003ediscovery, \u003c\/span\u003e\u003cspan\u003emerchandising, \u003c\/span\u003e\u003cspan\u003eengagement \u003c\/span\u003e\u003cspan\u003eand \u003c\/span\u003e\u003cspan\u003ecommerce \u003c\/span\u003e\u003cspan\u003eoperations.\u003c\/span\u003e\u003c\/p\u003e","products":[{"product_id":"etail-and-consumer-goods-ai-value-and-deployment-assessment","title":"Retail: AI value and deployment assessment","description":"\u003cp\u003eIdentify where AI can create practical value in retail and consumer goods 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":68850824872130,"sku":"CF-AI-031","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":"etail-and-consumer-goods-agentic-product-discovery","title":"Retail: Agentic product discovery","description":"\u003cp\u003eCustomers struggle to find suitable products. Corefinery works with your retail and consumer goods team to turn this bottleneck into a practical, measurable workflow. The engagement focuses on interpret shopper intent and explain product matches rather than a standalone demonstration.\u003c\/p\u003e\u003ch3\u003eWhat the engagement covers\u003c\/h3\u003e\u003cp\u003eOne production workflow; up to two integrations; one business unit. We map the current process with the people doing the work, identify decision rights and exceptions, and agree which actions require approval. The workflow is designed around existing systems and an accountable business owner.\u003c\/p\u003e\u003ch3\u003eWorkflow and data\u003c\/h3\u003e\u003cp\u003eSource inputs: Client-authorized source-system records; approved operating policies; representative task examples; integration documentation. Interpret shopper intent and explain product matches. Every recommendation should retain its supporting context so an operator can inspect the evidence and correct incomplete or inaccurate output.\u003c\/p\u003e\u003ch3\u003eEngineering approach\u003c\/h3\u003e\u003cp\u003eDeterministic components: Stock checks; price rules; attribute filters. These rules control calculations, eligibility and permitted actions. Non-deterministic AI handles interpretation, retrieval and draft recommendations; it does not replace the policy engine. Consequential actions require an authorized human decision.\u003c\/p\u003e\u003cp\u003eModel selection compares task quality, latency, privacy requirements and cost. Claude or OpenAI can support complex language tasks; a specialized small language model can handle narrower classification or retrieval when it meets the same evaluation criteria. Adaptation, quantization and private or edge deployment are considered only when supported by data rights, infrastructure and measured performance. Hosted vendor models remain subject to vendor deployment options and terms.\u003c\/p\u003e\u003ch3\u003eDeliverables\u003c\/h3\u003e\u003cul\u003e\n\u003cli\u003eCurrent-state workflow map, baseline metrics and prioritized requirements\u003c\/li\u003e\n\u003cli\u003eArchitecture, access boundaries, integration design and exception-handling plan\u003c\/li\u003e\n\u003cli\u003eRepresentative evaluation cases and an agreed acceptance scorecard\u003c\/li\u003e\n\u003cli\u003eWorking workflow software, scoped integrations and operator review queue\u003c\/li\u003e\n\u003cli\u003eMonitoring, audit events, deployment runbook, team training and source-code handover\u003c\/li\u003e\n\u003c\/ul\u003e\u003ch3\u003eDelivery and acceptance\u003c\/h3\u003e\u003cp\u003eForward deployed engineers work alongside your operators, with an AI engineer, integration engineer and shared quality and security support. The team reviews progress weekly, tests representative normal and exception cases, and documents limitations. Release follows owner acceptance, access-control testing and a demonstrated pause or rollback process.\u003c\/p\u003e\u003ch3\u003eHow value is measured\u003c\/h3\u003e\u003cp\u003ePrimary measures: Search-to-cart rate; assisted conversion. Establish a baseline before changes, then compare a controlled pilot or agreed pre\/post period. Track quality, adoption and attributable benefits alongside labor time and ongoing model costs. Targets and acceptance thresholds are agreed during discovery; financial or operational results are not guaranteed.\u003c\/p\u003e\u003ch3\u003eInvestment and timing\u003c\/h3\u003e\u003cp\u003eEstimated engagement: USD $40,000–$90,000. The listed product price of $40,000 is the starting professional services fee, not a deposit or a guaranteed fixed quote. Indicative delivery: 8-16 weeks after prerequisites are available. Final scope, milestones and commercial terms are agreed in a statement of work. Model\/API usage, cloud infrastructure, hardware, third-party licenses, taxes, major data remediation and ongoing managed support are excluded unless expressly included.\u003c\/p\u003e\u003ch3\u003eGetting started\u003c\/h3\u003e\u003cp\u003eProvide a named process owner, authorized access to representative data, integration documentation and operator time for review. Regulated or sensitive workflows require applicable privacy and security approvals before data access. The first working session confirms the use case, baseline, approval boundaries and the smallest valuable delivery scope.\u003c\/p\u003e","brand":"Corefinery","offers":[{"title":"Default Title","offer_id":68850824904898,"sku":"CF-AI-032","price":40000.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0798\/2101\/3186\/files\/Platinum_pathway_to_what_s_next-2.png?v=1794072948"},{"product_id":"etail-and-consumer-goods-merchandising-intelligence-studio","title":"Retail: Merchandising intelligence studio","description":"\u003cp\u003eAssortment decisions lack unified signals. Corefinery works with your retail and consumer goods team to turn this bottleneck into a practical, measurable workflow. The engagement focuses on summarize demand signals and propose assortment actions rather than a standalone demonstration.\u003c\/p\u003e\u003ch3\u003eWhat the engagement covers\u003c\/h3\u003e\u003cp\u003eOne production workflow; up to two integrations; one business unit. We map the current process with the people doing the work, identify decision rights and exceptions, and agree which actions require approval. The workflow is designed around existing systems and an accountable business owner.\u003c\/p\u003e\u003ch3\u003eWorkflow and data\u003c\/h3\u003e\u003cp\u003eSource inputs: Client-authorized source-system records; approved operating policies; representative task examples; integration documentation. Summarize demand signals and propose assortment actions. Every recommendation should retain its supporting context so an operator can inspect the evidence and correct incomplete or inaccurate output.\u003c\/p\u003e\u003ch3\u003eEngineering approach\u003c\/h3\u003e\u003cp\u003eDeterministic components: Margin floors; inventory limits; category rules. These rules control calculations, eligibility and permitted actions. Non-deterministic AI handles interpretation, retrieval and draft recommendations; it does not replace the policy engine. Consequential actions require an authorized human decision.\u003c\/p\u003e\u003cp\u003eModel selection compares task quality, latency, privacy requirements and cost. Claude or OpenAI can support complex language tasks; a specialized small language model can handle narrower classification or retrieval when it meets the same evaluation criteria. Adaptation, quantization and private or edge deployment are considered only when supported by data rights, infrastructure and measured performance. Hosted vendor models remain subject to vendor deployment options and terms.\u003c\/p\u003e\u003ch3\u003eDeliverables\u003c\/h3\u003e\u003cul\u003e\n\u003cli\u003eCurrent-state workflow map, baseline metrics and prioritized requirements\u003c\/li\u003e\n\u003cli\u003eArchitecture, access boundaries, integration design and exception-handling plan\u003c\/li\u003e\n\u003cli\u003eRepresentative evaluation cases and an agreed acceptance scorecard\u003c\/li\u003e\n\u003cli\u003eWorking workflow software, scoped integrations and operator review queue\u003c\/li\u003e\n\u003cli\u003eMonitoring, audit events, deployment runbook, team training and source-code handover\u003c\/li\u003e\n\u003c\/ul\u003e\u003ch3\u003eDelivery and acceptance\u003c\/h3\u003e\u003cp\u003eForward deployed engineers work alongside your operators, with an AI engineer, integration engineer and shared quality and security support. The team reviews progress weekly, tests representative normal and exception cases, and documents limitations. Release follows owner acceptance, access-control testing and a demonstrated pause or rollback process.\u003c\/p\u003e\u003ch3\u003eHow value is measured\u003c\/h3\u003e\u003cp\u003ePrimary measures: Sell-through; aged inventory. Establish a baseline before changes, then compare a controlled pilot or agreed pre\/post period. Track quality, adoption and attributable benefits alongside labor time and ongoing model costs. Targets and acceptance thresholds are agreed during discovery; financial or operational results are not guaranteed.\u003c\/p\u003e\u003ch3\u003eInvestment and timing\u003c\/h3\u003e\u003cp\u003eEstimated engagement: USD $60,000–$140,000. The listed product price of $60,000 is the starting professional services fee, not a deposit or a guaranteed fixed quote. Indicative delivery: 8-16 weeks after prerequisites are available. Final scope, milestones and commercial terms are agreed in a statement of work. Model\/API usage, cloud infrastructure, hardware, third-party licenses, taxes, major data remediation and ongoing managed support are excluded unless expressly included.\u003c\/p\u003e\u003ch3\u003eGetting started\u003c\/h3\u003e\u003cp\u003eProvide a named process owner, authorized access to representative data, integration documentation and operator time for review. Regulated or sensitive workflows require applicable privacy and security approvals before data access. The first working session confirms the use case, baseline, approval boundaries and the smallest valuable delivery scope.\u003c\/p\u003e","brand":"Corefinery","offers":[{"title":"Default Title","offer_id":68850837389506,"sku":"CF-AI-033","price":60000.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0798\/2101\/3186\/files\/Platinum_pathway_to_what_s_next-2.png?v=1794072948"},{"product_id":"etail-and-consumer-goods-inventory-allocation-optimizer","title":"Retail: Inventory allocation optimizer","description":"\u003cp\u003eStock is mismatched across locations. Corefinery works with your retail and consumer goods team to turn this bottleneck into a practical, measurable workflow. The engagement focuses on explain demand exceptions and propose allocation scenarios rather than a standalone demonstration.\u003c\/p\u003e\u003ch3\u003eWhat the engagement covers\u003c\/h3\u003e\u003cp\u003eOne production workflow; up to two integrations; one business unit. We map the current process with the people doing the work, identify decision rights and exceptions, and agree which actions require approval. The workflow is designed around existing systems and an accountable business owner.\u003c\/p\u003e\u003ch3\u003eWorkflow and data\u003c\/h3\u003e\u003cp\u003eSource inputs: Client-authorized source-system records; approved operating policies; representative task examples; integration documentation. Explain demand exceptions and propose allocation scenarios. Every recommendation should retain its supporting context so an operator can inspect the evidence and correct incomplete or inaccurate output.\u003c\/p\u003e\u003ch3\u003eEngineering approach\u003c\/h3\u003e\u003cp\u003eDeterministic components: Inventory conservation; transfer costs; capacity constraints. These rules control calculations, eligibility and permitted actions. Non-deterministic AI handles interpretation, retrieval and draft recommendations; it does not replace the policy engine. Consequential actions require an authorized human decision.\u003c\/p\u003e\u003cp\u003eModel selection compares task quality, latency, privacy requirements and cost. Claude or OpenAI can support complex language tasks; a specialized small language model can handle narrower classification or retrieval when it meets the same evaluation criteria. Adaptation, quantization and private or edge deployment are considered only when supported by data rights, infrastructure and measured performance. Hosted vendor models remain subject to vendor deployment options and terms.\u003c\/p\u003e\u003ch3\u003eDeliverables\u003c\/h3\u003e\u003cul\u003e\n\u003cli\u003eCurrent-state workflow map, baseline metrics and prioritized requirements\u003c\/li\u003e\n\u003cli\u003eArchitecture, access boundaries, integration design and exception-handling plan\u003c\/li\u003e\n\u003cli\u003eRepresentative evaluation cases and an agreed acceptance scorecard\u003c\/li\u003e\n\u003cli\u003eWorking workflow software, scoped integrations and operator review queue\u003c\/li\u003e\n\u003cli\u003eMonitoring, audit events, deployment runbook, team training and source-code handover\u003c\/li\u003e\n\u003c\/ul\u003e\u003ch3\u003eDelivery and acceptance\u003c\/h3\u003e\u003cp\u003eForward deployed engineers work alongside your operators, with an AI engineer, integration engineer and shared quality and security support. The team reviews progress weekly, tests representative normal and exception cases, and documents limitations. Release follows owner acceptance, access-control testing and a demonstrated pause or rollback process.\u003c\/p\u003e\u003ch3\u003eHow value is measured\u003c\/h3\u003e\u003cp\u003ePrimary measures: Stockout rate; transfer economics. Establish a baseline before changes, then compare a controlled pilot or agreed pre\/post period. Track quality, adoption and attributable benefits alongside labor time and ongoing model costs. Targets and acceptance thresholds are agreed during discovery; financial or operational results are not guaranteed.\u003c\/p\u003e\u003ch3\u003eInvestment and timing\u003c\/h3\u003e\u003cp\u003eEstimated engagement: USD $75,000–$180,000. The listed product price of $75,000 is the starting professional services fee, not a deposit or a guaranteed fixed quote. Indicative delivery: 8-16 weeks after prerequisites are available. Final scope, milestones and commercial terms are agreed in a statement of work. Model\/API usage, cloud infrastructure, hardware, third-party licenses, taxes, major data remediation and ongoing managed support are excluded unless expressly included.\u003c\/p\u003e\u003ch3\u003eGetting started\u003c\/h3\u003e\u003cp\u003eProvide a named process owner, authorized access to representative data, integration documentation and operator time for review. Regulated or sensitive workflows require applicable privacy and security approvals before data access. The first working session confirms the use case, baseline, approval boundaries and the smallest valuable delivery scope.\u003c\/p\u003e","brand":"Corefinery","offers":[{"title":"Default Title","offer_id":68850837422274,"sku":"CF-AI-034","price":75000.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0798\/2101\/3186\/files\/Platinum_pathway_to_what_s_next-2.png?v=1794072948"},{"product_id":"etail-and-consumer-goods-returns-resolution-copilot","title":"Retail: Returns resolution copilot","description":"\u003cp\u003eReturns consume service capacity. Corefinery works with your retail and consumer goods team to turn this bottleneck into a practical, measurable workflow. The engagement focuses on summarize issues and propose policy-grounded resolutions rather than a standalone demonstration.\u003c\/p\u003e\u003ch3\u003eWhat the engagement covers\u003c\/h3\u003e\u003cp\u003eOne production workflow; up to two integrations; one business unit. We map the current process with the people doing the work, identify decision rights and exceptions, and agree which actions require approval. The workflow is designed around existing systems and an accountable business owner.\u003c\/p\u003e\u003ch3\u003eWorkflow and data\u003c\/h3\u003e\u003cp\u003eSource inputs: Client-authorized source-system records; approved operating policies; representative task examples; integration documentation. Summarize issues and propose policy-grounded resolutions. Every recommendation should retain its supporting context so an operator can inspect the evidence and correct incomplete or inaccurate output.\u003c\/p\u003e\u003ch3\u003eEngineering approach\u003c\/h3\u003e\u003cp\u003eDeterministic components: Return window; refund limits; approval gates. These rules control calculations, eligibility and permitted actions. Non-deterministic AI handles interpretation, retrieval and draft recommendations; it does not replace the policy engine. Consequential actions require an authorized human decision.\u003c\/p\u003e\u003cp\u003eModel selection compares task quality, latency, privacy requirements and cost. Claude or OpenAI can support complex language tasks; a specialized small language model can handle narrower classification or retrieval when it meets the same evaluation criteria. Adaptation, quantization and private or edge deployment are considered only when supported by data rights, infrastructure and measured performance. Hosted vendor models remain subject to vendor deployment options and terms.\u003c\/p\u003e\u003ch3\u003eDeliverables\u003c\/h3\u003e\u003cul\u003e\n\u003cli\u003eCurrent-state workflow map, baseline metrics and prioritized requirements\u003c\/li\u003e\n\u003cli\u003eArchitecture, access boundaries, integration design and exception-handling plan\u003c\/li\u003e\n\u003cli\u003eRepresentative evaluation cases and an agreed acceptance scorecard\u003c\/li\u003e\n\u003cli\u003eWorking workflow software, scoped integrations and operator review queue\u003c\/li\u003e\n\u003cli\u003eMonitoring, audit events, deployment runbook, team training and source-code handover\u003c\/li\u003e\n\u003c\/ul\u003e\u003ch3\u003eDelivery and acceptance\u003c\/h3\u003e\u003cp\u003eForward deployed engineers work alongside your operators, with an AI engineer, integration engineer and shared quality and security support. The team reviews progress weekly, tests representative normal and exception cases, and documents limitations. Release follows owner acceptance, access-control testing and a demonstrated pause or rollback process.\u003c\/p\u003e\u003ch3\u003eHow value is measured\u003c\/h3\u003e\u003cp\u003ePrimary measures: Resolution time; repeat contact rate. Establish a baseline before changes, then compare a controlled pilot or agreed pre\/post period. Track quality, adoption and attributable benefits alongside labor time and ongoing model costs. Targets and acceptance thresholds are agreed during discovery; financial or operational results are not guaranteed.\u003c\/p\u003e\u003ch3\u003eInvestment and timing\u003c\/h3\u003e\u003cp\u003eEstimated engagement: USD $90,000–$225,000. The listed product price of $90,000 is the starting professional services fee, not a deposit or a guaranteed fixed quote. Indicative delivery: 8-16 weeks after prerequisites are available. Final scope, milestones and commercial terms are agreed in a statement of work. Model\/API usage, cloud infrastructure, hardware, third-party licenses, taxes, major data remediation and ongoing managed support are excluded unless expressly included.\u003c\/p\u003e\u003ch3\u003eGetting started\u003c\/h3\u003e\u003cp\u003eProvide a named process owner, authorized access to representative data, integration documentation and operator time for review. Regulated or sensitive workflows require applicable privacy and security approvals before data access. The first working session confirms the use case, baseline, approval boundaries and the smallest valuable delivery scope.\u003c\/p\u003e","brand":"Corefinery","offers":[{"title":"Default Title","offer_id":68850837455042,"sku":"CF-AI-035","price":90000.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0798\/2101\/3186\/files\/Platinum_pathway_to_what_s_next-2.png?v=1794072948"},{"product_id":"etail-and-consumer-goods-catalog-enrichment-factory","title":"Retail: Catalog enrichment factory","description":"\u003cp\u003eIncomplete product data weakens discovery. Corefinery works with your retail and consumer goods team to turn this bottleneck into a practical, measurable workflow. The engagement focuses on generate descriptions and normalize attributes from source data rather than a standalone demonstration.\u003c\/p\u003e\u003ch3\u003eWhat the engagement covers\u003c\/h3\u003e\u003cp\u003eOne production workflow; up to two integrations; one business unit. We map the current process with the people doing the work, identify decision rights and exceptions, and agree which actions require approval. The workflow is designed around existing systems and an accountable business owner.\u003c\/p\u003e\u003ch3\u003eWorkflow and data\u003c\/h3\u003e\u003cp\u003eSource inputs: Client-authorized source-system records; approved operating policies; representative task examples; integration documentation. Generate descriptions and normalize attributes from source data. Every recommendation should retain its supporting context so an operator can inspect the evidence and correct incomplete or inaccurate output.\u003c\/p\u003e\u003ch3\u003eEngineering approach\u003c\/h3\u003e\u003cp\u003eDeterministic components: Required attributes; taxonomy checks; prohibited claim rules. These rules control calculations, eligibility and permitted actions. Non-deterministic AI handles interpretation, retrieval and draft recommendations; it does not replace the policy engine. Consequential actions require an authorized human decision.\u003c\/p\u003e\u003cp\u003eModel selection compares task quality, latency, privacy requirements and cost. Claude or OpenAI can support complex language tasks; a specialized small language model can handle narrower classification or retrieval when it meets the same evaluation criteria. Adaptation, quantization and private or edge deployment are considered only when supported by data rights, infrastructure and measured performance. Hosted vendor models remain subject to vendor deployment options and terms.\u003c\/p\u003e\u003ch3\u003eDeliverables\u003c\/h3\u003e\u003cul\u003e\n\u003cli\u003eCurrent-state workflow map, baseline metrics and prioritized requirements\u003c\/li\u003e\n\u003cli\u003eArchitecture, access boundaries, integration design and exception-handling plan\u003c\/li\u003e\n\u003cli\u003eRepresentative evaluation cases and an agreed acceptance scorecard\u003c\/li\u003e\n\u003cli\u003eWorking workflow software, scoped integrations and operator review queue\u003c\/li\u003e\n\u003cli\u003eMonitoring, audit events, deployment runbook, team training and source-code handover\u003c\/li\u003e\n\u003c\/ul\u003e\u003ch3\u003eDelivery and acceptance\u003c\/h3\u003e\u003cp\u003eForward deployed engineers work alongside your operators, with an AI engineer, integration engineer and shared quality and security support. The team reviews progress weekly, tests representative normal and exception cases, and documents limitations. Release follows owner acceptance, access-control testing and a demonstrated pause or rollback process.\u003c\/p\u003e\u003ch3\u003eHow value is measured\u003c\/h3\u003e\u003cp\u003ePrimary measures: Attribute completeness; editing time. Establish a baseline before changes, then compare a controlled pilot or agreed pre\/post period. Track quality, adoption and attributable benefits alongside labor time and ongoing model costs. Targets and acceptance thresholds are agreed during discovery; financial or operational results are not guaranteed.\u003c\/p\u003e\u003ch3\u003eInvestment and timing\u003c\/h3\u003e\u003cp\u003eEstimated engagement: USD $100,000–$250,000. The listed product price of $100,000 is the starting professional services fee, not a deposit or a guaranteed fixed quote. Indicative delivery: 8-16 weeks after prerequisites are available. Final scope, milestones and commercial terms are agreed in a statement of work. Model\/API usage, cloud infrastructure, hardware, third-party licenses, taxes, major data remediation and ongoing managed support are excluded unless expressly included.\u003c\/p\u003e\u003ch3\u003eGetting started\u003c\/h3\u003e\u003cp\u003eProvide a named process owner, authorized access to representative data, integration documentation and operator time for review. Regulated or sensitive workflows require applicable privacy and security approvals before data access. The first working session confirms the use case, baseline, approval boundaries and the smallest valuable delivery scope.\u003c\/p\u003e","brand":"Corefinery","offers":[{"title":"Default Title","offer_id":68850837487810,"sku":"CF-AI-036","price":100000.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0798\/2101\/3186\/files\/Platinum_pathway_to_what_s_next-2.png?v=1794072948"},{"product_id":"etail-and-consumer-goods-member-growth-workbench","title":"Retail: Member growth workbench","description":"\u003cp\u003eGeneric outreach misses individual needs. Corefinery works with your retail and consumer goods team to turn this bottleneck into a practical, measurable workflow. The engagement focuses on draft personalized outreach grounded in purchase history rather than a standalone demonstration.\u003c\/p\u003e\u003ch3\u003eWhat the engagement covers\u003c\/h3\u003e\u003cp\u003eOne production workflow; up to two integrations; one business unit. We map the current process with the people doing the work, identify decision rights and exceptions, and agree which actions require approval. The workflow is designed around existing systems and an accountable business owner.\u003c\/p\u003e\u003ch3\u003eWorkflow and data\u003c\/h3\u003e\u003cp\u003eSource inputs: Client-authorized source-system records; approved operating policies; representative task examples; integration documentation. Draft personalized outreach grounded in purchase history. Every recommendation should retain its supporting context so an operator can inspect the evidence and correct incomplete or inaccurate output.\u003c\/p\u003e\u003ch3\u003eEngineering approach\u003c\/h3\u003e\u003cp\u003eDeterministic components: Consent; frequency caps; offer eligibility. These rules control calculations, eligibility and permitted actions. Non-deterministic AI handles interpretation, retrieval and draft recommendations; it does not replace the policy engine. Consequential actions require an authorized human decision.\u003c\/p\u003e\u003cp\u003eModel selection compares task quality, latency, privacy requirements and cost. Claude or OpenAI can support complex language tasks; a specialized small language model can handle narrower classification or retrieval when it meets the same evaluation criteria. Adaptation, quantization and private or edge deployment are considered only when supported by data rights, infrastructure and measured performance. Hosted vendor models remain subject to vendor deployment options and terms.\u003c\/p\u003e\u003ch3\u003eDeliverables\u003c\/h3\u003e\u003cul\u003e\n\u003cli\u003eCurrent-state workflow map, baseline metrics and prioritized requirements\u003c\/li\u003e\n\u003cli\u003eArchitecture, access boundaries, integration design and exception-handling plan\u003c\/li\u003e\n\u003cli\u003eRepresentative evaluation cases and an agreed acceptance scorecard\u003c\/li\u003e\n\u003cli\u003eWorking workflow software, scoped integrations and operator review queue\u003c\/li\u003e\n\u003cli\u003eMonitoring, audit events, deployment runbook, team training and source-code handover\u003c\/li\u003e\n\u003c\/ul\u003e\u003ch3\u003eDelivery and acceptance\u003c\/h3\u003e\u003cp\u003eForward deployed engineers work alongside your operators, with an AI engineer, integration engineer and shared quality and security support. The team reviews progress weekly, tests representative normal and exception cases, and documents limitations. Release follows owner acceptance, access-control testing and a demonstrated pause or rollback process.\u003c\/p\u003e\u003ch3\u003eHow value is measured\u003c\/h3\u003e\u003cp\u003ePrimary measures: Incremental repeat purchase; unsubscribe rate. Establish a baseline before changes, then compare a controlled pilot or agreed pre\/post period. Track quality, adoption and attributable benefits alongside labor time and ongoing model costs. Targets and acceptance thresholds are agreed during discovery; financial or operational results are not guaranteed.\u003c\/p\u003e\u003ch3\u003eInvestment and timing\u003c\/h3\u003e\u003cp\u003eEstimated engagement: USD $125,000–$300,000. The listed product price of $125,000 is the starting professional services fee, not a deposit or a guaranteed fixed quote. Indicative delivery: 8-16 weeks after prerequisites are available. Final scope, milestones and commercial terms are agreed in a statement of work. Model\/API usage, cloud infrastructure, hardware, third-party licenses, taxes, major data remediation and ongoing managed support are excluded unless expressly included.\u003c\/p\u003e\u003ch3\u003eGetting started\u003c\/h3\u003e\u003cp\u003eProvide a named process owner, authorized access to representative data, integration documentation and operator time for review. Regulated or sensitive workflows require applicable privacy and security approvals before data access. The first working session confirms the use case, baseline, approval boundaries and the smallest valuable delivery scope.\u003c\/p\u003e","brand":"Corefinery","offers":[{"title":"Default Title","offer_id":68850837520578,"sku":"CF-AI-037","price":125000.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0798\/2101\/3186\/files\/Platinum_pathway_to_what_s_next-2.png?v=1794072948"},{"product_id":"etail-and-consumer-goods-supplier-negotiation-intelligence","title":"Retail: Supplier negotiation intelligence","description":"\u003cp\u003eBuyers manually reconcile supplier terms. Corefinery works with your retail and consumer goods team to turn this bottleneck into a practical, measurable workflow. The engagement focuses on summarize supplier performance and draft negotiation options rather than a standalone demonstration.\u003c\/p\u003e\u003ch3\u003eWhat the engagement covers\u003c\/h3\u003e\u003cp\u003eOne production workflow; up to two integrations; one business unit. We map the current process with the people doing the work, identify decision rights and exceptions, and agree which actions require approval. The workflow is designed around existing systems and an accountable business owner.\u003c\/p\u003e\u003ch3\u003eWorkflow and data\u003c\/h3\u003e\u003cp\u003eSource inputs: Client-authorized source-system records; approved operating policies; representative task examples; integration documentation. Summarize supplier performance and draft negotiation options. Every recommendation should retain its supporting context so an operator can inspect the evidence and correct incomplete or inaccurate output.\u003c\/p\u003e\u003ch3\u003eEngineering approach\u003c\/h3\u003e\u003cp\u003eDeterministic components: Landed cost calculations; target margins; approval limits. These rules control calculations, eligibility and permitted actions. Non-deterministic AI handles interpretation, retrieval and draft recommendations; it does not replace the policy engine. Consequential actions require an authorized human decision.\u003c\/p\u003e\u003cp\u003eModel selection compares task quality, latency, privacy requirements and cost. Claude or OpenAI can support complex language tasks; a specialized small language model can handle narrower classification or retrieval when it meets the same evaluation criteria. Adaptation, quantization and private or edge deployment are considered only when supported by data rights, infrastructure and measured performance. Hosted vendor models remain subject to vendor deployment options and terms.\u003c\/p\u003e\u003ch3\u003eDeliverables\u003c\/h3\u003e\u003cul\u003e\n\u003cli\u003eCurrent-state workflow map, baseline metrics and prioritized requirements\u003c\/li\u003e\n\u003cli\u003eArchitecture, access boundaries, integration design and exception-handling plan\u003c\/li\u003e\n\u003cli\u003eRepresentative evaluation cases and an agreed acceptance scorecard\u003c\/li\u003e\n\u003cli\u003eWorking workflow software, scoped integrations and operator review queue\u003c\/li\u003e\n\u003cli\u003eMonitoring, audit events, deployment runbook, team training and source-code handover\u003c\/li\u003e\n\u003c\/ul\u003e\u003ch3\u003eDelivery and acceptance\u003c\/h3\u003e\u003cp\u003eForward deployed engineers work alongside your operators, with an AI engineer, integration engineer and shared quality and security support. The team reviews progress weekly, tests representative normal and exception cases, and documents limitations. Release follows owner acceptance, access-control testing and a demonstrated pause or rollback process.\u003c\/p\u003e\u003ch3\u003eHow value is measured\u003c\/h3\u003e\u003cp\u003ePrimary measures: Buyer hours; negotiated savings. Establish a baseline before changes, then compare a controlled pilot or agreed pre\/post period. Track quality, adoption and attributable benefits alongside labor time and ongoing model costs. Targets and acceptance thresholds are agreed during discovery; financial or operational results are not guaranteed.\u003c\/p\u003e\u003ch3\u003eInvestment and timing\u003c\/h3\u003e\u003cp\u003eEstimated engagement: USD $150,000–$350,000. The listed product price of $150,000 is the starting professional services fee, not a deposit or a guaranteed fixed quote. Indicative delivery: 8-16 weeks after prerequisites are available. Final scope, milestones and commercial terms are agreed in a statement of work. Model\/API usage, cloud infrastructure, hardware, third-party licenses, taxes, major data remediation and ongoing managed support are excluded unless expressly included.\u003c\/p\u003e\u003ch3\u003eGetting started\u003c\/h3\u003e\u003cp\u003eProvide a named process owner, authorized access to representative data, integration documentation and operator time for review. Regulated or sensitive workflows require applicable privacy and security approvals before data access. The first working session confirms the use case, baseline, approval boundaries and the smallest valuable delivery scope.\u003c\/p\u003e","brand":"Corefinery","offers":[{"title":"Default Title","offer_id":68850837553346,"sku":"CF-AI-038","price":150000.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0798\/2101\/3186\/files\/Platinum_pathway_to_what_s_next-2.png?v=1794072948"},{"product_id":"etail-and-consumer-goods-private-slm-commerce-operations","title":"Retail: Private SLM commerce operations","description":"\u003cp\u003eRoutine product and order tasks have high inference cost. Corefinery works with your retail and consumer goods team to turn this bottleneck into a practical, measurable workflow. The engagement focuses on classify customer intent and retrieve product guidance rather than a standalone demonstration.\u003c\/p\u003e\u003ch3\u003eWhat the engagement covers\u003c\/h3\u003e\u003cp\u003eOne production workflow; up to two integrations; one business unit. We map the current process with the people doing the work, identify decision rights and exceptions, and agree which actions require approval. The workflow is designed around existing systems and an accountable business owner.\u003c\/p\u003e\u003ch3\u003eWorkflow and data\u003c\/h3\u003e\u003cp\u003eSource inputs: Client-authorized source-system records; approved operating policies; representative task examples; integration documentation. Classify customer intent and retrieve product guidance. Every recommendation should retain its supporting context so an operator can inspect the evidence and correct incomplete or inaccurate output.\u003c\/p\u003e\u003ch3\u003eEngineering approach\u003c\/h3\u003e\u003cp\u003eDeterministic components: Order permissions; stock validation; approved action schema. These rules control calculations, eligibility and permitted actions. Non-deterministic AI handles interpretation, retrieval and draft recommendations; it does not replace the policy engine. Consequential actions require an authorized human decision.\u003c\/p\u003e\u003cp\u003eModel selection compares task quality, latency, privacy requirements and cost. Claude or OpenAI can support complex language tasks; a specialized small language model can handle narrower classification or retrieval when it meets the same evaluation criteria. Adaptation, quantization and private or edge deployment are considered only when supported by data rights, infrastructure and measured performance. Hosted vendor models remain subject to vendor deployment options and terms.\u003c\/p\u003e\u003ch3\u003eDeliverables\u003c\/h3\u003e\u003cul\u003e\n\u003cli\u003eCurrent-state workflow map, baseline metrics and prioritized requirements\u003c\/li\u003e\n\u003cli\u003eArchitecture, access boundaries, integration design and exception-handling plan\u003c\/li\u003e\n\u003cli\u003eRepresentative evaluation cases and an agreed acceptance scorecard\u003c\/li\u003e\n\u003cli\u003eWorking workflow software, scoped integrations and operator review queue\u003c\/li\u003e\n\u003cli\u003eMonitoring, audit events, deployment runbook, team training and source-code handover\u003c\/li\u003e\n\u003c\/ul\u003e\u003ch3\u003eDelivery and acceptance\u003c\/h3\u003e\u003cp\u003eForward deployed engineers work alongside your operators, with an AI engineer, integration engineer and shared quality and security support. The team reviews progress weekly, tests representative normal and exception cases, and documents limitations. Release follows owner acceptance, access-control testing and a demonstrated pause or rollback process.\u003c\/p\u003e\u003ch3\u003eHow value is measured\u003c\/h3\u003e\u003cp\u003ePrimary measures: Cost per task; task completion accuracy. Establish a baseline before changes, then compare a controlled pilot or agreed pre\/post period. Track quality, adoption and attributable benefits alongside labor time and ongoing model costs. Targets and acceptance thresholds are agreed during discovery; financial or operational results are not guaranteed.\u003c\/p\u003e\u003ch3\u003eInvestment and timing\u003c\/h3\u003e\u003cp\u003eEstimated engagement: USD $175,000–$400,000. The listed product price of $175,000 is the starting professional services fee, not a deposit or a guaranteed fixed quote. Indicative delivery: 8-16 weeks after prerequisites are available. Final scope, milestones and commercial terms are agreed in a statement of work. Model\/API usage, cloud infrastructure, hardware, third-party licenses, taxes, major data remediation and ongoing managed support are excluded unless expressly included.\u003c\/p\u003e\u003ch3\u003eGetting started\u003c\/h3\u003e\u003cp\u003eProvide a named process owner, authorized access to representative data, integration documentation and operator time for review. Regulated or sensitive workflows require applicable privacy and security approvals before data access. The first working session confirms the use case, baseline, approval boundaries and the smallest valuable delivery scope.\u003c\/p\u003e","brand":"Corefinery","offers":[{"title":"Default Title","offer_id":68850838372546,"sku":"CF-AI-039","price":175000.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0798\/2101\/3186\/files\/Platinum_pathway_to_what_s_next-2.png?v=1794072948"},{"product_id":"etail-and-consumer-goods-enterprise-ai-workflow-transformation","title":"Retail: Enterprise AI workflow transformation","description":"\u003cp\u003eDisconnected workflows limit enterprise value in retail and consumer goods. Corefinery works with your retail and consumer goods 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":68850838405314,"sku":"CF-AI-040","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":"retail-retail-pricing-scenario-studio","title":"Retail: Retail pricing scenario studio","description":"\u003cp\u003eTeams lack a controlled way to compare price actions. Corefinery works with your retail and consumer goods team to turn this bottleneck into a practical, measurable workflow. The engagement focuses on explain demand assumptions and propose pricing 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: Product costs; sales history; price lists. Explain demand assumptions and propose pricing 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: Margin floors; approved price bands; publication 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: Margin impact; price-change rework. Establish a baseline before changes, then compare a controlled pilot or agreed pre\/post period. Track quality, adoption and attributable benefits alongside labor time and ongoing model costs. Targets and acceptance thresholds are agreed during discovery; financial or operational results are not guaranteed.\u003c\/p\u003e\u003ch3\u003eInvestment and timing\u003c\/h3\u003e\u003cp\u003eEstimated engagement: USD $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":68861902127298,"sku":"CF-AI-131","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":"retail-store-associate-clienteling","title":"Retail: Store associate clienteling","description":"\u003cp\u003eAssociates lack concise context for personal service. Corefinery works with your retail and consumer goods team to turn this bottleneck into a practical, measurable workflow. The engagement focuses on prepare customer briefs and draft relevant follow-ups rather than a standalone demonstration.\u003c\/p\u003e\u003ch3\u003eWhat the engagement covers\u003c\/h3\u003e\u003cp\u003eOne prioritized production workflow; up to two source-system integrations; one business unit; staged operator rollout. We map the current process with the people doing the work, identify decision rights and exceptions, and agree which actions require approval. The workflow is designed around existing systems and an accountable business owner.\u003c\/p\u003e\u003ch3\u003eWorkflow and data\u003c\/h3\u003e\u003cp\u003eSource inputs: Consented customer profiles; catalog; purchase history. Prepare customer briefs and draft relevant follow-ups. Every recommendation should retain its supporting context so an operator can inspect the evidence and correct incomplete or inaccurate output.\u003c\/p\u003e\u003ch3\u003eEngineering approach\u003c\/h3\u003e\u003cp\u003eDeterministic components: Consent; stock availability; contact frequency. 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: Repeat purchase; associate preparation time. Establish a baseline before changes, then compare a controlled pilot or agreed pre\/post period. Track quality, adoption and attributable benefits alongside labor time and ongoing model costs. Targets and acceptance thresholds are agreed during discovery; financial or operational results are not guaranteed.\u003c\/p\u003e\u003ch3\u003eInvestment and timing\u003c\/h3\u003e\u003cp\u003eEstimated engagement: USD $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":68861902225602,"sku":"CF-AI-132","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":"retail-wholesale-order-exception-desk","title":"Retail: Wholesale order exception desk","description":"\u003cp\u003eOrder discrepancies delay fulfillment. Corefinery works with your retail and consumer goods team to turn this bottleneck into a practical, measurable workflow. The engagement focuses on interpret discrepancies and draft resolution messages 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: Purchase orders; invoices; warehouse availability. Interpret discrepancies and draft resolution messages. 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: Quantity matching; customer terms; release authority. These rules control calculations, eligibility and permitted actions. Non-deterministic AI handles interpretation, retrieval and draft recommendations; it does not replace the policy engine. Consequential actions require an authorized human decision.\u003c\/p\u003e\u003cp\u003eModel selection compares task quality, latency, privacy requirements and cost. Claude or OpenAI can support complex language tasks; a specialized small language model can handle narrower classification or retrieval when it meets the same evaluation criteria. Adaptation, quantization and private or edge deployment are considered only when supported by data rights, infrastructure and measured performance. Hosted vendor models remain subject to vendor deployment options and terms.\u003c\/p\u003e\u003ch3\u003eDeliverables\u003c\/h3\u003e\u003cul\u003e\n\u003cli\u003eCurrent-state workflow map, baseline metrics and prioritized requirements\u003c\/li\u003e\n\u003cli\u003eArchitecture, access boundaries, integration design and exception-handling plan\u003c\/li\u003e\n\u003cli\u003eRepresentative evaluation cases and an agreed acceptance scorecard\u003c\/li\u003e\n\u003cli\u003eWorking workflow software, scoped integrations and operator review queue\u003c\/li\u003e\n\u003cli\u003eMonitoring, audit events, deployment runbook, team training and source-code handover\u003c\/li\u003e\n\u003c\/ul\u003e\u003ch3\u003eDelivery and acceptance\u003c\/h3\u003e\u003cp\u003eForward deployed engineers work alongside your operators, with an AI engineer, integration engineer and shared quality and security support. The team reviews progress weekly, tests representative normal and exception cases, and documents limitations. Release follows owner acceptance, access-control testing and a demonstrated pause or rollback process.\u003c\/p\u003e\u003ch3\u003eHow value is measured\u003c\/h3\u003e\u003cp\u003ePrimary measures: Exception age; order accuracy. Establish a baseline before changes, then compare a controlled pilot or agreed pre\/post period. Track quality, adoption and attributable benefits alongside labor time and ongoing model costs. Targets and acceptance thresholds are agreed during discovery; financial or operational results are not guaranteed.\u003c\/p\u003e\u003ch3\u003eInvestment and timing\u003c\/h3\u003e\u003cp\u003eEstimated engagement: USD $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":68861902356674,"sku":"CF-AI-133","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":"retail-product-launch-readiness-studio","title":"Retail: Product launch readiness studio","description":"\u003cp\u003eLaunch teams miss incomplete assets and dependencies. Corefinery works with your retail and consumer goods team to turn this bottleneck into a practical, measurable workflow. The engagement focuses on summarize readiness gaps and draft completion tasks 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: Product records; launch plans; creative approvals. Summarize readiness gaps and draft completion tasks. 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 assets; go-live dates; reviewer 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: On-time launch rate; missing assets. 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":68861903765698,"sku":"CF-AI-134","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":"retail-consumer-review-intelligence","title":"Retail: Consumer review intelligence","description":"\u003cp\u003eCustomer feedback is too scattered to guide action. Corefinery works with your retail and consumer goods team to turn this bottleneck into a practical, measurable workflow. The engagement focuses on cluster product issues and suggest investigation priorities 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 reviews; support themes; product attributes. Cluster product issues and suggest investigation 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: Source provenance; product mapping; reviewer 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: Analysis time; actionable themes. 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":68861904519362,"sku":"CF-AI-135","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":"retail-promotion-incrementality-workbench","title":"Retail: Promotion incrementality workbench","description":"\u003cp\u003ePromotion teams cannot isolate incremental value. Corefinery works with your retail and consumer goods team to turn this bottleneck into a practical, measurable workflow. The engagement focuses on explain experiment results and draft next-test options 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: Campaign records; transaction data; cost inputs. Explain experiment results and draft next-test 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: Holdout assignment; margin calculations; exposure rules. These rules control calculations, eligibility and permitted actions. Non-deterministic AI handles interpretation, retrieval and draft recommendations; it does not replace the policy engine. Consequential actions require an authorized human decision.\u003c\/p\u003e\u003cp\u003eModel selection compares task quality, latency, privacy requirements and cost. Claude or OpenAI can support complex language tasks; a specialized small language model can handle narrower classification or retrieval when it meets the same evaluation criteria. Adaptation, quantization and private or edge deployment are considered only when supported by data rights, infrastructure and measured performance. Hosted vendor models remain subject to vendor deployment options and terms.\u003c\/p\u003e\u003ch3\u003eDeliverables\u003c\/h3\u003e\u003cul\u003e\n\u003cli\u003eCurrent-state workflow map, baseline metrics and prioritized requirements\u003c\/li\u003e\n\u003cli\u003eArchitecture, access boundaries, integration design and exception-handling plan\u003c\/li\u003e\n\u003cli\u003eRepresentative evaluation cases and an agreed acceptance scorecard\u003c\/li\u003e\n\u003cli\u003eWorking workflow software, scoped integrations and operator review queue\u003c\/li\u003e\n\u003cli\u003eMonitoring, audit events, deployment runbook, team training and source-code handover\u003c\/li\u003e\n\u003c\/ul\u003e\u003ch3\u003eDelivery and acceptance\u003c\/h3\u003e\u003cp\u003eForward deployed engineers work alongside your operators, with an AI engineer, integration engineer and shared quality and security support. The team reviews progress weekly, tests representative normal and exception cases, and documents limitations. Release follows owner acceptance, access-control testing and a demonstrated pause or rollback process.\u003c\/p\u003e\u003ch3\u003eHow value is measured\u003c\/h3\u003e\u003cp\u003ePrimary measures: Incremental margin; experiment 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 $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":68861904814274,"sku":"CF-AI-136","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":"retail-retail-fraud-review-assistant","title":"Retail: Retail fraud review assistant","description":"\u003cp\u003eReturns and order alerts overwhelm reviewers. Corefinery works with your retail and consumer goods team to turn this bottleneck into a practical, measurable workflow. The engagement focuses on summarize patterns and prioritize manual investigation rather than a standalone demonstration.\u003c\/p\u003e\u003ch3\u003eWhat the engagement covers\u003c\/h3\u003e\u003cp\u003eOne prioritized production workflow; up to two source-system integrations; one business unit; staged operator rollout. We map the current process with the people doing the work, identify decision rights and exceptions, and agree which actions require approval. The workflow is designed around existing systems and an accountable business owner.\u003c\/p\u003e\u003ch3\u003eWorkflow and data\u003c\/h3\u003e\u003cp\u003eSource inputs: Order history; return records; approved risk signals. Summarize patterns and prioritize manual investigation. 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: Review thresholds; evidence requirements; refund authorization. These rules control calculations, eligibility and permitted actions. Non-deterministic AI handles interpretation, retrieval and draft recommendations; it does not replace the policy engine. Consequential actions require an authorized human decision.\u003c\/p\u003e\u003cp\u003eModel selection compares task quality, latency, privacy requirements and cost. Claude or OpenAI can support complex language tasks; a specialized small language model can handle narrower classification or retrieval when it meets the same evaluation criteria. Adaptation, quantization and private or edge deployment are considered only when supported by data rights, infrastructure and measured performance. Hosted vendor models remain subject to vendor deployment options and terms.\u003c\/p\u003e\u003ch3\u003eDeliverables\u003c\/h3\u003e\u003cul\u003e\n\u003cli\u003eCurrent-state workflow map, baseline metrics and prioritized requirements\u003c\/li\u003e\n\u003cli\u003eArchitecture, access boundaries, integration design and exception-handling plan\u003c\/li\u003e\n\u003cli\u003eRepresentative evaluation cases and an agreed acceptance scorecard\u003c\/li\u003e\n\u003cli\u003eWorking workflow software, scoped integrations and operator review queue\u003c\/li\u003e\n\u003cli\u003eMonitoring, audit events, deployment runbook, team training and source-code handover\u003c\/li\u003e\n\u003c\/ul\u003e\u003ch3\u003eDelivery and acceptance\u003c\/h3\u003e\u003cp\u003eForward deployed engineers work alongside your operators, with an AI engineer, integration engineer and shared quality and security support. The team reviews progress weekly, tests representative normal and exception cases, and documents limitations. Release follows owner acceptance, access-control testing and a demonstrated pause or rollback process.\u003c\/p\u003e\u003ch3\u003eHow value is measured\u003c\/h3\u003e\u003cp\u003ePrimary measures: Review time; confirmed-case precision. Establish a baseline before changes, then compare a controlled pilot or agreed pre\/post period. Track quality, adoption and attributable benefits alongside labor time and ongoing model costs. Targets and acceptance thresholds are agreed during discovery; financial or operational results are not guaranteed.\u003c\/p\u003e\u003ch3\u003eInvestment and timing\u003c\/h3\u003e\u003cp\u003eEstimated engagement: USD $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":68861906878658,"sku":"CF-AI-137","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":"retail-omnichannel-order-promise-engine","title":"Retail: Omnichannel order promise engine","description":"\u003cp\u003eCustomers receive inconsistent delivery expectations. Corefinery works with your retail and consumer goods team to turn this bottleneck into a practical, measurable workflow. The engagement focuses on explain promise exceptions and propose recovery communications 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: Inventory; carrier schedules; fulfillment queues. Explain promise exceptions and propose recovery communications. 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: Cutoff times; capacity; transit calculations. 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: Promise accuracy; customer contacts. 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":68861907566786,"sku":"CF-AI-138","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":"retail-claude-enterprise-workflow-deployment","title":"Retail: Claude enterprise workflow deployment","description":"\u003cp\u003eTeams need a governed path from Claude experimentation to one usable industry workflow. Corefinery works with your retail and consumer goods 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":68861908320450,"sku":"CF-AI-139","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":"retail-openai-production-agent-engineering","title":"Retail: OpenAI production agent engineering","description":"\u003cp\u003ePromising OpenAI prototypes lack production integration and accountable operating controls. Corefinery works with your retail and consumer goods 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":68861910450370,"sku":"CF-AI-140","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"}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0798\/2101\/3186\/collections\/Retail_AI_for_smarter_growth.png?v=1794454476","url":"https:\/\/corefinery.com\/collections\/retail.oembed","provider":"Corefinery","version":"1.0","type":"link"}