{"title":"Energy \u0026 utilities","description":"\u003cp\u003eConnect asset intelligence, field operations and customer service. Help teams prioritize maintenance, coordinate resources and respond to operational challenges.\u003c\/p\u003e","products":[{"product_id":"nergy-and-utilities-ai-value-and-deployment-assessment","title":"Energy: AI value and deployment assessment","description":"\u003cp\u003eIdentify where AI can create practical value in energy and utilities 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":68850841485506,"sku":"CF-AI-051","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":"nergy-and-utilities-asset-maintenance-prioritizer","title":"Energy: Asset maintenance prioritizer","description":"\u003cp\u003eAsset risks compete for limited crews. Corefinery works with your energy and utilities team to turn this bottleneck into a practical, measurable workflow. The engagement focuses on summarize condition evidence and rank review candidates rather than a standalone demonstration.\u003c\/p\u003e\u003ch3\u003eWhat the engagement covers\u003c\/h3\u003e\u003cp\u003eOne production workflow; up to two integrations; one business unit. We map the current process with the people doing the work, identify decision rights and exceptions, and agree which actions require approval. The workflow is designed around existing systems and an accountable business owner.\u003c\/p\u003e\u003ch3\u003eWorkflow and data\u003c\/h3\u003e\u003cp\u003eSource inputs: Client-authorized source-system records; approved operating policies; representative task examples; integration documentation. Summarize condition evidence and rank review candidates. Every recommendation should retain its supporting context so an operator can inspect the evidence and correct incomplete or inaccurate output.\u003c\/p\u003e\u003ch3\u003eEngineering approach\u003c\/h3\u003e\u003cp\u003eDeterministic components: Asset criticality; inspection intervals; crew qualifications. These rules control calculations, eligibility and permitted actions. Non-deterministic AI handles interpretation, retrieval and draft recommendations; it does not replace the policy engine. Consequential actions require an authorized human decision.\u003c\/p\u003e\u003cp\u003eModel selection compares task quality, latency, privacy requirements and cost. Claude or OpenAI can support complex language tasks; a specialized small language model can handle narrower classification or retrieval when it meets the same evaluation criteria. Adaptation, quantization and private or edge deployment are considered only when supported by data rights, infrastructure and measured performance. Hosted vendor models remain subject to vendor deployment options and terms.\u003c\/p\u003e\u003ch3\u003eDeliverables\u003c\/h3\u003e\u003cul\u003e\n\u003cli\u003eCurrent-state workflow map, baseline metrics and prioritized requirements\u003c\/li\u003e\n\u003cli\u003eArchitecture, access boundaries, integration design and exception-handling plan\u003c\/li\u003e\n\u003cli\u003eRepresentative evaluation cases and an agreed acceptance scorecard\u003c\/li\u003e\n\u003cli\u003eWorking workflow software, scoped integrations and operator review queue\u003c\/li\u003e\n\u003cli\u003eMonitoring, audit events, deployment runbook, team training and source-code handover\u003c\/li\u003e\n\u003c\/ul\u003e\u003ch3\u003eDelivery and acceptance\u003c\/h3\u003e\u003cp\u003eForward deployed engineers work alongside your operators, with an AI engineer, integration engineer and shared quality and security support. The team reviews progress weekly, tests representative normal and exception cases, and documents limitations. Release follows owner acceptance, access-control testing and a demonstrated pause or rollback process.\u003c\/p\u003e\u003ch3\u003eHow value is measured\u003c\/h3\u003e\u003cp\u003ePrimary measures: Inspection backlog; maintenance planning hours. Establish a baseline before changes, then compare a controlled pilot or agreed pre\/post period. Track quality, adoption and attributable benefits alongside labor time and ongoing model costs. Targets and acceptance thresholds are agreed during discovery; financial or operational results are not guaranteed.\u003c\/p\u003e\u003ch3\u003eInvestment and timing\u003c\/h3\u003e\u003cp\u003eEstimated engagement: USD $40,000–$90,000. The listed product price of $40,000 is the starting professional services fee, not a deposit or a guaranteed fixed quote. Indicative delivery: 8-16 weeks after prerequisites are available. Final scope, milestones and commercial terms are agreed in a statement of work. Model\/API usage, cloud infrastructure, hardware, third-party licenses, taxes, major data remediation and ongoing managed support are excluded unless expressly included.\u003c\/p\u003e\u003ch3\u003eGetting started\u003c\/h3\u003e\u003cp\u003eProvide a named process owner, authorized access to representative data, integration documentation and operator time for review. Regulated or sensitive workflows require applicable privacy and security approvals before data access. The first working session confirms the use case, baseline, approval boundaries and the smallest valuable delivery scope.\u003c\/p\u003e","brand":"Corefinery","offers":[{"title":"Default Title","offer_id":68850842108098,"sku":"CF-AI-052","price":40000.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0798\/2101\/3186\/files\/Platinum_pathway_to_what_s_next-2.png?v=1794072948"},{"product_id":"nergy-and-utilities-outage-coordination-copilot","title":"Energy: Outage coordination copilot","description":"\u003cp\u003eOutage response information is fragmented. Corefinery works with your energy and utilities team to turn this bottleneck into a practical, measurable workflow. The engagement focuses on summarize incident updates and draft coordination briefs rather than a standalone demonstration.\u003c\/p\u003e\u003ch3\u003eWhat the engagement covers\u003c\/h3\u003e\u003cp\u003eOne production workflow; up to two integrations; one business unit. We map the current process with the people doing the work, identify decision rights and exceptions, and agree which actions require approval. The workflow is designed around existing systems and an accountable business owner.\u003c\/p\u003e\u003ch3\u003eWorkflow and data\u003c\/h3\u003e\u003cp\u003eSource inputs: Client-authorized source-system records; approved operating policies; representative task examples; integration documentation. Summarize incident updates and draft coordination briefs. Every recommendation should retain its supporting context so an operator can inspect the evidence and correct incomplete or inaccurate output.\u003c\/p\u003e\u003ch3\u003eEngineering approach\u003c\/h3\u003e\u003cp\u003eDeterministic components: Dispatch authorization; safety procedures; restoration prerequisites. These rules control calculations, eligibility and permitted actions. Non-deterministic AI handles interpretation, retrieval and draft recommendations; it does not replace the policy engine. Consequential actions require an authorized human decision.\u003c\/p\u003e\u003cp\u003eModel selection compares task quality, latency, privacy requirements and cost. Claude or OpenAI can support complex language tasks; a specialized small language model can handle narrower classification or retrieval when it meets the same evaluation criteria. Adaptation, quantization and private or edge deployment are considered only when supported by data rights, infrastructure and measured performance. Hosted vendor models remain subject to vendor deployment options and terms.\u003c\/p\u003e\u003ch3\u003eDeliverables\u003c\/h3\u003e\u003cul\u003e\n\u003cli\u003eCurrent-state workflow map, baseline metrics and prioritized requirements\u003c\/li\u003e\n\u003cli\u003eArchitecture, access boundaries, integration design and exception-handling plan\u003c\/li\u003e\n\u003cli\u003eRepresentative evaluation cases and an agreed acceptance scorecard\u003c\/li\u003e\n\u003cli\u003eWorking workflow software, scoped integrations and operator review queue\u003c\/li\u003e\n\u003cli\u003eMonitoring, audit events, deployment runbook, team training and source-code handover\u003c\/li\u003e\n\u003c\/ul\u003e\u003ch3\u003eDelivery and acceptance\u003c\/h3\u003e\u003cp\u003eForward deployed engineers work alongside your operators, with an AI engineer, integration engineer and shared quality and security support. The team reviews progress weekly, tests representative normal and exception cases, and documents limitations. Release follows owner acceptance, access-control testing and a demonstrated pause or rollback process.\u003c\/p\u003e\u003ch3\u003eHow value is measured\u003c\/h3\u003e\u003cp\u003ePrimary measures: Brief preparation time; handoff omissions. Establish a baseline before changes, then compare a controlled pilot or agreed pre\/post period. Track quality, adoption and attributable benefits alongside labor time and ongoing model costs. Targets and acceptance thresholds are agreed during discovery; financial or operational results are not guaranteed.\u003c\/p\u003e\u003ch3\u003eInvestment and timing\u003c\/h3\u003e\u003cp\u003eEstimated engagement: USD $60,000–$140,000. The listed product price of $60,000 is the starting professional services fee, not a deposit or a guaranteed fixed quote. Indicative delivery: 8-16 weeks after prerequisites are available. Final scope, milestones and commercial terms are agreed in a statement of work. Model\/API usage, cloud infrastructure, hardware, third-party licenses, taxes, major data remediation and ongoing managed support are excluded unless expressly included.\u003c\/p\u003e\u003ch3\u003eGetting started\u003c\/h3\u003e\u003cp\u003eProvide a named process owner, authorized access to representative data, integration documentation and operator time for review. Regulated or sensitive workflows require applicable privacy and security approvals before data access. The first working session confirms the use case, baseline, approval boundaries and the smallest valuable delivery scope.\u003c\/p\u003e","brand":"Corefinery","offers":[{"title":"Default Title","offer_id":68850842304706,"sku":"CF-AI-053","price":60000.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0798\/2101\/3186\/files\/Platinum_pathway_to_what_s_next-2.png?v=1794072948"},{"product_id":"nergy-and-utilities-field-service-planning-engine","title":"Energy: Field service planning engine","description":"\u003cp\u003eTravel and skill mismatches reduce productivity. Corefinery works with your energy and utilities team to turn this bottleneck into a practical, measurable workflow. The engagement focuses on explain scheduling exceptions and propose dispatch options rather than a standalone demonstration.\u003c\/p\u003e\u003ch3\u003eWhat the engagement covers\u003c\/h3\u003e\u003cp\u003eOne production workflow; up to two integrations; one business unit. We map the current process with the people doing the work, identify decision rights and exceptions, and agree which actions require approval. The workflow is designed around existing systems and an accountable business owner.\u003c\/p\u003e\u003ch3\u003eWorkflow and data\u003c\/h3\u003e\u003cp\u003eSource inputs: Client-authorized source-system records; approved operating policies; representative task examples; integration documentation. Explain scheduling exceptions and propose dispatch options. Every recommendation should retain its supporting context so an operator can inspect the evidence and correct incomplete or inaccurate output.\u003c\/p\u003e\u003ch3\u003eEngineering approach\u003c\/h3\u003e\u003cp\u003eDeterministic components: Crew certifications; geography; work windows; rest limits. These rules control calculations, eligibility and permitted actions. Non-deterministic AI handles interpretation, retrieval and draft recommendations; it does not replace the policy engine. Consequential actions require an authorized human decision.\u003c\/p\u003e\u003cp\u003eModel selection compares task quality, latency, privacy requirements and cost. Claude or OpenAI can support complex language tasks; a specialized small language model can handle narrower classification or retrieval when it meets the same evaluation criteria. Adaptation, quantization and private or edge deployment are considered only when supported by data rights, infrastructure and measured performance. Hosted vendor models remain subject to vendor deployment options and terms.\u003c\/p\u003e\u003ch3\u003eDeliverables\u003c\/h3\u003e\u003cul\u003e\n\u003cli\u003eCurrent-state workflow map, baseline metrics and prioritized requirements\u003c\/li\u003e\n\u003cli\u003eArchitecture, access boundaries, integration design and exception-handling plan\u003c\/li\u003e\n\u003cli\u003eRepresentative evaluation cases and an agreed acceptance scorecard\u003c\/li\u003e\n\u003cli\u003eWorking workflow software, scoped integrations and operator review queue\u003c\/li\u003e\n\u003cli\u003eMonitoring, audit events, deployment runbook, team training and source-code handover\u003c\/li\u003e\n\u003c\/ul\u003e\u003ch3\u003eDelivery and acceptance\u003c\/h3\u003e\u003cp\u003eForward deployed engineers work alongside your operators, with an AI engineer, integration engineer and shared quality and security support. The team reviews progress weekly, tests representative normal and exception cases, and documents limitations. Release follows owner acceptance, access-control testing and a demonstrated pause or rollback process.\u003c\/p\u003e\u003ch3\u003eHow value is measured\u003c\/h3\u003e\u003cp\u003ePrimary measures: Travel hours; on-time completion. Establish a baseline before changes, then compare a controlled pilot or agreed pre\/post period. Track quality, adoption and attributable benefits alongside labor time and ongoing model costs. Targets and acceptance thresholds are agreed during discovery; financial or operational results are not guaranteed.\u003c\/p\u003e\u003ch3\u003eInvestment and timing\u003c\/h3\u003e\u003cp\u003eEstimated engagement: USD $75,000–$180,000. The listed product price of $75,000 is the starting professional services fee, not a deposit or a guaranteed fixed quote. Indicative delivery: 8-16 weeks after prerequisites are available. Final scope, milestones and commercial terms are agreed in a statement of work. Model\/API usage, cloud infrastructure, hardware, third-party licenses, taxes, major data remediation and ongoing managed support are excluded unless expressly included.\u003c\/p\u003e\u003ch3\u003eGetting started\u003c\/h3\u003e\u003cp\u003eProvide a named process owner, authorized access to representative data, integration documentation and operator time for review. Regulated or sensitive workflows require applicable privacy and security approvals before data access. The first working session confirms the use case, baseline, approval boundaries and the smallest valuable delivery scope.\u003c\/p\u003e","brand":"Corefinery","offers":[{"title":"Default Title","offer_id":68850842337474,"sku":"CF-AI-054","price":75000.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0798\/2101\/3186\/files\/Platinum_pathway_to_what_s_next-2.png?v=1794072948"},{"product_id":"nergy-and-utilities-interconnection-application-assistant","title":"Energy: Interconnection application assistant","description":"\u003cp\u003eIncomplete submissions slow project reviews. Corefinery works with your energy and utilities team to turn this bottleneck into a practical, measurable workflow. The engagement focuses on extract application facts and identify missing evidence rather than a standalone demonstration.\u003c\/p\u003e\u003ch3\u003eWhat the engagement covers\u003c\/h3\u003e\u003cp\u003eOne production workflow; up to two integrations; one business unit. We map the current process with the people doing the work, identify decision rights and exceptions, and agree which actions require approval. The workflow is designed around existing systems and an accountable business owner.\u003c\/p\u003e\u003ch3\u003eWorkflow and data\u003c\/h3\u003e\u003cp\u003eSource inputs: Client-authorized source-system records; approved operating policies; representative task examples; integration documentation. Extract application facts and identify missing evidence. Every recommendation should retain its supporting context so an operator can inspect the evidence and correct incomplete or inaccurate output.\u003c\/p\u003e\u003ch3\u003eEngineering approach\u003c\/h3\u003e\u003cp\u003eDeterministic components: Required forms; equipment ratings; engineering approval. These rules control calculations, eligibility and permitted actions. Non-deterministic AI handles interpretation, retrieval and draft recommendations; it does not replace the policy engine. Consequential actions require an authorized human decision.\u003c\/p\u003e\u003cp\u003eModel selection compares task quality, latency, privacy requirements and cost. Claude or OpenAI can support complex language tasks; a specialized small language model can handle narrower classification or retrieval when it meets the same evaluation criteria. Adaptation, quantization and private or edge deployment are considered only when supported by data rights, infrastructure and measured performance. Hosted vendor models remain subject to vendor deployment options and terms.\u003c\/p\u003e\u003ch3\u003eDeliverables\u003c\/h3\u003e\u003cul\u003e\n\u003cli\u003eCurrent-state workflow map, baseline metrics and prioritized requirements\u003c\/li\u003e\n\u003cli\u003eArchitecture, access boundaries, integration design and exception-handling plan\u003c\/li\u003e\n\u003cli\u003eRepresentative evaluation cases and an agreed acceptance scorecard\u003c\/li\u003e\n\u003cli\u003eWorking workflow software, scoped integrations and operator review queue\u003c\/li\u003e\n\u003cli\u003eMonitoring, audit events, deployment runbook, team training and source-code handover\u003c\/li\u003e\n\u003c\/ul\u003e\u003ch3\u003eDelivery and acceptance\u003c\/h3\u003e\u003cp\u003eForward deployed engineers work alongside your operators, with an AI engineer, integration engineer and shared quality and security support. The team reviews progress weekly, tests representative normal and exception cases, and documents limitations. Release follows owner acceptance, access-control testing and a demonstrated pause or rollback process.\u003c\/p\u003e\u003ch3\u003eHow value is measured\u003c\/h3\u003e\u003cp\u003ePrimary measures: Review time; resubmission rate. Establish a baseline before changes, then compare a controlled pilot or agreed pre\/post period. Track quality, adoption and attributable benefits alongside labor time and ongoing model costs. Targets and acceptance thresholds are agreed during discovery; financial or operational results are not guaranteed.\u003c\/p\u003e\u003ch3\u003eInvestment and timing\u003c\/h3\u003e\u003cp\u003eEstimated engagement: USD $90,000–$225,000. The listed product price of $90,000 is the starting professional services fee, not a deposit or a guaranteed fixed quote. Indicative delivery: 8-16 weeks after prerequisites are available. Final scope, milestones and commercial terms are agreed in a statement of work. Model\/API usage, cloud infrastructure, hardware, third-party licenses, taxes, major data remediation and ongoing managed support are excluded unless expressly included.\u003c\/p\u003e\u003ch3\u003eGetting started\u003c\/h3\u003e\u003cp\u003eProvide a named process owner, authorized access to representative data, integration documentation and operator time for review. Regulated or sensitive workflows require applicable privacy and security approvals before data access. The first working session confirms the use case, baseline, approval boundaries and the smallest valuable delivery scope.\u003c\/p\u003e","brand":"Corefinery","offers":[{"title":"Default Title","offer_id":68850845712578,"sku":"CF-AI-055","price":90000.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0798\/2101\/3186\/files\/Platinum_pathway_to_what_s_next-2.png?v=1794072948"},{"product_id":"nergy-and-utilities-energy-trading-research-copilot","title":"Energy: Energy trading research copilot","description":"\u003cp\u003eAnalysts manually reconcile market narratives. Corefinery works with your energy and utilities team to turn this bottleneck into a practical, measurable workflow. The engagement focuses on summarize authorized market information and draft scenario briefs rather than a standalone demonstration.\u003c\/p\u003e\u003ch3\u003eWhat the engagement covers\u003c\/h3\u003e\u003cp\u003eOne production workflow; up to two integrations; one business unit. We map the current process with the people doing the work, identify decision rights and exceptions, and agree which actions require approval. The workflow is designed around existing systems and an accountable business owner.\u003c\/p\u003e\u003ch3\u003eWorkflow and data\u003c\/h3\u003e\u003cp\u003eSource inputs: Client-authorized source-system records; approved operating policies; representative task examples; integration documentation. Summarize authorized market information and draft scenario briefs. Every recommendation should retain its supporting context so an operator can inspect the evidence and correct incomplete or inaccurate output.\u003c\/p\u003e\u003ch3\u003eEngineering approach\u003c\/h3\u003e\u003cp\u003eDeterministic components: Position limits; approved data access; no autonomous trade release. These rules control calculations, eligibility and permitted actions. Non-deterministic AI handles interpretation, retrieval and draft recommendations; it does not replace the policy engine. Consequential actions require an authorized human decision.\u003c\/p\u003e\u003cp\u003eModel selection compares task quality, latency, privacy requirements and cost. Claude or OpenAI can support complex language tasks; a specialized small language model can handle narrower classification or retrieval when it meets the same evaluation criteria. Adaptation, quantization and private or edge deployment are considered only when supported by data rights, infrastructure and measured performance. Hosted vendor models remain subject to vendor deployment options and terms.\u003c\/p\u003e\u003ch3\u003eDeliverables\u003c\/h3\u003e\u003cul\u003e\n\u003cli\u003eCurrent-state workflow map, baseline metrics and prioritized requirements\u003c\/li\u003e\n\u003cli\u003eArchitecture, access boundaries, integration design and exception-handling plan\u003c\/li\u003e\n\u003cli\u003eRepresentative evaluation cases and an agreed acceptance scorecard\u003c\/li\u003e\n\u003cli\u003eWorking workflow software, scoped integrations and operator review queue\u003c\/li\u003e\n\u003cli\u003eMonitoring, audit events, deployment runbook, team training and source-code handover\u003c\/li\u003e\n\u003c\/ul\u003e\u003ch3\u003eDelivery and acceptance\u003c\/h3\u003e\u003cp\u003eForward deployed engineers work alongside your operators, with an AI engineer, integration engineer and shared quality and security support. The team reviews progress weekly, tests representative normal and exception cases, and documents limitations. Release follows owner acceptance, access-control testing and a demonstrated pause or rollback process.\u003c\/p\u003e\u003ch3\u003eHow value is measured\u003c\/h3\u003e\u003cp\u003ePrimary measures: Research hours; source coverage. Establish a baseline before changes, then compare a controlled pilot or agreed pre\/post period. Track quality, adoption and attributable benefits alongside labor time and ongoing model costs. Targets and acceptance thresholds are agreed during discovery; financial or operational results are not guaranteed.\u003c\/p\u003e\u003ch3\u003eInvestment and timing\u003c\/h3\u003e\u003cp\u003eEstimated engagement: USD $100,000–$250,000. The listed product price of $100,000 is the starting professional services fee, not a deposit or a guaranteed fixed quote. Indicative delivery: 8-16 weeks after prerequisites are available. Final scope, milestones and commercial terms are agreed in a statement of work. Model\/API usage, cloud infrastructure, hardware, third-party licenses, taxes, major data remediation and ongoing managed support are excluded unless expressly included.\u003c\/p\u003e\u003ch3\u003eGetting started\u003c\/h3\u003e\u003cp\u003eProvide a named process owner, authorized access to representative data, integration documentation and operator time for review. Regulated or sensitive workflows require applicable privacy and security approvals before data access. The first working session confirms the use case, baseline, approval boundaries and the smallest valuable delivery scope.\u003c\/p\u003e","brand":"Corefinery","offers":[{"title":"Default Title","offer_id":68850845745346,"sku":"CF-AI-056","price":100000.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0798\/2101\/3186\/files\/Platinum_pathway_to_what_s_next-2.png?v=1794072948"},{"product_id":"nergy-and-utilities-utility-customer-resolution","title":"Energy: Utility customer resolution","description":"\u003cp\u003eBilling and service requests require repeated investigation. Corefinery works with your energy and utilities team to turn this bottleneck into a practical, measurable workflow. The engagement focuses on explain billing exceptions and draft service responses rather than a standalone demonstration.\u003c\/p\u003e\u003ch3\u003eWhat the engagement covers\u003c\/h3\u003e\u003cp\u003eOne production workflow; up to two integrations; one business unit. We map the current process with the people doing the work, identify decision rights and exceptions, and agree which actions require approval. The workflow is designed around existing systems and an accountable business owner.\u003c\/p\u003e\u003ch3\u003eWorkflow and data\u003c\/h3\u003e\u003cp\u003eSource inputs: Client-authorized source-system records; approved operating policies; representative task examples; integration documentation. Explain billing exceptions and draft service responses. Every recommendation should retain its supporting context so an operator can inspect the evidence and correct incomplete or inaccurate output.\u003c\/p\u003e\u003ch3\u003eEngineering approach\u003c\/h3\u003e\u003cp\u003eDeterministic components: Tariff calculations; account verification; adjustment limits. These rules control calculations, eligibility and permitted actions. Non-deterministic AI handles interpretation, retrieval and draft recommendations; it does not replace the policy engine. Consequential actions require an authorized human decision.\u003c\/p\u003e\u003cp\u003eModel selection compares task quality, latency, privacy requirements and cost. Claude or OpenAI can support complex language tasks; a specialized small language model can handle narrower classification or retrieval when it meets the same evaluation criteria. Adaptation, quantization and private or edge deployment are considered only when supported by data rights, infrastructure and measured performance. Hosted vendor models remain subject to vendor deployment options and terms.\u003c\/p\u003e\u003ch3\u003eDeliverables\u003c\/h3\u003e\u003cul\u003e\n\u003cli\u003eCurrent-state workflow map, baseline metrics and prioritized requirements\u003c\/li\u003e\n\u003cli\u003eArchitecture, access boundaries, integration design and exception-handling plan\u003c\/li\u003e\n\u003cli\u003eRepresentative evaluation cases and an agreed acceptance scorecard\u003c\/li\u003e\n\u003cli\u003eWorking workflow software, scoped integrations and operator review queue\u003c\/li\u003e\n\u003cli\u003eMonitoring, audit events, deployment runbook, team training and source-code handover\u003c\/li\u003e\n\u003c\/ul\u003e\u003ch3\u003eDelivery and acceptance\u003c\/h3\u003e\u003cp\u003eForward deployed engineers work alongside your operators, with an AI engineer, integration engineer and shared quality and security support. The team reviews progress weekly, tests representative normal and exception cases, and documents limitations. Release follows owner acceptance, access-control testing and a demonstrated pause or rollback process.\u003c\/p\u003e\u003ch3\u003eHow value is measured\u003c\/h3\u003e\u003cp\u003ePrimary measures: Resolution time; repeat calls. Establish a baseline before changes, then compare a controlled pilot or agreed pre\/post period. Track quality, adoption and attributable benefits alongside labor time and ongoing model costs. Targets and acceptance thresholds are agreed during discovery; financial or operational results are not guaranteed.\u003c\/p\u003e\u003ch3\u003eInvestment and timing\u003c\/h3\u003e\u003cp\u003eEstimated engagement: USD $125,000–$300,000. The listed product price of $125,000 is the starting professional services fee, not a deposit or a guaranteed fixed quote. Indicative delivery: 8-16 weeks after prerequisites are available. Final scope, milestones and commercial terms are agreed in a statement of work. Model\/API usage, cloud infrastructure, hardware, third-party licenses, taxes, major data remediation and ongoing managed support are excluded unless expressly included.\u003c\/p\u003e\u003ch3\u003eGetting started\u003c\/h3\u003e\u003cp\u003eProvide a named process owner, authorized access to representative data, integration documentation and operator time for review. Regulated or sensitive workflows require applicable privacy and security approvals before data access. The first working session confirms the use case, baseline, approval boundaries and the smallest valuable delivery scope.\u003c\/p\u003e","brand":"Corefinery","offers":[{"title":"Default Title","offer_id":68850845941954,"sku":"CF-AI-057","price":125000.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0798\/2101\/3186\/files\/Platinum_pathway_to_what_s_next-2.png?v=1794072948"},{"product_id":"nergy-and-utilities-capital-project-evidence-workbench","title":"Energy: Capital project evidence workbench","description":"\u003cp\u003eInfrastructure reviews depend on scattered documents. Corefinery works with your energy and utilities team to turn this bottleneck into a practical, measurable workflow. The engagement focuses on summarize project risks and assemble evidence packs rather than a standalone demonstration.\u003c\/p\u003e\u003ch3\u003eWhat the engagement covers\u003c\/h3\u003e\u003cp\u003eOne production workflow; up to two integrations; one business unit. We map the current process with the people doing the work, identify decision rights and exceptions, and agree which actions require approval. The workflow is designed around existing systems and an accountable business owner.\u003c\/p\u003e\u003ch3\u003eWorkflow and data\u003c\/h3\u003e\u003cp\u003eSource inputs: Client-authorized source-system records; approved operating policies; representative task examples; integration documentation. Summarize project risks and assemble evidence packs. Every recommendation should retain its supporting context so an operator can inspect the evidence and correct incomplete or inaccurate output.\u003c\/p\u003e\u003ch3\u003eEngineering approach\u003c\/h3\u003e\u003cp\u003eDeterministic components: Milestone gates; budget reconciliation; change authority. These rules control calculations, eligibility and permitted actions. Non-deterministic AI handles interpretation, retrieval and draft recommendations; it does not replace the policy engine. Consequential actions require an authorized human decision.\u003c\/p\u003e\u003cp\u003eModel selection compares task quality, latency, privacy requirements and cost. Claude or OpenAI can support complex language tasks; a specialized small language model can handle narrower classification or retrieval when it meets the same evaluation criteria. Adaptation, quantization and private or edge deployment are considered only when supported by data rights, infrastructure and measured performance. Hosted vendor models remain subject to vendor deployment options and terms.\u003c\/p\u003e\u003ch3\u003eDeliverables\u003c\/h3\u003e\u003cul\u003e\n\u003cli\u003eCurrent-state workflow map, baseline metrics and prioritized requirements\u003c\/li\u003e\n\u003cli\u003eArchitecture, access boundaries, integration design and exception-handling plan\u003c\/li\u003e\n\u003cli\u003eRepresentative evaluation cases and an agreed acceptance scorecard\u003c\/li\u003e\n\u003cli\u003eWorking workflow software, scoped integrations and operator review queue\u003c\/li\u003e\n\u003cli\u003eMonitoring, audit events, deployment runbook, team training and source-code handover\u003c\/li\u003e\n\u003c\/ul\u003e\u003ch3\u003eDelivery and acceptance\u003c\/h3\u003e\u003cp\u003eForward deployed engineers work alongside your operators, with an AI engineer, integration engineer and shared quality and security support. The team reviews progress weekly, tests representative normal and exception cases, and documents limitations. Release follows owner acceptance, access-control testing and a demonstrated pause or rollback process.\u003c\/p\u003e\u003ch3\u003eHow value is measured\u003c\/h3\u003e\u003cp\u003ePrimary measures: Review preparation hours; unresolved exceptions. Establish a baseline before changes, then compare a controlled pilot or agreed pre\/post period. Track quality, adoption and attributable benefits alongside labor time and ongoing model costs. Targets and acceptance thresholds are agreed during discovery; financial or operational results are not guaranteed.\u003c\/p\u003e\u003ch3\u003eInvestment and timing\u003c\/h3\u003e\u003cp\u003eEstimated engagement: USD $150,000–$350,000. The listed product price of $150,000 is the starting professional services fee, not a deposit or a guaranteed fixed quote. Indicative delivery: 8-16 weeks after prerequisites are available. Final scope, milestones and commercial terms are agreed in a statement of work. Model\/API usage, cloud infrastructure, hardware, third-party licenses, taxes, major data remediation and ongoing managed support are excluded unless expressly included.\u003c\/p\u003e\u003ch3\u003eGetting started\u003c\/h3\u003e\u003cp\u003eProvide a named process owner, authorized access to representative data, integration documentation and operator time for review. Regulated or sensitive workflows require applicable privacy and security approvals before data access. The first working session confirms the use case, baseline, approval boundaries and the smallest valuable delivery scope.\u003c\/p\u003e","brand":"Corefinery","offers":[{"title":"Default Title","offer_id":68850846236866,"sku":"CF-AI-058","price":150000.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0798\/2101\/3186\/files\/Platinum_pathway_to_what_s_next-2.png?v=1794072948"},{"product_id":"nergy-and-utilities-private-slm-asset-knowledge-engine","title":"Energy: Private SLM asset knowledge engine","description":"\u003cp\u003eRemote teams need fast controlled procedure access. Corefinery works with your energy and utilities team to turn this bottleneck into a practical, measurable workflow. The engagement focuses on retrieve maintenance instructions using compact models rather than a standalone demonstration.\u003c\/p\u003e\u003ch3\u003eWhat the engagement covers\u003c\/h3\u003e\u003cp\u003eOne production workflow; up to two integrations; one business unit. We map the current process with the people doing the work, identify decision rights and exceptions, and agree which actions require approval. The workflow is designed around existing systems and an accountable business owner.\u003c\/p\u003e\u003ch3\u003eWorkflow and data\u003c\/h3\u003e\u003cp\u003eSource inputs: Client-authorized source-system records; approved operating policies; representative task examples; integration documentation. Retrieve maintenance instructions using compact models. Every recommendation should retain its supporting context so an operator can inspect the evidence and correct incomplete or inaccurate output.\u003c\/p\u003e\u003ch3\u003eEngineering approach\u003c\/h3\u003e\u003cp\u003eDeterministic components: Asset permissions; procedure versions; offline boundaries. These rules control calculations, eligibility and permitted actions. Non-deterministic AI handles interpretation, retrieval and draft recommendations; it does not replace the policy engine. Consequential actions require an authorized human decision.\u003c\/p\u003e\u003cp\u003eModel selection compares task quality, latency, privacy requirements and cost. Claude or OpenAI can support complex language tasks; a specialized small language model can handle narrower classification or retrieval when it meets the same evaluation criteria. Adaptation, quantization and private or edge deployment are considered only when supported by data rights, infrastructure and measured performance. Hosted vendor models remain subject to vendor deployment options and terms.\u003c\/p\u003e\u003ch3\u003eDeliverables\u003c\/h3\u003e\u003cul\u003e\n\u003cli\u003eCurrent-state workflow map, baseline metrics and prioritized requirements\u003c\/li\u003e\n\u003cli\u003eArchitecture, access boundaries, integration design and exception-handling plan\u003c\/li\u003e\n\u003cli\u003eRepresentative evaluation cases and an agreed acceptance scorecard\u003c\/li\u003e\n\u003cli\u003eWorking workflow software, scoped integrations and operator review queue\u003c\/li\u003e\n\u003cli\u003eMonitoring, audit events, deployment runbook, team training and source-code handover\u003c\/li\u003e\n\u003c\/ul\u003e\u003ch3\u003eDelivery and acceptance\u003c\/h3\u003e\u003cp\u003eForward deployed engineers work alongside your operators, with an AI engineer, integration engineer and shared quality and security support. The team reviews progress weekly, tests representative normal and exception cases, and documents limitations. Release follows owner acceptance, access-control testing and a demonstrated pause or rollback process.\u003c\/p\u003e\u003ch3\u003eHow value is measured\u003c\/h3\u003e\u003cp\u003ePrimary measures: Response latency; cited-answer accuracy. Establish a baseline before changes, then compare a controlled pilot or agreed pre\/post period. Track quality, adoption and attributable benefits alongside labor time and ongoing model costs. Targets and acceptance thresholds are agreed during discovery; financial or operational results are not guaranteed.\u003c\/p\u003e\u003ch3\u003eInvestment and timing\u003c\/h3\u003e\u003cp\u003eEstimated engagement: USD $175,000–$400,000. The listed product price of $175,000 is the starting professional services fee, not a deposit or a guaranteed fixed quote. Indicative delivery: 8-16 weeks after prerequisites are available. Final scope, milestones and commercial terms are agreed in a statement of work. Model\/API usage, cloud infrastructure, hardware, third-party licenses, taxes, major data remediation and ongoing managed support are excluded unless expressly included.\u003c\/p\u003e\u003ch3\u003eGetting started\u003c\/h3\u003e\u003cp\u003eProvide a named process owner, authorized access to representative data, integration documentation and operator time for review. Regulated or sensitive workflows require applicable privacy and security approvals before data access. The first working session confirms the use case, baseline, approval boundaries and the smallest valuable delivery scope.\u003c\/p\u003e","brand":"Corefinery","offers":[{"title":"Default Title","offer_id":68850849611970,"sku":"CF-AI-059","price":175000.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0798\/2101\/3186\/files\/Platinum_pathway_to_what_s_next-2.png?v=1794072948"},{"product_id":"nergy-and-utilities-enterprise-ai-workflow-transformation","title":"Energy: Enterprise AI workflow transformation","description":"\u003cp\u003eDisconnected workflows limit enterprise value in energy and utilities. Corefinery works with your energy and utilities 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":68850849677506,"sku":"CF-AI-060","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":"energy-renewable-project-diligence-desk","title":"Energy: Renewable project diligence desk","description":"\u003cp\u003eProject teams reconcile fragmented technical evidence. Corefinery works with your energy and utilities team to turn this bottleneck into a practical, measurable workflow. The engagement focuses on summarize diligence gaps and draft decision briefs rather than a standalone demonstration.\u003c\/p\u003e\u003ch3\u003eWhat the engagement covers\u003c\/h3\u003e\u003cp\u003eOne prioritized production workflow; up to two source-system integrations; one business unit; staged operator rollout. We map the current process with the people doing the work, identify decision rights and exceptions, and agree which actions require approval. The workflow is designed around existing systems and an accountable business owner.\u003c\/p\u003e\u003ch3\u003eWorkflow and data\u003c\/h3\u003e\u003cp\u003eSource inputs: Project documents; permits; generation assumptions. Summarize diligence gaps and draft decision briefs. Every recommendation should retain its supporting context so an operator can inspect the evidence and correct incomplete or inaccurate output.\u003c\/p\u003e\u003ch3\u003eEngineering approach\u003c\/h3\u003e\u003cp\u003eDeterministic components: Assumption lineage; milestone checks; 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: Diligence time; evidence completeness. Establish a baseline before changes, then compare a controlled pilot or agreed pre\/post period. Track quality, adoption and attributable benefits alongside labor time and ongoing model costs. Targets and acceptance thresholds are agreed during discovery; financial or operational results are not guaranteed.\u003c\/p\u003e\u003ch3\u003eInvestment and timing\u003c\/h3\u003e\u003cp\u003eEstimated engagement: USD $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":68861977493698,"sku":"CF-AI-151","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":"energy-vegetation-management-planner","title":"Energy: Vegetation management planner","description":"\u003cp\u003eInspection findings do not translate into efficient work plans. Corefinery works with your energy and utilities team to turn this bottleneck into a practical, measurable workflow. The engagement focuses on classify observations and propose work 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: Inspection records; asset locations; crew schedules. Classify observations and propose work 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: Clearance rules; access permissions; crew qualifications. These rules control calculations, eligibility and permitted actions. Non-deterministic AI handles interpretation, retrieval and draft recommendations; it does not replace the policy engine. Consequential actions require an authorized human decision.\u003c\/p\u003e\u003cp\u003eModel selection compares task quality, latency, privacy requirements and cost. Claude or OpenAI can support complex language tasks; a specialized small language model can handle narrower classification or retrieval when it meets the same evaluation criteria. Adaptation, quantization and private or edge deployment are considered only when supported by data rights, infrastructure and measured performance. Hosted vendor models remain subject to vendor deployment options and terms.\u003c\/p\u003e\u003ch3\u003eDeliverables\u003c\/h3\u003e\u003cul\u003e\n\u003cli\u003eCurrent-state workflow map, baseline metrics and prioritized requirements\u003c\/li\u003e\n\u003cli\u003eArchitecture, access boundaries, integration design and exception-handling plan\u003c\/li\u003e\n\u003cli\u003eRepresentative evaluation cases and an agreed acceptance scorecard\u003c\/li\u003e\n\u003cli\u003eWorking workflow software, scoped integrations and operator review queue\u003c\/li\u003e\n\u003cli\u003eMonitoring, audit events, deployment runbook, team training and source-code handover\u003c\/li\u003e\n\u003c\/ul\u003e\u003ch3\u003eDelivery and acceptance\u003c\/h3\u003e\u003cp\u003eForward deployed engineers work alongside your operators, with an AI engineer, integration engineer and shared quality and security support. The team reviews progress weekly, tests representative normal and exception cases, and documents limitations. Release follows owner acceptance, access-control testing and a demonstrated pause or rollback process.\u003c\/p\u003e\u003ch3\u003eHow value is measured\u003c\/h3\u003e\u003cp\u003ePrimary measures: Planning hours; overdue inspections. 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":68861977526466,"sku":"CF-AI-152","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":"energy-meter-exception-intelligence","title":"Energy: Meter exception intelligence","description":"\u003cp\u003eMeter anomalies generate repetitive manual investigation. Corefinery works with your energy and utilities team to turn this bottleneck into a practical, measurable workflow. The engagement focuses on summarize anomalies and suggest investigation paths rather than a standalone demonstration.\u003c\/p\u003e\u003ch3\u003eWhat the engagement covers\u003c\/h3\u003e\u003cp\u003eOne prioritized production workflow; up to two source-system integrations; one business unit; staged operator rollout. We map the current process with the people doing the work, identify decision rights and exceptions, and agree which actions require approval. The workflow is designed around existing systems and an accountable business owner.\u003c\/p\u003e\u003ch3\u003eWorkflow and data\u003c\/h3\u003e\u003cp\u003eSource inputs: Meter events; consumption histories; service records. Summarize anomalies and suggest investigation paths. Every recommendation should retain its supporting context so an operator can inspect the evidence and correct incomplete or inaccurate output.\u003c\/p\u003e\u003ch3\u003eEngineering approach\u003c\/h3\u003e\u003cp\u003eDeterministic components: Read validation; tariff checks; adjustment 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: Exception resolution; repeat truck rolls. 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":68861977624770,"sku":"CF-AI-153","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":"energy-utility-regulatory-filing-studio","title":"Energy: Utility regulatory filing studio","description":"\u003cp\u003eFiling teams manually assemble operational narratives. Corefinery works with your energy and utilities team to turn this bottleneck into a practical, measurable workflow. The engagement focuses on draft source-linked narrative sections and evidence summaries rather than a standalone demonstration.\u003c\/p\u003e\u003ch3\u003eWhat the engagement covers\u003c\/h3\u003e\u003cp\u003eOne prioritized production workflow; up to two source-system integrations; one business unit; staged operator rollout. We map the current process with the people doing the work, identify decision rights and exceptions, and agree which actions require approval. The workflow is designed around existing systems and an accountable business owner.\u003c\/p\u003e\u003ch3\u003eWorkflow and data\u003c\/h3\u003e\u003cp\u003eSource inputs: Approved reports; operational metrics; evidence archives. Draft source-linked narrative sections and evidence summaries. Every recommendation should retain its supporting context so an operator can inspect the evidence and correct incomplete or inaccurate output.\u003c\/p\u003e\u003ch3\u003eEngineering approach\u003c\/h3\u003e\u003cp\u003eDeterministic components: Filing checklist; numerical reconciliation; sign-off. These rules control calculations, eligibility and permitted actions. Non-deterministic AI handles interpretation, retrieval and draft recommendations; it does not replace the policy engine. Consequential actions require an authorized human decision.\u003c\/p\u003e\u003cp\u003eModel selection compares task quality, latency, privacy requirements and cost. Claude or OpenAI can support complex language tasks; a specialized small language model can handle narrower classification or retrieval when it meets the same evaluation criteria. Adaptation, quantization and private or edge deployment are considered only when supported by data rights, infrastructure and measured performance. Hosted vendor models remain subject to vendor deployment options and terms.\u003c\/p\u003e\u003ch3\u003eDeliverables\u003c\/h3\u003e\u003cul\u003e\n\u003cli\u003eCurrent-state workflow map, baseline metrics and prioritized requirements\u003c\/li\u003e\n\u003cli\u003eArchitecture, access boundaries, integration design and exception-handling plan\u003c\/li\u003e\n\u003cli\u003eRepresentative evaluation cases and an agreed acceptance scorecard\u003c\/li\u003e\n\u003cli\u003eWorking workflow software, scoped integrations and operator review queue\u003c\/li\u003e\n\u003cli\u003eMonitoring, audit events, deployment runbook, team training and source-code handover\u003c\/li\u003e\n\u003c\/ul\u003e\u003ch3\u003eDelivery and acceptance\u003c\/h3\u003e\u003cp\u003eForward deployed engineers work alongside your operators, with an AI engineer, integration engineer and shared quality and security support. The team reviews progress weekly, tests representative normal and exception cases, and documents limitations. Release follows owner acceptance, access-control testing and a demonstrated pause or rollback process.\u003c\/p\u003e\u003ch3\u003eHow value is measured\u003c\/h3\u003e\u003cp\u003ePrimary measures: Preparation hours; reviewer corrections. 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":68861978280130,"sku":"CF-AI-154","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":"energy-distributed-energy-service-coordinator","title":"Energy: Distributed energy service coordinator","description":"\u003cp\u003eDistributed asset requests are difficult to route. Corefinery works with your energy and utilities team to turn this bottleneck into a practical, measurable workflow. The engagement focuses on interpret requests and draft service coordination plans 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: Service requests; asset registries; maintenance calendars. Interpret requests and draft service coordination plans. Every recommendation should retain its supporting context so an operator can inspect the evidence and correct incomplete or inaccurate output.\u003c\/p\u003e\u003ch3\u003eEngineering approach\u003c\/h3\u003e\u003cp\u003eDeterministic components: Asset permissions; service windows; dispatch approval. These rules control calculations, eligibility and permitted actions. Non-deterministic AI handles interpretation, retrieval and draft recommendations; it does not replace the policy engine. Consequential actions require an authorized human decision.\u003c\/p\u003e\u003cp\u003eModel selection compares task quality, latency, privacy requirements and cost. Claude or OpenAI can support complex language tasks; a specialized small language model can handle narrower classification or retrieval when it meets the same evaluation criteria. Adaptation, quantization and private or edge deployment are considered only when supported by data rights, infrastructure and measured performance. Hosted vendor models remain subject to vendor deployment options and terms.\u003c\/p\u003e\u003ch3\u003eDeliverables\u003c\/h3\u003e\u003cul\u003e\n\u003cli\u003eCurrent-state workflow map, baseline metrics and prioritized requirements\u003c\/li\u003e\n\u003cli\u003eArchitecture, access boundaries, integration design and exception-handling plan\u003c\/li\u003e\n\u003cli\u003eRepresentative evaluation cases and an agreed acceptance scorecard\u003c\/li\u003e\n\u003cli\u003eWorking workflow software, scoped integrations and operator review queue\u003c\/li\u003e\n\u003cli\u003eMonitoring, audit events, deployment runbook, team training and source-code handover\u003c\/li\u003e\n\u003c\/ul\u003e\u003ch3\u003eDelivery and acceptance\u003c\/h3\u003e\u003cp\u003eForward deployed engineers work alongside your operators, with an AI engineer, integration engineer and shared quality and security support. The team reviews progress weekly, tests representative normal and exception cases, and documents limitations. Release follows owner acceptance, access-control testing and a demonstrated pause or rollback process.\u003c\/p\u003e\u003ch3\u003eHow value is measured\u003c\/h3\u003e\u003cp\u003ePrimary measures: Routing time; completion rate. Establish a baseline before changes, then compare a controlled pilot or agreed pre\/post period. Track quality, adoption and attributable benefits alongside labor time and ongoing model costs. Targets and acceptance thresholds are agreed during discovery; financial or operational results are not guaranteed.\u003c\/p\u003e\u003ch3\u003eInvestment and timing\u003c\/h3\u003e\u003cp\u003eEstimated engagement: USD $150,000–$350,000. The listed product price of $150,000 is the starting professional services fee, not a deposit or a guaranteed fixed quote. Indicative delivery: 8-20 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":68861978902722,"sku":"CF-AI-155","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":"energy-energy-contract-obligation-monitor","title":"Energy: Energy contract obligation monitor","description":"\u003cp\u003eContract obligations are missed across document versions. Corefinery works with your energy and utilities team to turn this bottleneck into a practical, measurable workflow. The engagement focuses on extract obligations and draft exception summaries 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: Supply contracts; amendments; obligation calendars. Extract obligations and draft exception summaries. Every recommendation should retain its supporting context so an operator can inspect the evidence and correct incomplete or inaccurate output.\u003c\/p\u003e\u003ch3\u003eEngineering approach\u003c\/h3\u003e\u003cp\u003eDeterministic components: Date calculations; volume checks; approval matrix. These rules control calculations, eligibility and permitted actions. Non-deterministic AI handles interpretation, retrieval and draft recommendations; it does not replace the policy engine. Consequential actions require an authorized human decision.\u003c\/p\u003e\u003cp\u003eModel selection compares task quality, latency, privacy requirements and cost. Claude or OpenAI can support complex language tasks; a specialized small language model can handle narrower classification or retrieval when it meets the same evaluation criteria. Adaptation, quantization and private or edge deployment are considered only when supported by data rights, infrastructure and measured performance. Hosted vendor models remain subject to vendor deployment options and terms.\u003c\/p\u003e\u003ch3\u003eDeliverables\u003c\/h3\u003e\u003cul\u003e\n\u003cli\u003eCurrent-state workflow map, baseline metrics and prioritized requirements\u003c\/li\u003e\n\u003cli\u003eArchitecture, access boundaries, integration design and exception-handling plan\u003c\/li\u003e\n\u003cli\u003eRepresentative evaluation cases and an agreed acceptance scorecard\u003c\/li\u003e\n\u003cli\u003eWorking workflow software, scoped integrations and operator review queue\u003c\/li\u003e\n\u003cli\u003eMonitoring, audit events, deployment runbook, team training and source-code handover\u003c\/li\u003e\n\u003c\/ul\u003e\u003ch3\u003eDelivery and acceptance\u003c\/h3\u003e\u003cp\u003eForward deployed engineers work alongside your operators, with an AI engineer, integration engineer and shared quality and security support. The team reviews progress weekly, tests representative normal and exception cases, and documents limitations. Release follows owner acceptance, access-control testing and a demonstrated pause or rollback process.\u003c\/p\u003e\u003ch3\u003eHow value is measured\u003c\/h3\u003e\u003cp\u003ePrimary measures: Missed milestones; review hours. Establish a baseline before changes, then compare a controlled pilot or agreed pre\/post period. Track quality, adoption and attributable benefits alongside labor time and ongoing model costs. Targets and acceptance thresholds are agreed during discovery; financial or operational results are not guaranteed.\u003c\/p\u003e\u003ch3\u003eInvestment and timing\u003c\/h3\u003e\u003cp\u003eEstimated engagement: USD $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":68861979525314,"sku":"CF-AI-156","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":"energy-utility-resilience-exercise-studio","title":"Energy: Utility resilience exercise studio","description":"\u003cp\u003eTeams lack realistic cross-functional practice. Corefinery works with your energy and utilities team to turn this bottleneck into a practical, measurable workflow. The engagement focuses on generate tabletop scenarios and summarize response gaps rather than a standalone demonstration.\u003c\/p\u003e\u003ch3\u003eWhat the engagement covers\u003c\/h3\u003e\u003cp\u003eOne prioritized production workflow; up to two source-system integrations; one business unit; staged operator rollout. We map the current process with the people doing the work, identify decision rights and exceptions, and agree which actions require approval. The workflow is designed around existing systems and an accountable business owner.\u003c\/p\u003e\u003ch3\u003eWorkflow and data\u003c\/h3\u003e\u003cp\u003eSource inputs: Approved incident playbooks; asset context; training plans. Generate tabletop scenarios and summarize response gaps. Every recommendation should retain its supporting context so an operator can inspect the evidence and correct incomplete or inaccurate output.\u003c\/p\u003e\u003ch3\u003eEngineering approach\u003c\/h3\u003e\u003cp\u003eDeterministic components: Scenario boundaries; exercise permissions; scoring rubric. These rules control calculations, eligibility and permitted actions. Non-deterministic AI handles interpretation, retrieval and draft recommendations; it does not replace the policy engine. Consequential actions require an authorized human decision.\u003c\/p\u003e\u003cp\u003eModel selection compares task quality, latency, privacy requirements and cost. Claude or OpenAI can support complex language tasks; a specialized small language model can handle narrower classification or retrieval when it meets the same evaluation criteria. Adaptation, quantization and private or edge deployment are considered only when supported by data rights, infrastructure and measured performance. Hosted vendor models remain subject to vendor deployment options and terms.\u003c\/p\u003e\u003ch3\u003eDeliverables\u003c\/h3\u003e\u003cul\u003e\n\u003cli\u003eCurrent-state workflow map, baseline metrics and prioritized requirements\u003c\/li\u003e\n\u003cli\u003eArchitecture, access boundaries, integration design and exception-handling plan\u003c\/li\u003e\n\u003cli\u003eRepresentative evaluation cases and an agreed acceptance scorecard\u003c\/li\u003e\n\u003cli\u003eWorking workflow software, scoped integrations and operator review queue\u003c\/li\u003e\n\u003cli\u003eMonitoring, audit events, deployment runbook, team training and source-code handover\u003c\/li\u003e\n\u003c\/ul\u003e\u003ch3\u003eDelivery and acceptance\u003c\/h3\u003e\u003cp\u003eForward deployed engineers work alongside your operators, with an AI engineer, integration engineer and shared quality and security support. The team reviews progress weekly, tests representative normal and exception cases, and documents limitations. Release follows owner acceptance, access-control testing and a demonstrated pause or rollback process.\u003c\/p\u003e\u003ch3\u003eHow value is measured\u003c\/h3\u003e\u003cp\u003ePrimary measures: Exercise completion; action closure. 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":68861979623618,"sku":"CF-AI-157","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":"energy-energy-procurement-intelligence","title":"Energy: Energy procurement intelligence","description":"\u003cp\u003eTeams struggle to compare complex supplier proposals. Corefinery works with your energy and utilities team to turn this bottleneck into a practical, measurable workflow. The engagement focuses on summarize proposal tradeoffs and draft clarification questions 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: Bids; usage profiles; contract terms. Summarize proposal tradeoffs and draft clarification questions. Every recommendation should retain its supporting context so an operator can inspect the evidence and correct incomplete or inaccurate output.\u003c\/p\u003e\u003ch3\u003eEngineering approach\u003c\/h3\u003e\u003cp\u003eDeterministic components: Unit normalization; price formulas; award approval. These rules control calculations, eligibility and permitted actions. Non-deterministic AI handles interpretation, retrieval and draft recommendations; it does not replace the policy engine. Consequential actions require an authorized human decision.\u003c\/p\u003e\u003cp\u003eModel selection compares task quality, latency, privacy requirements and cost. Claude or OpenAI can support complex language tasks; a specialized small language model can handle narrower classification or retrieval when it meets the same evaluation criteria. Adaptation, quantization and private or edge deployment are considered only when supported by data rights, infrastructure and measured performance. Hosted vendor models remain subject to vendor deployment options and terms.\u003c\/p\u003e\u003ch3\u003eDeliverables\u003c\/h3\u003e\u003cul\u003e\n\u003cli\u003eCurrent-state workflow map, baseline metrics and prioritized requirements\u003c\/li\u003e\n\u003cli\u003eArchitecture, access boundaries, integration design and exception-handling plan\u003c\/li\u003e\n\u003cli\u003eRepresentative evaluation cases and an agreed acceptance scorecard\u003c\/li\u003e\n\u003cli\u003eWorking workflow software, scoped integrations and operator review queue\u003c\/li\u003e\n\u003cli\u003eMonitoring, audit events, deployment runbook, team training and source-code handover\u003c\/li\u003e\n\u003c\/ul\u003e\u003ch3\u003eDelivery and acceptance\u003c\/h3\u003e\u003cp\u003eForward deployed engineers work alongside your operators, with an AI engineer, integration engineer and shared quality and security support. The team reviews progress weekly, tests representative normal and exception cases, and documents limitations. Release follows owner acceptance, access-control testing and a demonstrated pause or rollback process.\u003c\/p\u003e\u003ch3\u003eHow value is measured\u003c\/h3\u003e\u003cp\u003ePrimary measures: Sourcing hours; evaluated cost. Establish a baseline before changes, then compare a controlled pilot or agreed pre\/post period. Track quality, adoption and attributable benefits alongside labor time and ongoing model costs. Targets and acceptance thresholds are agreed during discovery; financial or operational results are not guaranteed.\u003c\/p\u003e\u003ch3\u003eInvestment and timing\u003c\/h3\u003e\u003cp\u003eEstimated engagement: USD $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":68861980901570,"sku":"CF-AI-158","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":"energy-claude-enterprise-workflow-deployment","title":"Energy: Claude enterprise workflow deployment","description":"\u003cp\u003eTeams need a governed path from Claude experimentation to one usable industry workflow. Corefinery works with your energy and utilities 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":68861981524162,"sku":"CF-AI-159","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":"energy-openai-production-agent-engineering","title":"Energy: OpenAI production agent engineering","description":"\u003cp\u003ePromising OpenAI prototypes lack production integration and accountable operating controls. Corefinery works with your energy and utilities 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":68861981622466,"sku":"CF-AI-160","price":125000.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0798\/2101\/3186\/files\/Platinum_pathway_to_what_s_next-2.png?v=1794072948"}],"url":"https:\/\/corefinery.com\/collections\/energy-utilities.oembed","provider":"Corefinery","version":"1.0","type":"link"}