Manufacturing: Manufacturing yield intelligence

Teams struggle to explain variation in output. Corefinery works with your manufacturing and industrial team to turn this bottleneck into a practical, measurable workflow. The engagement focuses on summarize yield variation and propose investigation hypotheses rather than a standalone demonstration.

What the engagement covers

One 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.

Workflow and data

Source inputs: Batch records; process events; quality results. Summarize yield variation and propose investigation hypotheses. Every recommendation should retain its supporting context so an operator can inspect the evidence and correct incomplete or inaccurate output.

Engineering approach

Deterministic components: Specification ranges; batch genealogy; 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.

Model 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.

Deliverables

• Current-state workflow map, baseline metrics and prioritized requirements
• Architecture, access boundaries, integration design and exception-handling plan
• Representative evaluation cases and an agreed acceptance scorecard
• Working workflow software, scoped integrations and operator review queue
• Monitoring, audit events, deployment runbook, team training and source-code handover

Delivery and acceptance

Forward 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.

How value is measured

Primary measures: Investigation time; first-pass yield. 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.

Investment and timing

Estimated 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.

Getting started

Provide 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.