ECO / ECR Copilot Implementation

Use case: implementation detail for the Manufacturing / Semicap Workflow Automation opportunity “ECO / ECR Copilot.”

Automation focus: Change-request intake, affected-artifact discovery, impact analysis drafts, approval packets, missing-reviewer tracking, and ERP/MES/PLM handoff support.

Primary buyers: Engineering operations, manufacturing engineering, quality, PLM owners, configuration management.

V1 principle: read from existing systems, draft evidence-backed packets or summaries, route to humans for approval, and write back only after review. Do not replace PLM, QMS, ERP, MES, ticketing, or program systems of record.

Common Implementation Stack

Layer	Recommendation	Reason
Language	Python for extraction/RAG/workflows; TypeScript for review UI	Python has the best default ecosystem for document parsing, data transforms, evals, and LLM structured outputs.
Workflow engine	Temporal or Inngest; n8n for consulting pilots	Semicap/manufacturing workflows need durable retries, human gates, schedules, and audit trails.
LLM gateway	LiteLLM	Provider routing, model swaps, and one OpenAI-shaped interface for extraction, summarization, and drafting.
Structured outputs	Pydantic / JSON Schema	Required for ECO packets, triage fields, CAPA fields, readiness checklists, and status/risk artifacts.
Documents	Reducto, LlamaParse, Unstructured, Azure Document Intelligence, or AWS Textract	Manufacturing artifacts include tables, PDFs, drawings, specs, service notes, release notes, and scanned documents.
RAG/search	Postgres + pgvector	Good enough for workflow-scoped corpora with rich metadata: product, revision, customer, site, tool, serial number, issue type.
State/files	Postgres via Supabase or RDS; S3 or Cloudflare R2	Keep workflow state, approvals, source artifacts, extracted facts, and audit trails separate from raw documents.
Auth	Clerk or WorkOS + row-level security	B2B users will eventually need SSO/SAML and tenant isolation.
Observability	Langfuse + Axiom or Better Stack	Trace prompts, retrieved evidence, generated packets, reviewer decisions, exceptions, and eval failures.
Evals	Promptfoo or Inspect AI	Non-negotiable for high-cost workflows: wrong triage, missing affected docs, or hallucinated readiness creates real operational risk.
Review UI	Next.js	Engineers, quality managers, service leads, and program managers need a clear evidence-backed review/approval queue.

Integration Moat

PLM/PDM: PTC Windchill, Siemens Teamcenter, Autodesk Fusion Manage API, Dassault ENOVIA.
ERP/MES/QMS: NetSuite SuiteTalk, SAP S/4HANA Cloud docs, Plex Smart Manufacturing Platform, MasterControl QMS.
Engineering/project: Jira Cloud REST API, GitHub REST API, Azure DevOps REST API.
Documents/e-sign: SharePoint, DocuSign API.

Per-Use-Case Implementation Mapping

Use case	Pipeline
Change request intake	ECR form/email/Jira issue → classify type/urgency/product → structured change record.
Affected artifact discovery	Part/doc/software identifiers → PLM/PDM/doc search → affected BOMs/docs/configs/customers/revisions.
Impact analysis draft	Affected artifacts + prior changes + open issues → risk/impact draft → engineer review.
Approval packet	Impact draft + source citations + reviewer list + missing information → approval packet → PLM/Jira update.
Implementation handoff	Approved change → ERP/MES/QMS handoff checklist → effectivity/release notes draft → owner review.

Guardrails / Not in V1

Never approve or implement a change automatically.
Preserve exact revision/effectivity metadata and source citations.
Do not replace PLM as system of record.
Route ambiguous impact to configuration management or responsible engineer.
No Kubernetes, custom vector database, broad “AI ops” SaaS, autonomous approval, or replacement of enterprise systems in V1.

Created: 2026-05-10. Manufacturing / semicap workflow implementation drilldown. Confidence: medium; validate customer systems and data-access constraints before implementation.