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Field Issue Triage + Service Knowledge Capture Implementation
Use case: implementation detail for the Manufacturing / Semicap Workflow Automation opportunity “Field Issue Triage + Service Knowledge Capture.”
Automation focus: Case intake, similar-case retrieval, diagnostic checklist generation, likely-root-cause hypotheses, escalation packet drafting, and captured service knowledge feedback loops.
Primary buyers: Service, customer support, product engineering, field applications, sustaining engineering.
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
- Service/ticketing: ServiceNow Developer, Zendesk API, Jira Cloud REST API, Dynamics 365 Customer Service.
- Knowledge/files: Confluence REST API, SharePoint, Google Drive, Box.
- Logs/telemetry: Databricks, Snowflake developer docs, Elastic Stack, Splunk docs.
- Comms: Microsoft Graph, Slack API.
Per-Use-Case Implementation Mapping
| Use case | Pipeline |
|---|---|
| Case intake | Ticket/email/log bundle → normalize product/site/config/severity → structured triage record → missing-info checklist. |
| Similar-case retrieval | Issue summary + error codes + config metadata → pgvector search over closed cases/service notes → cited similar cases. |
| Diagnostic checklist | Symptoms + retrieved cases + manuals → LLM draft checklist → service engineer review. |
| Escalation packet | Case artifacts + attempted steps + related known issues → engineering escalation packet → approval → ticket update. |
| Knowledge capture loop | Resolved case → accepted root cause/fix → sanitized knowledge note → review → indexed knowledge base. |
Guardrails / Not in V1
- No autonomous root-cause declaration; present hypotheses with evidence.
- Always cite source cases, logs, manuals, or service notes.
- Human approval before customer-facing recommendations or engineering escalation.
- Separate customer/site-sensitive records with tenant and access controls.
- 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.