← Home · Ventura vocation ideas

Light Industrial / Manufacturing / Distribution Implementation

Recommended Tech Stack

Layer	Recommendation	Reason
Language	Python	Best default velocity for extraction-heavy manufacturing workflows: stronger document parsing libraries, data tooling, and mature LLM ecosystem. TypeScript is fine if preferred, but Python is the default pick.
Workflow engine	n8n self-hosted during consulting; Temporal or Inngest when productizing	n8n gives low-code speed and SMB connectors while preserving IP. Temporal/Inngest add durable retries, schedules, human-approval gates, and audit trails.
LLM gateway	LiteLLM	One OpenAI-shaped interface with per-task routing and provider swaps via config instead of code changes.
Agent loops	Pydantic AI or LangGraph only where needed	Most workflows here are pipelines with LLM steps plus human review, not autonomous agents. Avoid over-agentifying.
Document parsing	Hybrid: Reducto, LlamaParse, or Unstructured; Azure Document Intelligence or AWS Textract as alternates; vision LLM for drawings/spec photos	Hybrid parsing is non-negotiable: manufacturing docs include tables, line items, drawings, hand-marked specs, photos, and inconsistent PDFs.
Structured outputs	Pydantic / JSON Schema	Never trust free-text extraction for POs, invoices, quote line-items, specs, or CAPA packets.
Vector / RAG	Postgres + pgvector; embeddings routed through LiteLLM	Simplest stack that scales fine for SMBs. Embeddings can use voyage-3, OpenAI text-embedding-3, Cohere, or another provider without rewriting the app.
State / files	Postgres as source of truth; Supabase or RDS; S3 or Cloudflare R2 for raw documents	Keep workflow state, extracted entities, approvals, tenants, and audit records in Postgres. Store raw documents separately.
Auth / tenancy	Clerk or WorkOS + Postgres row-level security	Avoid building auth. WorkOS is attractive when B2B SSO/SAML becomes important.
Observability	Langfuse + structured logs in Axiom or Better Stack	Provider-agnostic LLM traces plus operational logs are essential for debugging extraction and review workflows.
Evals	Promptfoo or Inspect AI	Non-negotiable for PO/invoice extraction and quote line items; bad output costs real money.
UI	Next.js for review/approval screens; n8n forms during consulting	Fast path to human-in-the-loop review without overbuilding the first version.
Hosting	Render, Railway, or Fly.io	Good enough for SMB scale. Move to AWS or GCP only when a client mandates it.

Integration Moat

• ERP: NetSuite SuiteTalk, Acumatica REST/OData, Epicor Kinetic, Sage Intacct, SAP Business One.
• Accounting: QuickBooks Online, Xero, or Codat as a unified abstraction.
• Email: Microsoft Graph for Outlook, Gmail API.
• Files: SharePoint, Google Drive, Dropbox, Box.
• CRM: HubSpot for many SMBs; Salesforce when already present.

Per-Use-Case Implementation Mapping

Use case	Pipeline
RFQ intake → quote packet	Email or portal → Reducto/LlamaParse → spec match via pgvector → LLM draft/template fill → Next.js review → ERP/CRM writeback.
PO / invoice extraction	Email/upload → vision LLM + structured schema → validate against open orders → sync to QuickBooks Online or ERP → continuous eval suite.
Work-order status summaries	ERP pull → aggregate per customer → LLM draft → scheduled run through n8n or Temporal → email send after review rules.
Nonconformance / CAPA packets	Form/email intake → pull related work orders and prior NCs with pgvector → 8D template fill → quality-manager review queue.
Customer spec search	Upload → chunk/embed → pgvector with rich metadata such as customer, revision, and date → chat/search UI with source-page citations.

Deliberately Not in V1

• Kubernetes.
• Custom vector database.
• A broad “AI ops” SaaS.
• Multi-region architecture.
• Homegrown auth.

Created: 2026-05-10. Split from the local SMB parent page so implementation detail lives at drilldown level.