AI · 2024-2026
AI Automation Platform | Case Study
A private AI layer for companies: ingest the knowledge, ground the answers, act through tools, log everything.
00 · CONTEXT
A private AI layer for companies: ingest internal knowledge from wikis, tickets and PDFs, ground every answer in retrieved evidence, act through permissioned tools, and log the lot.
01 · PROBLEM
Teams had institutional knowledge scattered across wikis, tickets, PDFs and people's heads, and generic chatbots hallucinated.
02 · STRATEGY
Retrieval first, generation second. Every answer cites a source; every action passes through a permission gate.
03 · DESIGN
Answers rendered as evidence: citation cards, confidence bands, and one-click escalation to a human.
04 · ENGINEERING
Hybrid vector + keyword retrieval, chunking tuned per document class, evaluation harness gating every prompt change.
05 · CHALLENGES
Generic chatbots hallucinate
Confidently wrong answers about policy or pricing cost more trust than they save time.
Knowledge lives in incompatible formats
Wiki pages, ticket threads, scanned PDFs and spreadsheets each need different chunking to retrieve well.
Prompt changes silently regress
Without evaluation, every improvement risked breaking three answers nobody was watching.
06 · PROCESS
Hybrid vector plus keyword retrieval, tuned per document class, with generation restricted to what was actually retrieved.
Answers render as citation cards with confidence bands and one-click escalation to a human, so users can verify instantly.
Tool calls pass through the same permission model as the user, so the assistant can never see or do more than the person asking.
A graded eval set runs on every prompt, chunking or model change; regressions block the release.
07 · THE SOLUTION
- Connectors for wikis, ticketing, drives and PDFs
- Hybrid retrieval with per-class chunking strategies
- Cited answers with confidence bands
- Permission-scoped tool calling
- Human escalation with full conversation context
- Evaluation harness and regression dashboard
- Complete audit log of every AI action
08 · ARCHITECTURE
- Python ingestion and retrieval services
- PostgreSQL with pgvector for hybrid search
- LLM gateway with model routing and cost controls
- Next.js console for chat, sources and admin
09 · RESULTS
- 62% of support tickets deflected
- 94% grounded-answer accuracy on the eval set
- Full audit log of every AI action taken
10 · WHAT IT TAUGHT US
Trust came from citations and evals, not from a bigger model. Once answers were checkable, deflection followed on its own.
11 · STACK
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