# Auditor Context — project preamble for fact extraction

This file is read by `auditor/fact_extractor.ts` and prepended to the
extract-facts prompt sent to llm_team. The goal: give the extractor +
verifier enough grounding to ground domain-specific facts instead of
marking them UNVERIFIABLE by default.

Keep this short (< 400 words). Verifier only reads the first ~4KB of
the prompt alongside the facts. Longer = noise, not signal.

Update when: a new Phase lands, a crate is added/removed, the project's
primary domain shifts (e.g. staffing → DevOps).

---

## What Lakehouse is

Lakehouse is a Rust-first data platform over S3-compatible object
storage. Primary use: a staffing company ingesting legacy CRM data for
AI-powered worker matching, contract fulfillment, and playbook-driven
coordination.

Architecture: 13 Rust crates + a Python sidecar (Ollama) + TypeScript
sub-agents (auditor, scrum_master, bot). Runs on a single server
(Nvidia A4000, 128GB RAM). All services on localhost: gateway :3100,
sidecar :3200, UI :3300, MCP :3700, observer :3800, MinIO :9000.

## Key crates (each maps to a responsibility)

- **shared** — types, Arrow helpers, PII utilities, SecretsProvider
- **proto** — gRPC definitions
- **storaged** — S3/MinIO I/O, AppendLog, ErrorJournal
- **catalogd** — metadata authority (manifests, views, tombstones)
- **queryd** — DataFusion SQL, MemTable cache, compaction
- **ingestd** — CSV/JSON/PDF/Postgres/MySQL ingest
- **vectord** — embeddings, HNSW index, **playbook_memory meta-index** (Phase 19+)
- **vectord-lance** — Lance 4.0 firewall crate (separate Arrow version)
- **journald** — append-only mutation event log
- **aibridge** — Rust↔Python sidecar bridge, context budget + continuation
- **gateway** — Axum HTTP :3100 + gRPC :3101 (Phase 38+ adds /v1/chat)
- **ui** — Dioxus WASM (stale, pre-Phase-9)
- **lance-bench** — standalone benchmark

## Current architectural direction (Phase 38-44)

Universal AI Control Plane: a `/v1/chat` OpenAI-compatible API that
routes all LLM traffic through one layer for token accounting + provider
fallback. Truth Layer + Validation Pipeline enforce staffing-domain
invariants (worker eligibility, PII, contract rules). The Auditor
(Phase A of cohesion plan) hard-blocks PR merges on placeholder code.

## Auditor sub-agent role

`auditor/` (TypeScript, Bun runtime) polls Gitea every 90s for open PRs.
For each fresh head SHA it runs 4 checks in parallel: static (grep-style
placeholder detection), dynamic (runs the hybrid fixture), inference
(gpt-oss:120b cloud review with N=3 consensus + qwen3-coder:480b
tie-breaker), and kb_query (reads `data/_kb/*.jsonl` for prior evidence).
Verdicts post to Gitea as commit status + review comment. Findings
append to `data/_kb/audit_lessons.jsonl` (path-agnostic signatures for
dedup). Curated scratchpads from tree-split get routed through this
extract-facts pipeline to populate `audit_facts.jsonl` — which is what
you (the extractor) are currently producing.

## Things that are NOT the auditor

- The LLM Team UI at `/root/llm_team_ui.py` (devop.live:5000) — a separate product for human-facing multi-model experimentation
- The scrum_master pipeline at `tests/real-world/scrum_master_pipeline.ts` — reviews files, not claims
- The bot at `bot/` — will apply fixes, doesn't audit