How the briefs are made

Not “an AI wrote this.” A retrieval pipeline, an automated quality gate, and human review — built and run end-to-end. Here's the whole machine.

The pipeline

curated sources  (transcripts · engineering docs · papers)
        │
        ▼
   RAG pipeline
   ├── BM25 + TF-IDF + RRF retrieval
   ├── cross-encoder reranking
   ├── knowledge-graph entity linking
   └── multi-angle intent detection
        │
        ▼
   LLM synthesis  (Claude / local models)
        │
        ▼
   quality gate  (automated checks)
   ├── quote-fidelity verification
   ├── banned-phrase detection
   ├── source-relevance validation
   └── structural compliance
        │
        ▼
   human review  →  publish

The quality gate

Every brief passes an automated gate before publish: quote fidelity is checked against the source text (fuzzy match), fabricated citations are stripped, cheerleading/buzzword phrases are flagged, and section structure is enforced. Current score: 99/100. Retrieval: 20/20 (95.6%, grade A).

The sources

The corpus is 69,731+ curated chunks — engineering talks, documentation, and papers — retrieved per query with a hybrid BM25 + vector + RRF ranker, then reranked. Every brief cites the specific sources it drew from, with direct links.

The whole system runs on a 3-node K3s cluster in a home office — no cloud compute. GitOps deploys (push → Argo CD → cluster); Cloudflare Tunnel + Zero Trust at the edge.