RAG that retrieves the right chunk, every time.
Most RAG demos break in production. I build retrieval that works on real corpora — hybrid search, reranking, eval harnesses, and observability built in.
What's included
Production-grade RAG implementation that ships, not theater.
- Ingestion: PDFs, Notion, Google Docs, websites, Slack
- Smart chunking (semantic, structure-aware, table-aware)
- Embeddings: OpenAI, Cohere, Voyage, or self-hosted
- Hybrid search: pgvector + BM25 + reranker
- Eval harness with retrieval and answer-quality metrics
- Observability: traces, latency, cost per query
What you walk away with
Deliverables you keep — code, infrastructure, and the runbook.
- Deployed RAG service with API
- Eval dashboard + golden test set
- Re-indexing automation
- Per-query cost and latency budgets
Frequently asked
What sources can your RAG ingest?+
PDFs (with OCR), Notion, Google Docs, Confluence, websites (crawled), Slack/Discord exports, GitHub repos, and arbitrary CSVs. Custom connectors written as needed.
How do you measure RAG quality?+
I ship every RAG with an eval harness: retrieval precision/recall on a golden set, answer faithfulness measured by a judge model, and per-query latency and cost. You can see quality regress before it hits users.
When should I use pgvector vs Qdrant vs FAISS?+
Pgvector for ≤10M chunks and ops simplicity (one Postgres). Qdrant for larger or multi-tenant. FAISS for self-hosted on-device. I help pick based on your scale and ops appetite.
Does your RAG support multi-tenant isolation?+
Yes — tenant-scoped indexes with row-level security or per-tenant collections. Critical for B2B SaaS where one tenant must never see another's data.
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From $6,000Ready to scope your RAG implementation?
Email me what you're building. I'll respond with a quote, scope questions, and a clear next step.