For Companies with internal docs, knowledge bases, support content, or proprietary corpora

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.

Get a quotefrom $10,000 · USD

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.

Ready to scope your RAG implementation?

Email me what you're building. I'll respond with a quote, scope questions, and a clear next step.