Your knowledge, retrievable — at the accuracy you need.
Not 'we sprinkled embeddings on it.' Engineered retrieval pipelines.
RAG is easy to demo and hard to ship. The default LangChain tutorial misses 30-50% of relevant chunks on real corpora. We design retrieval pipelines that hit your accuracy targets — with hybrid search, re-rankers, query rewriting, and evals built in.
Engineering, not slides.
Hybrid Search
BM25 + dense vectors + metadata filters. Combine with re-rankers when single-strategy recall falls short.
Smart Chunking
Document structure-aware chunking — headings, tables, code blocks. Per-document-type strategies.
Query Rewriting
User questions rephrased into 2-3 retrieval queries automatically. HyDE, multi-query, decomposition — picked by use case.
Re-rankers
Cohere, Voyage, cross-encoders, LLM-as-judge re-ranking. Layered on top of retrieval for precision.
Retrieval Evals
Recall@K, MRR, hit-rate at scale. Synthetic eval-set generation when you don't have one yet.
Document Ingestion
PDFs, scans, tables, slides, web pages. OCR + structure extraction wired in for the messy stuff.
From idea to production.
Corpus audit
Sample queries, expected answers — we measure baseline retrieval before changing anything.
Eval-set construction
We build the eval set first. Improvements measured against it. No vibes-based iteration.
Pipeline tuning
Chunking → embedding → retrieval → re-ranking, each layer measured independently.
Cost & latency tuning
Caching, batch embeddings, smaller models where possible. Production cost in mind from day one.
Models & tools we reach for.
Common questions.
Do we need a vector database?
Often Postgres + pgvector is enough through medium scale. We pick what fits your existing stack, not what's fashionable.
How accurate can RAG get?
Depends entirely on your corpus and query distribution. We target measurable accuracy — usually 80-95% recall@10 on well-scoped corpora.
Can RAG replace fine-tuning?
For knowledge tasks, almost always. For style or behavior tuning, no. We help draw the line.
Let's scope it together.
Free 30-minute call. Bring your problem statement and current stack — we'll tell you honestly whether it's worth the build.