RAG that answers correctly — and proves it.
From document chunking strategy to reranking to citation-grounded outputs, we build RAG systems engineered to the quality bar your business actually needs — with the eval harness to prove it.
Most RAG systems hallucinate. Often.
The default RAG tutorial works on a demo dataset. Build that same pipeline against your real corpus — messy PDFs, tables, scanned documents, multi-language content — and answer quality drops below the bar your business needs.
Chunks split a sentence in half. The embedding model doesn't understand your domain vocabulary. Top-5 retrieval misses the document with the answer. The LLM confabulates a citation that doesn't exist. And no one has built a golden dataset to even measure the regression. Production RAG is not a vibe — it's a measured system.
The eight-stage RAG pipeline we deploy.
The difference between a 60%-accurate RAG and a 90%-accurate RAG is rarely the LLM — it's how each stage is tuned.
Document parsing
Layout-aware parsers (Unstructured, Docling, Textract) handle real-world PDFs, tables, and scans.
Chunking
Structure-aware, semantic chunking — not naive fixed-size splitting.
Embedding
Benchmarked on your data: text-embedding-3, Cohere, BGE-M3, Voyage, etc.
Vector store
pgvector, Pinecone, Weaviate, Qdrant, Milvus — chosen for your scale and stack.
Hybrid retrieval
Dense + BM25 sparse retrieval, combined via reciprocal rank fusion.
Reranking
Cross-encoder reranker (Cohere Rerank, BGE, ColBERT) lifts precision@3 by 15–30 points.
Citation-grounded generation
Strict prompt + structured output: answer only from context, cite the source, refuse if unsupported.
Guardrails + eval
Citation verification, factuality scoring, continuous eval against a golden dataset.
What grounded retrieval actually looks like.
Hybrid retrieval, a reranker, and a citation-strict prompt — the same shape we ship to production.
def answer(question: str) -> Answer: # 1. Hybrid retrieval: dense + sparse, fused dense = vector_store.search(embed(question), k=40) sparse = bm25.search(question, k=40) candidates = reciprocal_rank_fusion(dense, sparse) # 2. Rerank — the single biggest precision lever top = reranker.rank(question, candidates)[:6] # 3. Generate strictly from retrieved context return llm.generate( system="Answer ONLY from context. Cite sources. " "If unsupported, say you don't know.", context=top, question=question, )Every claim is grounded in a retrieved source and the citation is verified against the document before the answer is returned.
We measure quality before we ship it.
The difference between a demo and a production RAG system is a golden dataset and an automated eval that runs on every change.
We build that harness first — so prompt tweaks, model swaps, and chunking changes are measured against real questions, and a regression blocks the deploy instead of reaching your users.
See how we engageSix failure modes that kill RAG quality.
| Failure mode | What we do about it |
|---|---|
| Chunks split a sentence or table | Layout-aware parsing; recursive splitting; tables as structured chunks |
| Right document isn't in top-k | Hybrid (dense + BM25) + RRF; query rewriting; higher initial-k + reranking |
| Reranker isn't there | Always deploy a reranker for production; measure precision@3 with vs. without |
| LLM hallucinates beyond context | Strict 'answer only from context' prompt; structured output; per-claim citations |
| Citations point to nothing | Citation verification — every cited quote checked against the source |
| System slowly degrades | Golden dataset + automated eval on every change; regression blocks deploy |
How RAG projects engage with us.
- Corpus audit + embedding benchmark
- Vector DB recommendation + TCO
- Golden-dataset starter
- Parsing → chunking → embedding → retrieval
- Hybrid + reranking + citation grounding
- Full eval harness + observability + 30-day support
- Weekly eval review + dataset expansion
- Embedding-model migrations
- Chunking iterations on real queries
Common questions.
When is RAG the wrong answer?
How big should my chunks be?
Which vector database?
Do we need a reranker?
Hybrid vs. pure-vector search?
What about hallucinated citations?
Bring us your corpus.
A 30-minute call: document mix, query patterns, quality bar, latency target. We'll tell you honestly whether RAG is the right pattern and what kind of build it would take.