Most RAG customer support and document search implementations are a single vector query call and a hope. We build retrieval systems that hold up under real query distributions, with eval harnesses that prove accuracy before anything ships.
Who this is for
Mid-market to enterprise teams with 10,000 or more documents and a real accuracy requirement. Engineering leaders who attempted in-house RAG, hit the long-tail problem, and need a team to fix it properly.
What you get
- A chunking strategy tuned to your corpus, whether semantic, structural, or hybrid, based on what your document types actually need.
- Hybrid retrieval combining dense and sparse search, plus rerankers to push the most relevant results to the top.
- An evaluation suite measuring recall at K, MRR, and end-to-end answer quality.
- Versioned indexes with rebuild pipelines so you can ingest new documents without rebuilding from scratch.
- Citations in every answer so users can trace claims back to source documents.
How we work on this
We start with a corpus audit and build the eval set before writing retrieval code. Then we design the retrieval pipeline, build end-to-end, and run ongoing tuning against the eval set until accuracy targets are met.
Tech stack
LlamaIndex for ingestion. LangChain for the chain logic. Pinecone or Qdrant for vector storage. Cohere or BGE for reranking.
When this is the wrong choice
If your corpus is under 100 documents, long-context models such as Claude Opus 4.7 with its extended context window may outperform any RAG system. We benchmark both approaches before building.
Pricing
$8,000 to $15,000 for clean, well-structured corpora with a single document type. $15,000 to $45,000 for messy, multi-source, or multimodal corpora requiring custom ingestion pipelines.
FAQ
How do you prevent hallucinations? Every answer is grounded in retrieved documents. The system prompt requires the model to cite sources and say “I do not know” when retrieval returns nothing relevant. We measure this behavior against the eval set.
Do answers include citations? Yes. Each answer references the specific document or section it drew from, so users can verify the source.
How do you handle freshness when documents update? The rebuild pipeline re-ingests changed documents on a schedule you control. Incremental indexing keeps costs down for large corpora.
Can the system handle multiple tenants with separate document sets? Yes. We build tenant-scoped indexes from the start when your product requires it.
What security model do you use for the document corpus? Documents stay inside your infrastructure. The retrieval layer runs in your VPC or cloud account. We do not send your documents to third-party services unless you explicitly choose hosted vector storage.
How do you measure eval quality? We define a golden set of 50 to 200 question-answer pairs from your real user queries, measure recall at K and end-to-end answer accuracy, and publish results in CI on every change to the retrieval pipeline.