RAG that answers correctly — and can prove where it got it.
Most RAG demos fall apart on the client's actual documents. We build retrieval that holds up on messy PDFs, decade-old contracts and inconsistent internal wikis, then measure it so you can defend the accuracy in a room.
Scope this with us“Can we let our team just ask questions against all of our documents?”
Fits a single 14-day sprint for one document domain and one interface. Multiple domains or heavy access-control rules usually take two.
Working retrieval over real data
Ingestion of the client's actual corpus — not a sample set — with the chunking strategy tuned to how those documents are structured.
Answers with citations
Every response points back to the source passage, so the end user can verify it and your client can audit it.
An eval suite you own
A scored question set that runs on every change, so a prompt tweak cannot quietly break accuracy after handover.
Documented architecture
Written decisions on the model, the vector store and the trade-offs, so your team can extend it without reverse-engineering our choices.
The parts that decide whether it survives contact with users.
- Hybrid retrieval — dense vectors plus keyword search, because pure embeddings miss exact identifiers, part numbers and names
- Reranking on the candidate set, which is usually the single biggest accuracy gain per hour spent
- Chunking tuned per document type: contracts, transcripts and tables each need different treatment
- Metadata filtering and per-tenant isolation, so one client's data cannot surface in another's answers
- Groundedness scoring to catch answers the retrieved context does not actually support
- Refresh jobs so the index reflects documents added after launch
- Ship a vector search demo and call it a product
- Skip evals and let the client discover the accuracy problem in production
- Dump whole documents into the prompt and bill the client for the token waste
- Pick a vector database because it is trendy rather than because it fits the scale
Before you scope it.
Our client's documents are a mess. Is that a problem?
It is the normal starting condition, and it is most of the work. Scanned PDFs, inconsistent headings and duplicate versions all get handled in the ingestion layer — we scope that honestly on the consultation rather than discovering it on day 9.
Which vector database do you use?
Whichever fits the scale and the client's existing infrastructure. If they are already on Postgres, pgvector usually avoids adding a vendor. At larger scale or with heavy filtering, a dedicated store earns its place. We will explain the trade-off rather than defaulting.
How do we prove accuracy to the client?
With the eval suite. It gives you a number on a defined question set instead of an opinion, and it is yours to run after we leave.
Want to stop turning down AI projects?
Bring the scope you’re unsure about — an RFP, a client request, a half-quoted project. We’ll tell you what’s buildable, what it takes, and whether it fits in one sprint. No charge for the call.