RAG
development
Assistants that answer from your own documents and cite where the answer came from — so they stay current without retraining.
What we build
Four areas where this work is won or lost.
Ingestion and chunking
Documents split by meaning rather than by page, with metadata kept, so retrieval finds the clause rather than the file.
Retrieval and ranking
Vector and keyword search combined, filtered by permissions and re-ranked — retrieval quality caps answer quality.
Citations and refusal
Every answer points to its source passage, and the system declines when nothing relevant was found.
Evaluation
A fixed question set scored on every change, so you can tell whether an edit made answers better or worse.
How we work
A short loop with a written result at every step.
Audit the corpus
What documents exist, how stale they are and who may read what. Most RAG failures are data problems, not model problems.
Build retrieval first
Chunking, embeddings and ranking tuned against real questions before any prompt work begins.
Ship with evals
A test set built from real queries and known failures, run on every prompt or model change.
Stack
Chosen for your product, not for our habit.
PineconeDocuments nobody
can search?
Tell us what you have and who needs answers. We will start with the data, not with a demo.
