AI that answers from your data — and stays right in production.
We build the ingestion, retrieval, permissions, and evaluation layer beneath the chat window — the part most teams skip, and the reason answers stay trustworthy after launch.
What RAG actually is
RAG — retrieval-augmented generation — grounds AI answers in your real documents. When someone asks a question, the system first searches your data, retrieves the most relevant passages, then answers using only that material — not the model's memory. This sharply cuts invented answers and gives every response a source you can check.
The chat window is the easy part
Anyone can wire an API key to a chat box and get a demo that looks smart. The hard part is the layer underneath: getting the right passage back every time, respecting who's allowed to see what, and proving the answers are still correct months later. That layer is what we build.
What we own
Ingestion & the data pipeline
We build a pipeline per source (PDFs, wikis, tickets, databases), not a one-size template, so your content stays current as it changes.
Retrieval & search
Chunking, embeddings, and re-ranking tuned so the right passage comes back, not a nearby one.
Permissions
Retrieval filters by each user's access before the search runs. A document you can't open shouldn't be searchable by your assistant either.
Evaluation & drift monitoring
A scored test set gates every release, and we keep checking accuracy after launch as your data shifts.
A grounded answer, end to end
A question comes in. The system retrieves the passages the user is allowed to see. It answers from those passages and cites them. The eval gate has already confirmed that kind of answer holds up. (Walkthrough drawn from Greenlight's retrieval-grounded research platform.)
Two paths
- Enterprise — an internal copilot grounded in proprietary documents, permission-aware and compliance-ready, across a large corpus.
- Startup — a customer- or product-facing assistant on a smaller corpus, scoped to answer correctly from day one.
Runs after launch
Documents change, and a RAG system that was accurate at launch drifts if no one watches it. We monitor retrieval quality and re-score against the eval set on a set cadence — with an SLA or a trained handover, your choice. You own the code, the index, and the models.
