AI development
LLM apps grounded in your data
We build LLM applications for search, document Q&A, summarization, and internal assistants. Answers come from material you control, and each answer can show where it came from.
The problem
Dropping a chat box on a general model gives fluent answers that are not yours. Staff cannot tell what was read. Private documents end up in prompts they should never enter. Users stop trusting the tool after a few wrong replies.
A retrieval system is the product: which documents are in, who may see them, how chunks are searched, and how you test real questions before anyone relies on the answers. We build that path, then the interface on top.
What we deliver
Retrieval over your material
Policies, manuals, filings, and knowledge bases indexed so the model reads your sources first.
Answers with sources
Responses that cite the passage they used, so a person can check the claim.
Access that matches your org
The model only retrieves what that user is allowed to see.
A test set of real questions
Evaluation on questions your team actually asks, before and after each change.
How we work
01
Collect the questions
We write the questions the system must answer, and the ones it should decline.
02
Prepare the sources
We clean, split, and index the documents, and drop anything that should stay out.
03
Build retrieval and checks
We wire search, generation, citations, and the refusal path.
04
Score it, then launch
We grade answers on the test set and only then open it to users.