Applications built on large language models and grounded in your own knowledge, so answers are accurate, cited and visible only to the people who should see them.
A custom LLM application is software built on a large language model and connected to your organisation's own data. Using retrieval-augmented generation (RAG), it finds the relevant passages in your documents, answers from them and cites its sources, so answers stay accurate and visible only to people with access.
Your organisation's knowledge is scattered across PDFs, folders and people's heads.
We gather the real questions people need answered and agree what a correct answer looks like.
Documents, wikis and drives are cleaned, split and indexed, with permissions carried over from the source.
The application finds the relevant passages, answers from them and shows the sources it used.
An evaluation suite built from your questions scores the system before launch and after every change.
Imagine a manufacturer whose service engineers search through hundreds of PDF manuals. A knowledge assistant lets them ask in plain language, returns the procedure with a link to the exact page, and only shows documents each engineer's role allows.
A good first project is an internal assistant over one well-kept document set, such as HR policies or product manuals. Most first projects begin as a proof of concept lasting two to four weeks. See ways to work with us
If your source documents are out of date or contradict each other, fix those first, since the assistant can only be as accurate as what it reads. For a small set of documents, good search may be enough.
RAG lets a language model answer from your own documents. The system first finds the relevant passages, then generates an answer grounded in them and cites the sources.
No. We configure model providers so your data is not used for training, and we can deploy privately or on-premise when your data must stay in-house.
We build evaluation suites from your real questions and measure accuracy before launch and after every change.
Tell us about the process you want to improve. We'll recommend the simplest path to a result, and the first conversation is free.