LLM apps, RAG and knowledge systems
Your documents and data, searchable and answerable in plain language.
Assistants and search that answer from your own documents and systems, cite their sources, and respect who is allowed to see what.
A language model on its own knows nothing about your business. Retrieval-augmented generation (RAG) fixes that by finding the right passages in your own material and asking the model to answer from them. Done carefully, people get answers they can check. Done carelessly, they get confident guesses. We do it carefully.
What we build#
- Internal knowledge assistants that answer questions from policies, manuals, past projects and wikis.
- Customer-facing question answering grounded in your product documentation, with every answer linked to its source.
- Document analysis and extraction for contracts, reports and forms: pull out the clauses, figures and dates that matter.
- Copilots inside your existing tools, so people get help where they already work.
What makes it trustworthy#
- Permissions-aware retrieval. People only get answers drawn from material they are allowed to see.
- Evaluation on your real questions. We build a test set from what people actually ask and measure accuracy before and after every change.
- Careful chunking and search design. Retrieval quality decides answer quality, so we spend our time there.
- Cost and speed under control. The right model for each step, with caching where it helps.
What you get#
- A working assistant or search experience on your own content
- An evaluation set and a way to run it whenever things change
- Admin tools to add, remove and re-index content
- Usage and quality reporting
Have a process that eats hours? Tell us about it.
A short call is enough to tell you whether an agent is the right answer, and what it would take.