Private AI that actually knows your business

Ask a stock AI model about your business and it will answer confidently and wrong, because it has never seen your handbook, your contracts, your past projects, or your pricing. The thing that makes AI genuinely useful at work is not raw intelligence. It is access to your own information, handled in a way that keeps it private.

The model does not need to memorize your business. It needs to be able to look things up in it, on demand.

Retrieval, without the jargon

The technique that does this is usually called retrieval-augmented generation, or RAG, and the idea is simpler than the name. Your documents are indexed and stored locally. When someone asks a question, the system finds the most relevant passages from your own files and hands them to the model along with the question. The answer comes back grounded in your material, with the model reasoning over real, current information instead of guessing from what it learned in training.

What that feels like in practice

It feels like an assistant that has actually read everything your company has written. Ask what the renewal terms are in a specific contract and it pulls the clause. Ask how your team handled a similar project two years ago and it finds the notes. Ask a new hire's question about policy and it answers from the handbook, not from the open internet. The information was always there; RAG is what makes it answerable in a sentence instead of a folder dive.

Why it has to be private

RAG only works if the model can read your most sensitive documents, which is exactly why doing it on a public service is a problem. You would be uploading the crown jewels to a third party to index and query. On a private system, the documents, the index, and the model all sit on hardware you own. The assistant can read everything, because everything stays inside your building.

Tuned on top

Retrieval handles your facts; fine-tuning handles your style and your terminology, so the model not only knows your business but sounds like it. Together they turn a generic model into one that is genuinely yours. See the use cases this unlocks, how we build the agents and workflows on top, and the security that keeps it all in-house.

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