In-house AI

It depends which route you pick. With an external model, the question and parts of the matched documents are sent to that service. An on-premise LLM keeps the model on your own servers, so documents never leave and there is no per-call cost that grows with use, at the price of upfront cost and hardware. We decide together based on how sensitive the documents are and how much you will use it.

We build assuming it will be wrong sometimes. It does not just produce an answer; it shows which document and which passage it relied on, so a person can check on the spot. That is what makes it usable for real work. When it cannot find grounds, we tune it to say it does not know rather than invent something. This is the deciding factor for adoption, so we design for it from the start.

You can. We just do not load everything. We pick around ten questions people actually ask, then feed in only the documents that really contain those answers. That reveals which documents are useful and which are out of date, and the tidying tends to happen along the way. Waiting until everything is organized usually means never starting.

The first fork is whether documents may leave the company. If they may, the chatbot route starts faster and cheaper. If they may not, it is on-premise. The second is volume: external models charge per call, so past a certain daily usage on-premise becomes cheaper. In the first conversation we look at the nature of your documents and expected volume and point out where that crossover sits.