Thirty years of documents
The group had thirty years of documents: procedures, contracts, technical manuals, minutes, quotes. And mixed in, as always, personnel files, confidential agreements, a legal dispute.
It was the kind of request we now get every week: we want an internal assistant that answers people's questions using our documents.
The silent risk
Discussion of these systems almost always centres on a single risk, error: the confidently invented answer, the so-called hallucination. But in business settings there is a quieter, more costly risk: the right answer given to the wrong person.
An assistant that has read everything and answers everyone is not a productivity project. It is an incident waiting to happen.
Permissions before the model
So the project started there, not with the model. Document retrieval was built to respect the original permissions: the assistant only searches places the person asking could already access on their own, with their own credentials.
Every answer cites its sources, with a link to the document it comes from. And when the context is not enough for a well-founded answer, the assistant says so: "I don't know, with the information available" is an answer the project was designed to give, not a system failure.
The test bench
Before writing a single line of integration code, we wrote the evaluation set: a few hundred questions with their expected answers, including trick questions, built specifically to tempt the system into making things up or straying into areas it should not.
Every change goes through that test bench before it reaches users. Everything the assistant receives and answers is logged, because a system that talks to the company must be auditable like any other business system.
The decisive demo
The demonstration that decided the project was not a brilliant answer. In a meeting, an executive asked the assistant for a department's pay data. The assistant replied that those documents were not accessible with his profile, and told him who to ask.
There was a moment of silence, then the sentence that made the whole job worthwhile: so we really can use it.
The takeaway
People soon tire of a system that answers everything; they come to regret one that answers too much. In a business, the reliability of an AI is also measured by what it refuses to say.