AI assistants grounded in your company's information
I build assistants that help a team find and use information in its own documents. The work starts with the source material and the questions people actually ask.
Find an answer and check its source
A useful knowledge assistant does more than produce a paragraph. It needs to help someone find the supporting document and decide whether the answer fits the situation.
For American Clay, staff can ask about product guidance, draft a customer reply, and open the supporting resource. For NMSA, coaches can find activities and open a field diagram or printable card.
What an assistant can get wrong
Generated answers can be incomplete or incorrect. Finding a relevant source doesn't guarantee that an answer uses it correctly. The quality and coverage of the source library matter, and people still need to check important answers.
A project needs representative questions, examples of useful answers, and clear expectations for what should happen when the source material doesn't contain an answer.
Private access is part of the scope
The client assistants shown here are private workspaces. Their public demonstrations use selected material and scripted examples.
For a new project, we need to agree on who can access the tool, where the documents and model run, and how conversation records are handled. Those decisions depend on the business and its data; they aren't one default promise for every project.
Bring a few real questions
Send a description of the people who need the assistant and a few questions they struggle to answer today. We can work out which documents would support those questions and what a useful first version would cover. Please don't send confidential source files through the inquiry form.
Tell me what you need
A short description is enough to start.