Your knowledge base, answerable in seconds.
For teams whose know-how is buried in documents, tickets, wikis, and databases. We build AI that finds the right answer, shows where it came from, admits when it doesn't know — and keeps working at 3am.
When document-reading AI gets it wrong, it's usually not the AI's fault. It looked in the wrong place.
The failure pattern is always the same: a company dumps its documents into an AI and wonders why the answers are wrong or made up. The AI is fine. The search behind it is broken — it keeps handing the AI the wrong pages. So we start with the search.
We combine several ways of searching your documents, because any single method has blind spots. And before we write a single AI instruction, we build the test: hundreds of real questions with known correct answers, so we can tell objectively whether each change makes the system better or worse.
We also build the upkeep tools: checks that new documents get picked up, alerts when accuracy slips, and a running tally of what each question costs. A system that worked on day one and quietly degrades by month three isn't a working system — it's a problem you haven't found yet.
What we build.
Smarter document search
We combine several search methods — matching exact words and matching meaning — so the AI finds the right passage even when the question is worded nothing like the document.
Real accuracy testing
Before launch we test against hundreds of real questions with known answers — including questions it should refuse. We re-test every time anything changes.
Reads all your document types
PDF, Word, web pages, Notion, Confluence, Google Docs, Slack, and Jira. Everything stays up to date automatically as documents change.
Every answer shows its source
Each answer links to the exact document and page it came from, with a confidence score. No mystery answers.
Respects who can see what
Your existing permissions carry over — engineers can't pull up HR files, and contractors can't read the company financials.
Accuracy alerts
We keep testing accuracy automatically. If a new batch of documents or a software update makes answers worse, you get an alert — not a surprise.
How we build it.
Document audit
We review your documents: what formats, how good, how fresh, how many, and who's allowed to see what. We list the top 50 questions people will ask and build the test set from them.
Finding the best way to organize it
There are several ways to split up and file your documents for AI search. We test 3–4 of them against real questions and pick the winner based on results — not a gut call.
Building the search
We build the search system layer by layer, measuring at every step how often it finds the right passage.
Writing the answers
We design how answers are worded, how sources are shown, and when the system says 'I don't know.' Then we actively try to trick it into making things up — and fix what we find — before launch.
Watching & improving
We launch with a full log of every question and answer, plus cost tracking. Each week we review what went wrong and make it better.
Tools we use.
Frequently asked.
5 questions answered. Still have one? Reach out.
Before we build anything, we create a test set of 200–2,000 real questions with known correct answers. Every version of the system gets scored against it — right answers, right sources, and knowing when to say 'I don't know.' You can watch the scores on a dashboard from day one.