The company knowledge that machines can use too
The knowledge that today lives in a handful of heads becomes a shared base that anyone can query and that AI agents reason on without making things up
What it solves
In most companies, the knowledge that matters is written down nowhere. It lives in a handful of people's heads, in chat threads, in old emails, in folders nobody has opened in months. Whenever an answer is needed, everyone ends up at the same person's desk, and when that person is out, the work stops.
You pay for it in slow onboarding, in the same questions coming back every week, in decisions made in the dark because the document that held the context was nowhere to be found. We see it often: the bigger a company grows, the more this knowledge fragments, and it becomes a real risk once it depends on someone who might one day leave.
An enterprise AI knowledge base gathers this scattered knowledge and makes it queryable in natural language, with a trail of where everything was found. The same content, under the same permissions, then becomes the ground AI agents work on: they stop answering from memory and start reasoning over the company's real documents.
How it works
We start from the sources you already have
We connect the systems where the knowledge actually lives: shared documents, email, work chat, repositories, technical documentation. We don't ask you to move anything or rewrite it all elsewhere. Every piece of information keeps its link to the original document and to who is entitled to see it.
Anyone who searches gets an answer with its source attached
A person asks the question in their own words and gets an answer that shows where it comes from: which document, which version, who wrote it. Faced with a question the documents don't cover, the system admits it and stops; we'd rather give a verifiable "I don't know" than a plausible sentence built out of nothing.
AI agents reason on the same base
The same content, under the same permissions, feeds the agents that carry out real work: answering a customer, drafting a reply, routing a request. This is where we learned something the hard way. Early on we'd sometimes give an agent unrestricted access to all the knowledge available, trusting it to answer well; the result was fast agents that were impossible to control, and that occasionally pulled from the wrong document. Today the agent works within the same boundaries as a person, and every step stays traceable back to its source document.
Why it stays under control
- Every answer carries its source: document, version and author are shown in plain sight, so you can trace it back to the original for a direct check
- The permissions you already have are applied before the answer even takes shape: anyone without rights to a piece of content never sees it cited, not even second-hand inside another answer
- Confidential and public contexts stay separate by design, so internal information can't slip into an answer meant for a customer or the outside world
What changes
- Answers that used to go through a single person become reachable by anyone who needs them, the moment they need them
- New hires find most of what they are looking for on their own, and colleagues get interrupted far less
- The AI agents you put into production reason on context that is real and traceable, so they hold up under scrutiny instead of asking you to take their word for it
What we don't promise
- We don't promise an AI that answers everything: where your documents are silent, the system stops and says so, instead of filling the gap with invented sentences
- This is not a project you switch on and forget: knowledge keeps changing, and it has to be maintained and governed to stay reliable over time
- We don't replace the people who hold the context: we make their knowledge accessible, we don't send them away
Frequently asked questions
How do I know the answers are true and not made up
Every answer cites the document and version it comes from, so you can open the original and check it in one click; when a question isn't covered by your sources, the system says so instead of improvising a plausible-sounding answer.
Does confidential data stay protected
Yes, and we don't compromise on this point. Permission filtering happens upstream, before the answer is put together, and follows the visibility rules you already use in your systems. In the projects we run, this is the part we invest the most time in at the start, because getting a boundary wrong here is costly.
Do we need to rewrite all our documentation before starting
No. We start from the sources as they are today: we connect what you already use, without a big upfront clean-up. The first real answers arrive well before any thorough tidying work, and they are often exactly what tells you which documents are worth fixing.
How risky is it to try
Not very, if you start with the Inception package: a 2-3 week exploratory engagement at contained investment. You connect the first sources, see real answers drawn from your own knowledge, and decide where to go next on concrete evidence, not a bet.
At a glance
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