How does AI learn how your company actually works?
HexaHQ captures useful context as people work, learns from corrections, keeps shared concepts aligned, and gives each AI task only the parts that apply.
Your documentation is source material, not automatically the answer
Your docs, repos, tickets, and Slack history are genuinely useful, and HexaHQ does not ask you to move out of them. They are built for people to read, though, and the same rule often appears in several of them at once: an old version in a wiki page, a newer one in a thread, an exception someone agreed to in a comment.
Finding all four of those faster does not tell an AI which one is current, who had the authority to set it, or whether it applies to the person asking. That judgment is the part HexaHQ maintains, and it is what turns source material into something an AI can safely act on.
Corrections become organizational learning
Most of what a company knows never gets written down, because writing it down is a separate job nobody has time for. It does come out during work: someone reads a draft and says the discount needed legal sign-off, or that this customer is on the old pricing, or that support tickets from enterprise accounts go to a different queue.
HexaHQ captures those corrections as they happen and keeps them where later work can use them. The person who made the correction reviews what was saved. They do not have to stop and maintain a second knowledge base by hand.
A correction is not automatically a rule for everyone. HexaHQ keeps its scope, its source, and where it applies, so a personal preference does not turn into company policy without anyone deciding that.
What is true for the company, and what is true for you
How your company prices a renewal is a company fact. How you like a follow-up structured is yours. Both are real context and both change the output, so HexaHQ keeps them separately and uses them together.
That separation is what makes sharing safe. Your own way of working can improve every task you do without being pushed onto anyone else, and a company rule can reach everyone it applies to without flattening how individuals work.
One concept, not five copies drifting apart
The number of records in your product database gets quoted in a deck, a help article, a sales email, and a page on your website. Then it changes. Usually one copy gets updated.
In HexaHQ it is one concept with a current version, a history of what it used to be, and a record of who changed it. Update it once and later work starts from the new number. When two documents turn out to describe the same thing in different words, HexaHQ can propose merging them and let a person make the call, because deciding which of two rules is authoritative is not something to guess at.
Right context, not maximum context
An AI can have access to everything your company has ever written and still get the decision wrong, because retrieval is not the same as relevance. What matters for a task is the short list of facts, rules, and exceptions that would change the answer.
That is what HexaHQ hands over before the AI decides or acts: the parts that apply to this task, for this person, in their current version. Irrelevant context is not harmless. It makes the reasoning worse, even when there is room for it.
Teach it once, on work you already have.
Start free, hand your AI a real task, and correct what it gets wrong.