Knowledge scattered across Slack, email, and docs contradicts itself. Nobody notices until someone sends the wrong number to a client. We stop that.
Your company's knowledge is scattered across Slack, email, and docs. It contradicts itself constantly. Nobody notices until someone sends the wrong number to a client, quotes an expired policy, or makes a decision based on something that changed three weeks ago.
Reads everything your team produces — conversations, email, docs. Extracts atomic, cited facts. No manual entry.
Every fact knows when it became true, when it stopped being true, who said it, and what replaced it. Not summaries — versioned truth.
When sources disagree, the system catches it. Flags it to the right people. Before it becomes a costly mistake.
Half of project management is just remembering things and synchronizing people. The system handles it — gathering context before decisions, disseminating them after.
Knowledge graphs have been attempted for decades. They required rigid schemas designed upfront and dedicated engineering teams to maintain. They never scaled beyond demos.
This system uses natural language facts, not structured attribute triples. Each fact is a sentence — cited to the original source, timestamped, linked to entities, and tracked through supersession chains. When two facts conflict, the system detects it structurally.
This is only possible with LLMs — and only economically viable as token costs drop. The infrastructure is proven on real data today. As costs fall and local models improve, it scales to every company.
We're working with early adopters. Reach out if this sounds like a problem you have.
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