Most AI projects struggle for one reason: the organisation's knowledge is not ready for AI, and it does not stay ready, because knowledge keeps changing. KAG is the approach that addresses this at the source, by giving AI structured, validated knowledge before it answers.
Powerful and fluent, but without a reliable knowledge base it can answer with confidence even when it is wrong.
A promising first step, but it retrieves similar passages, not structured or verified knowledge.
Knowledge is identified, assembled and validated, so answers stay within verified knowledge and can be audited.
Every question follows the same path, and the answer is checked before it reaches the user.
KBAI identifies which knowledge is being asked for and in which contexts it applies.
It gathers structured knowledge from the knowledge base, each item with the context it belongs to.
The AI answers from that knowledge, the answer is checked for coherence, and refined when needed.
Answers draw on your own validated knowledge, not on the open web or the model's memory.
Every claim traces back to the knowledge and context it came from.
Expertise captured in the knowledge base remains available when people move on.
KAG is a new and still emerging approach, and KBAI is among those pioneering it. Our contribution is the way knowledge is represented: it carries its time, truth value, confidence and reliability, so AI can tell what is current and trustworthy.
It is the result of more than a decade of continuous R&D with universities in knowledge representation and reasoning, a work that continues today.