Enterprise platform AI

Consultants were spending days on manual archaeology to answer questions that should take seconds. What breaks if we make this field required. Which automations conflict on this object. What is no longer used anywhere.

AI helps here only under two conditions: it has to be grounded in the real configuration rather than general knowledge, and it must never be able to change production without being asked. Everything in the design follows from those two.

Generated output is validated, never trusted

Code is compiled and configuration checked against the live schema before anyone sees a deploy button. A model that invents a field name should fail a check, not somebody's Monday.

Answers are checked against the real installation

Every component name appearing in an answer is verified to exist. Anything unverifiable is flagged on the response rather than presented as fact, which turns a confident hallucination into a visible caveat.

Cache aggressively or the integration dies

Platform metadata endpoints are slow and rate limited. A naive crawler exhausts an organisation's daily API budget before lunch, which is the most common reason tooling like this becomes unusable at scale.