Data Ask

The question isn't whether AI can answer. It's who builds the understanding of your data — and what that costs.

Modern AI tools can be accurate on enterprise data — once your data engineering team first builds them a curated semantic layer to be accurate on, table by table, model by model, application by application — and kept current by your engineers as each application changes. Their accuracy is real. So is the bill, in dollars and in time — and any question outside the curated scope lands back in the engineering queue.

Data Ask was built to obviate that work. The AKG is generated automatically from your raw enterprise schema, at full enterprise scale, human-reviewed at every checkpoint, and updated as your data evolves. It encodes the business objects, relationships, terms, and tested query patterns inside each enterprise application — across the value streams (Procure-to-Pay, Order-to-Cash, Record-to-Report, Make-to-Order) every enterprise runs on. No question is ever outside scope, because the whole schema is in the map.

And the graph learns with use. When a steward or an end user confirms an answer, it is recorded in the AKG as a verified query pattern — so the next question that means the same thing, however it's phrased, finds a proven answer. Fix an answer once and it stays fixed for everyone. Accuracy compounds instead of decaying.