The enterprise ontology: a model of your business, for AI

Tables are not understanding

The CRM has customers, the ERP has orders, the mailbox has inquiries, the shared drive has product files. Each is correct on its own, and none of them knows how it relates to the others. What AI reads is a handful of isolated tables, not a company in motion — which is why its answers are so often “almost right”: it doesn’t know which customer this quote belongs to or which quote this order came from.

What the ontology is

The operational ontology maps the real business into objects AI can understand and act on: customers, products, inquiries, opportunities, quotes, orders, documents, tasks, agents and market signals, together with their relationships, states, action types and business logic.

• A quote knows which inquiry it came from, which configuration and pricing rule it used, whether it crossed the margin guardrail, and who approved it.
• An order knows which quote it came from, what stage it is at, whether it is paid, which documents exist and which operations need approval.
• A task knows its goal, to-dos, state, owner, evidence and approvals — even when execution is interrupted, the system knows where it stands.

Why the ontology comes first

With a shared model of the business, 288 AI employees can work inside the same reality: any capability’s output is another capability’s input. Customer 360, Quote Studio, Trade Execution and the Evolution Engine are all built on this layer, and the Identity Spine makes one customer the same object across the CRM, the mailbox and the order book.

Ontology, forward deployment, proactive execution and governed evolution together make up the Intelligence layer.