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An ontology is the map of your business logic. It’s how you teach Wayak’s agent to understand your data the same way a person at your company would — what things are called, how they relate, and what they mean. In Wayak, the ontology is the semantic layer. It sits between your raw data and the AI agent, translating business language into precise data operations.
Wayak semantic objects interface

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Why you need one

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Without an ontology

The agent guesses. A table called txn_ledger with a column amt_net_usd means nothing without context. Results are inconsistent and unreliable.

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With an ontology

The agent answers with the same precision as your best analyst. It knows “revenue” means SUM(amt_net_usd) WHERE txn_type = 'sale' and applies that consistently.

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The building blocks

The ontology is made up of six components that work together in a hierarchy:

Objects

The core entities in your business — customers, orders, products, employees.

Dimensions

The attributes that describe and categorize your objects — region, status, tier.

Entities

Specific instances and how they’re identified and resolved across data sources.

Metrics

The calculations your team cares about — revenue, churn, conversion rate.

Views

Standalone charts and tables that visualize specific slices of your data.

Dashboards

Collections of views assembled into a single, shareable page.

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How it all fits together

The agent uses this hierarchy to navigate from a user’s question down to the exact data operation needed to answer it.
Start by defining your most important objects and metrics — those two alone give the agent enough context to answer most business questions accurately.