AI semantic layer for consistent business metrics
Everyone agrees the number should be the same every time. The disagreement is about who gets to decide what it means.
A question becomes a decision
Does everyone mean the same thing by revenue?
- 01
Connect
Finance-approved metric
- 02
Resolve meaning
One definition, reused across questions
- 03
Review the answer
The certified definition is explicit, instead of being reinvented in each prompt.
Illustrative workflow. Results depend on configured sources, definitions, and permissions.
Nexatron's semantic layer is expressed in MetricQL: metrics, dimensions, hierarchies and joins are declared once and compiled to warehouse SQL at query time, so the same definition serves a typed question, a certified-metric lookup and an agent query. Technical semantics are generated automatically when a source connects; promoting a definition to certified remains a human action.
What it does
- Declared once, compiled per dialectA metric defined in MetricQL compiles to the SQL each connected warehouse actually speaks, so one definition does not fragment into per-source variants that drift.
- Generated technical semantics on connectConnecting a source produces its entity and field projection automatically: what exists, what type it is and how it relates, scoped to what the connecting user can see.
- A person certifies it, never a heuristicThe platform will suggest and rank candidate definitions. It will not mark one certified on its own, because an auto-certified metric that is subtly wrong is indistinguishable from one that is right until a quarter closes on it.
- Grain and joins are declaredRelationships carry their cardinality, so aggregating across a one-to-many link is either handled correctly or refused, instead of silently inflating every total.
What it deliberately won’t do
Each of these is a case where returning something would have been easy and wrong, so the product is built not to.
- Connecting a source does not give you governed metricsIt gives you the technical layer: tables, fields, types, relationships. The business meaning (which of four revenue definitions is the right one) is analyst work, and the product is explicit that it has not done it for you.
- Uncertified answers are still available, and still labelledThe layer does not block exploration. It refuses to let exploration wear the same badge as an approved definition.
Common questions
- What is a semantic layer?
- A semantic layer is a governed map between raw database tables and the business concepts people ask about: metrics, dimensions and the relationships between them. It makes a question such as "what was net revenue last quarter?" resolve to one agreed definition instead of whichever SQL a person or a model happens to write.
- Why does AI analytics need a semantic layer?
- A language model pointed at raw tables has to guess which of several plausible definitions a question means, and it can guess differently on different days. A semantic layer gives it the agreed definition, the declared joins and their grain, so answers from people, dashboards and AI agents match.
- How is the Nexatron semantic layer defined?
- In MetricQL. Metrics, dimensions, hierarchies and joins are declared once and compiled to each connected warehouse's SQL at query time. Technical semantics are generated when a source connects; promoting a definition to certified is always a person's decision.
Test the claim, don’t take it
Ask it something you already know the answer to, then ask it something your data cannot answer. Those two questions tell you more than a feature list.