Give AI the business meaning behind your data.

A semantic layer expresses data in the language of the business: customers, orders, deliveries, revenue and agreed measures. Instead of asking every analyst or AI agent to interpret table names and joins independently, it provides an explicit model they can use to interpret the data. A useful layer also explains scope: what counts, what is excluded and which records support a result.

A database schema tells you where values are stored. It does not necessarily explain whether revenue means invoiced, paid or recognized revenue, or whether a delivery metric includes shipments still in transit. An AI-generated query can be syntactically correct and still answer the wrong business question. Explicit definitions and relationships make those assumptions inspectable.

In Talamys’s illustrative commerce example, an on-time delivery is a completed shipment delivered on or before its promised date. The June population contains 8,000 completed deliveries, of which 7,392 were on time. The result is 92.4%. The 100 shipments still in transit are excluded. The definition, denominator and underlying records matter as much as the percentage. These figures are fictional demonstration data, not a customer result.

Talamys presents business objects, their relationships and the records behind business answers together. Its enterprise map connects customers, orders, shipments, invoices and payments. The objective is to let teams inspect the meaning behind an answer and follow it back to evidence, rather than treating the answer as an unexplained number.

Start with one business question. Agree its definitions and population, check the relationships used to answer it, and inspect representative records. Ask how ambiguous definitions, missing evidence and changing business rules are handled. A semantic layer provides context; it does not by itself guarantee accurate source data or correct AI output.