An Ontology Model Is the Skeleton Every Business Needs to Make AI Answers Trusted
A Data Mart is one bone: your logic defined once, as one of six types, carrying an output schema that says what each field means. Publishing is the gate, so nothing reaches spreadsheet, dashboard or AI assistant until you publish it. A Data Model is the articulated set of those bones. Declare the join keys between marts once and the business moves across Ads, CRM and product data instead of queueing for you to write the join.
Metadata Autofill drafts each field’s alias and description from the schema and a sample of up to 30 real rows; you edit and save, so the words an AI answer is grounded in stay yours. Start from a mart you already have, or adapt a industry model. There is no new language to learn and no modeling project first.
Data Modeling Features
F01
Data Mart Management
When every report carries its own copy of the logic, numbers drift between teams and tools. A Data Mart is that logic defined once by the Data Owner — SQL, Table, View, Pattern or Connector — with an output schema that says what each field means.
- Every report built on a Data Mart shows the SQL behind its numbers, re-runnable in your own warehouse.
- Nothing reaches a spreadsheet, a dashboard or an AI assistant until the Data Owner publishes it.
- Start from a query, table, view or pattern you already have; the AI helper drafts field names and descriptions.
F02
Metadata Autofill
A business user or an AI assistant can only use a Data Mart it understands, and metadata typed by hand is the task every Data Owner defers. Metadata Autofill drafts it for the Data Owner to approve.
- Every draft is written from the schema and a sample of up to 30 real rows, so it describes what the data actually contains.
- Autofill drafts, the Data Owner edits and saves — the descriptions an AI answer is grounded in stay the Data Owner's.
- One click fills aliases and descriptions for every field and the Data Mart's own title and description; field descriptions reach the spreadsheet's column picker and header-cell notes.
F03
Joinable Data Marts
When a question needs a column from another Data Mart, the usual fix is one more SQL Data Mart, or a hand-written join that silently doubles revenue on a one-to-many table. Declare the relationship once, and every report on the Data Mart picks the joined fields.
- The SQL pre-aggregates each joined Data Mart by its join key before the LEFT JOIN, so adding a column never multiplies your rows, and the query is one click away.
- The Data Owner sets the join keys and how each field collapses; business users and AI assistants pick only through joins the Data Owner declared.
- Chains count: if Campaigns joins Orders and Orders joins Products, Products fields are one tick away in every Campaigns report.
You are booked. The invitation is on its way to , with the calendar invite and the meeting link.