Business intelligence
BI tools were built for a world where insight lived inside dashboards and ticket queues, and what the data meant lived in a few experts' heads.
Credible starts with a data model. Ask in plain language and the number you get is the number everyone else gets — and when you need a dashboard or data app, describe it and the agent generates it from the same model, as code your data team can review.
The problem
A dashboard says MAU is 12,847. The board deck says 15,200. Finance trusts a spreadsheet, and the reconciliation ticket has been open for months.
New questions spawn dashboards, SQL scripts, and spreadsheets, each with its own copy of the logic. Stale reports accumulate, nobody knows which one is current, and the definition of a metric drifts across every surface it lives on.
The sales director who wants revenue by region knows exactly what she wants to see. The old workflow forced it through two lossy conversions: her knowledge flattened into a ticket, an engineer's expertise spent turning the ticket into clicks.
Every click set a property, the properties serialized to a blob, the blob got rendered. That's a compiler with no text, no diff, no review, no tests, and one IDE in a tab your vendor owned. Rename a field and forty dashboards break over the following week.
How it works
Start with the model, or none of this works. Write down what your data means as a semantic data model, and every BI surface serves from it. Dashboards become outputs of the model — not the source of truth.
Metrics, dimensions, and business rules live in the model instead of dashboard configs and scattered SQL. Finance and Product ask about MAU and get the same number from the same definition — and joined totals never double-count, because the model knows how the tables relate.
Conversation is the right tool for the question you haven't asked yet. Ask in plain language in a shared workspace; answers arrive as KPI cards, charts, and tables grounded in the model, with the query behind each one a click away. Pin an insight, extend it, ask the follow-up — understanding compounds instead of resetting with every query.
A dashboard isn't a way of asking a question — it's a way of never asking it again. Keep it. What goes is the canvas: say the dashboard you want in a sentence and an agent writes it as a data app on the governed model, versioned like code, with the query behind every tile one click away.
A colleague writes the sentence; the data team reviews the diff — a few lines, checked for correctness and governance — and git holds the source of truth. The same loop works on the model itself: anyone proposes, the owners review, every surface inherits.
The engine watches what gets asked and keeps the hot rollups warm, so a dashboard a team opens every Monday loads from a small table instead of a warehouse scan — and the bill for it stays small too.
The same model reaches Claude, ChatGPT, Gemini, Slack, and the products you ship. Ask wherever you are; the answer is the one everyone else gets.
The result
Eight sources, five definitions, and zero agreement become one governed model, one MAU definition, and one number delivered everywhere.
bars.com used to spend over a week assembling one campaign report by hand. On Credible, two report decks due the same day were both done before lunch — a conversation with the model, grounded in numbers that reconcile to the cent. Teams get answers on demand, and every number a leader sees agrees with every other. The people who own the model stop being a ticket queue and become what they should have been: its authors and reviewers.
Ready when you are
See how the AI Analytics Engine puts one data model behind every dashboard, workspace, and conversation.