Analytics
Analytics used to mean a BI tool: answers 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.
The problem
The store says $214k. Finance says $198k. The ad platform says something else, and the reconciliation ticket has been open for months. Behind it is a setup built for a central data team: the business lands its data in the warehouse first, then waits for someone else to model it.
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 in a dashboard builder set a hidden setting, and the dashboard became something nobody could read, check, or compare. Rename a field and forty dashboards break over the following week, and nobody knows which ones until someone opens them.
How it works
Start with the model, or none of this works. Describe your domain as a data model, and every answer, dashboard, and data app is served 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 settings and scattered spreadsheets. 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 definitions behind each one a click away. Pin an insight, extend it, ask the follow-up — understanding compounds instead of resetting with every question.
A dashboard isn't a way of asking a question — it's a way of never asking it again. Keep it. What changes is how it gets made: say the dashboard you want in a sentence and the agent builds it on the governed model, with the numbers behind every tile one click away.
A change is a sentence too. Describe it, and the previous version is always there to go back to.
“Revenue should exclude refunds.” “Active means signed in this month.” Say it, and the agent proposes the change to the model. The owner approves it, and every dashboard, app, and agent inherits it at once. No settings panel, no hunting through forty dashboards for the one place the old rule still lives.
Anyone proposes, the owners approve, every surface inherits. The people who know what a number means change it in their own words.
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
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 numbers stop being a ticket queue and become what they should have been: the ones who decide what the numbers mean.
Ready when you are
See how the AI Analytics Engine puts one data model behind every dashboard, workspace, and conversation.