They don't know what your data means
Active users, net revenue, segment logic — the definitions that make an answer correct for your business live in docs, dashboards, and a few experts' heads, not in the schema the agent can see.
AI agents
Your AI is guessing. Credible makes it know.
Point an agent at raw data and it fills the gaps with unfounded confidence: invented joins, guessed definitions, stale data treated as current.
Credible gives every agent the data model instead — Claude, ChatGPT, Gemini, or one you build — over MCP, the open protocol that connects agents to tools. Each question gets just the slice of the model it needs, checked against the model's access rules, so the answer uses the right definitions and shows its work.
The problem
Why agents get data wrong
A human analyst reading a cryptic schema fills the gaps with judgment. An agent fills them with confidence. The answer sounds right, and nobody can tell that it isn't.
Their work is hard to check
An agent writes a complex query in seconds. Without a governed model behind it, nobody can inspect which definitions it used — or whether the join quietly double-counted.
Every fix is bespoke
Prompt chains and ad-hoc pipelines turn every schema change into fragile maintenance. And stuffing the whole schema into the prompt costs tokens and accuracy on every single question.
How it works
The right context, retrieved
Your team writes down what your data means as a data model. When an agent asks, the engine matches the question by meaning and returns the slice of the model it needs — not the whole model stuffed into every prompt.
One gateway
Every question enters through one gateway over MCP. There it is checked against the model's access rules and logged to a permanent audit trail. The agent only gets back the rows the caller is allowed to see.
Discoverable by meaning
At publish, the engine compresses the model into a concept index — every source, measure, and dimension, its #(doc), and the actual values of every #(index)dimension on unrestricted sources — so an agent matches a question by meaning, not by column name. A question about “sports gear” finds Running Shoes and Athletic Apparel; get_context returns the matching slice, plus suggested queries grounded in governed views.
Correct by construction
The agent queries the model, not the tables. Every definition is written once, joined totals never double-count, and every field reference is checked against the live model before a query runs — so the answer is explainable and auditable, not merely plausible.
Cheaper because it's right
Small request in, small context out. A correct answer costs a fraction of the tokens — and correctness is the biggest saving of all, because wrong answers are what multiply retries.
The engine tunes itself
Every retrieval is a test of whether the model surfaced the right concepts. Misses become proposed fixes — a better doc line, a missing index, a view worth declaring — as reviewable changes to the model, in git. The more you ask, the better it gets.
The result
Plugs into the agent you already use
An agent that shows its work instead of asking to be trusted — in the chat window your team already has open.
We didn't build an agent. You already own the harness.
Every vendor promises AI answers you can trust — inside their chat window, on their agent, in their app. That's a platform. Credible is the engine that plugs into the one you already use: Claude, ChatGPT, Gemini, and the agents you build yourself, over MCP. One data model, the same answers, wherever the question gets asked.
Ready when you are
Stop debugging hallucinations. Start trusting answers.
See how the AI Analytics Engine gives every agent just the slice of the model each question needs — answers you can trust, not hallucinations you have to debug.