Do we ask questions in Metagem, or in the tools we already use?

Either. Metagem can answer business questions in-platform. And if you have a preferred engagement layer — Databricks Genie, Microsoft Copilot, a custom agent — we connect to it seamlessly and improve the answers with governed business context. What stays the same either way is what the answer rests on: definitions your people own, traced to the data underneath.

Why do these tools get business questions wrong when the data is right there?

Because data on its own does not carry meaning. A model sees column names, not which of three revenue fields your CFO recognises, or that your fiscal year starts in April. It infers, plausibly and invisibly, and a wrong answer arrives with exactly the same confidence as a right one.

We already have a semantic layer. Isn't that the same thing?

A semantic layer is part of it. It defines metrics and dimensions so a BI tool produces consistent charts. A context layer adds ownership, provenance, quality signals, and the tacit knowledge sitting in documents, in a form that agents can work with.

Where would we start?

One domain. We map the metrics that domain reports on, trace each to its source fields, and surface where definitions disagree. That foundation is useful whether or not you ever switch on a talk-to-your-data tool.

TRUSTED GENERATIVE BI

Ask the Same Question Twice. Get Two Different Answers.

Point a model at your warehouse and it will answer everything you ask. Some of those answers are wrong, and nothing in the output tells you which. The chart looks identical either way, and someone makes a decision on it.

"It answered all seventeen questions.Nine of them were wrong."
*Internal benchmark on ungoverned models on operational data.

Maia

Generative BI

HOW METAGEM HELPS

Define. Ground. Flag.

What any trusted rollout needs in place first, whichever tool you point at your data.

Define

Step 1

Metagem builds contracts that explain how a dataset is structured, how it should be queried and what the headers mean.

Ground

Step 2

Every question has to resolve to real fields, not to column names that merely look plausible. Meaning mapped to the physical data underneath it.

Flag

Step 3

When context is missing or a question is ambiguous, the honest response is to say so. Asking beats answering confidently and wrongly.

What You Get

The groundwork trusted answers need.

Agreed metric definitions

Settled once, owned by name, with the source attached.

Metrics mapped to source fields

Every definition traced to where the data physically lives.

The gaps that would break a rollout

Ambiguities and conflicts surfaced before a tool answers on them.

Context any tool can read

Open and exportable, for your BI layer and your agents alike.

Frequently Asked Questions

Common questions about talking to your data.

Thinking about letting people talk to your data?

We map one domain and show you what would break first.

Let's Talk