Ask your Power BI data in plain English, and trust the answer.

Your team has a Power BI dashboard for almost everything. Revenue by region. Pipeline by stage. Churn by cohort. And yet the most common question in any data-driven company is still the one the dashboard doesn’t answer: “okay, but why?”

The dashboard answers the question you already knew to ask

A dashboard is a set of answers to questions someone anticipated in advance. That is exactly what makes it useful, and exactly where it runs out. The follow-up you didn’t foresee (the one that occurs to you because of what the dashboard just showed you) isn’t on the canvas.

The number you need almost always exists in the model already. The problem is never the data. The problem is the path to it: file a ticket and wait two days for an analyst, learn enough DAX to write the measure yourself, or quietly drop the question. None of those are self-service. They’re a queue.

Ask the question you actually have

Exagomatica closes that gap by letting you ask your Power BI data a question in plain English, inside Claude Desktop, and getting three things back: the precise answer, an interactive chart, and the lineage that shows how the number was built.

No new dashboard to design. No measure to write. No ticket to file. You ask the way you’d ask a colleague who happens to know the model inside out, and you get a specific, defensible answer instead of a link to a report you then have to interpret.

It runs where your data already lives

The reason this is safe to point at real corporate data is that Exagomatica adds no cloud of its own: it runs locally, on your machine, as a bridge between Claude Desktop and the systems you already have access to. It reaches your Power BI datasets (and, if your team connects it, your Azure DevOps pipelines) using your existing corporate login: no separate servers, no VPN, no firewall changes.

Because it authenticates as you, Power BI applies exactly the rules it applies to you anywhere else. On the recommended Viewer tier, row-level security is inherited, not bypassed: if you can’t see EMEA salaries in Power BI today, you can’t see them through Claude either. Workspace Contributors and above are a different case, because Microsoft applies no row-level security to those roles, in Power BI itself; they see the same wider view through Claude that they already have there. Either way Exagomatica widens nothing of its own. It just makes the access you already have conversational.

A worked example: “why did revenue spike last quarter?”

Say the number in front of you is a quarterly revenue figure that jumped more than you expected. You ask why. Exagomatica queries the semantic model, returns the trend with a chart, and (this is the part a dashboard can’t do) traces the figure back through the pipeline that produced it.

It doesn’t stop at “revenue is up 14%.” It can point at the exact transformation that shaped the number: a filter applied in a specific dataflow, defined in M code, read straight from the model and the pipelines that feed it. You get the answer, the picture, and the provenance, which is the difference between a number you report and a number you can stand behind.

What about Microsoft’s own Power BI AI?

A fair question, and worth answering plainly. Microsoft now ships first-party ways to query a Power BI model in natural language. Turning a plain-English question into DAX is genuinely useful, and increasingly table stakes. We don’t claim to be the only tool that can do it.

The difference is scope. Those first-party options are Power BI-only, focused on one semantic model at a time. Exagomatica is a single local bridge that reads the whole story behind the model: the semantic model itself, the pipelines that feed it, and your business docs, with optional change tracking from Azure DevOps. It doesn’t just execute the DAX, it knows where the number came from and which ETL step shaped it. The value isn’t the query. It’s the orchestration across sources, and the trail of provenance that comes with every answer.

Where your data actually goes

Security here is a matter of architecture, not a marketing adjective. Your data flows from your enterprise systems, through the local Exagomatica bridge, to Claude Desktop on your machine. There is no Exagomatica cloud in the path. Claude Desktop, like any AI assistant, still sends your question and the returned data to Anthropic’s API to answer it; the local guarantee is about Exagomatica, not the AI itself. It runs under your Microsoft (Entra ID) identity and MFA, so Power BI applies exactly the access controls it applies to you anywhere else: row-level security for Viewer-role users, and the wider view workspace Contributors already have. Nothing is sidestepped, and nothing is widened. It is strictly read-only at every role: no write operation exists in the product, and data-definition and write operations are blocked at the server level, so a question can never become a change. Sign-in tokens are held in an encrypted local store, never in plaintext. There is no usage telemetry, and it speaks over a local channel with no open network ports.

That posture is deliberate. Exagomatica adds no cloud of its own to your data path (no extra service that ingests or stores a copy of your data), which is exactly what the mid-market and enterprise data teams it’s built for need from the tool itself: it reaches the data in place rather than handing the whole dataset to yet another vendor.

The shortest path from a question to a trustworthy answer

Self-service BI was always supposed to mean anyone can get an answer without waiting in line. For most teams it has meant something narrower: anyone can look at a dashboard someone else decided to build. Asking in plain English (and getting the answer, the chart, and the lineage behind it) is what closes the remaining distance.

Your data has answers. Just ask Claude.