Conversational analytics with answers you can check

Conversational analytics means asking questions about your business data in plain language and getting an answer back, instead of building a report or writing a query. You type something like "which customers bought less this quarter than last" and the assistant returns the number, a short explanation and a chart. It only works well when the data underneath has clear, agreed definitions, which is where most of my work goes.

Power BI Copilot is enough for many teams already on Power BI, provided the semantic model is prepared for it. Copilot needs a Fabric capacity on top of your existing licences. A custom assistant makes more sense when the users have no Power BI licence, when clients need to ask questions inside a portal, or when the data lives outside Power BI. I'll recommend one route in the quote.

The main defence is a clean model with defined metrics, so the assistant picks the right measure instead of guessing. On top of that, every answer shows the measure, filters and period it used, the scope is kept narrow, and a test set of real questions with known answers is run before launch and after every change. Questions outside the scope get a polite refusal instead of an invented number.

Partly. It handles the routine questions an analyst gets every week, such as totals, trends and comparisons on defined metrics, and it shows how it reached each answer. It does not know why a number moved when the reason sits outside the data, and it refuses questions outside its scope. The practical result is that your analyst spends less time on lookups and more on the questions that need judgement.

Yes, the assistant is shaped to your data, your users and the questions they actually ask. Describe in your own words who should use it, what they want to know and where they should find it, and I match that as closely as possible. If a question can't be answered reliably with the current data, I tell you and say what would fix it.

No, the assistant follows the same permissions as your reports. In Power BI and Fabric, row-level security applies to Copilot and data agents, so a regional manager only gets answers about their region. In a custom build I enforce the same rules in the database query before the AI sees any data, so a clever question cannot get around them.

With a custom chat, yes: the question and the query results go to an API provider. With Copilot or a Fabric data agent, processing runs under your own Microsoft tenant and your existing agreement with Microsoft. For custom builds I use EU regions where the provider offers them and API terms that exclude training where that option exists, and only the rows needed for the answer are sent. NDA and DPA on request.

A conversational analytics assistant is priced as a custom quote because most of the work depends on your current data model. If your Power BI model is already clean, setting up Copilot or a data agent is fairly quick. If the model needs restructuring first, that part takes longer and is often the most valuable piece. You get a fixed price before work starts.