Business intelligence dashboards play an essential role in monitoring established performance indicators. Teams use them to track revenue, compare results with targets and monitor operational trends. However, dashboards reflect the questions their designers anticipated, while business discussions often lead in unexpected directions.
Consider a leadership meeting in which a dashboard shows that one region exceeded its sales forecast while another fell short. The team might want to know whether the difference came from customer activity, order volume, pricing or product mix. Existing reports may cover part of the answer, but the next step could require several filters, an Excel export or a new request to the data team.

That gap between seeing a result and investigating its drivers can slow decision-making. q3 helps users follow these questions within the governed data environment their organisation already maintains.

With q3, users ask business questions in plain language through a dedicated web application or by mentioning q3 in a Microsoft Teams chat. The product interprets each request against the available Power BI semantic model and returns an answer grounded in the organisation’s data.

Depending on the question, q3 can present written explanations, supporting breakdowns, comparisons, tables or charts. A user can then ask a follow-up question that builds on the same analytical context, making it easier to move from a headline figure to a more detailed investigation.

For example, a sales leader might ask:
 How did monthly revenue compare with our forecast across the top three products?
 Which customer groups contributed most to the change in sales?
 Was the difference driven by order volume, prices or average basket size?

The value lies in the continuity of the investigation. Users can refine their questions as new findings emerge rather than starting over with each request. They can also save useful tables and charts in the q3 Catalog for later reference and export the data behind their analysis in CSV format.

q3 extends Power BI rather than replacing it. The product works with an existing Power BI semantic model, including the business definitions, measures, relationships and governed structures that organisations have already established. This approach keeps conversational analysis connected to the same foundation that supports existing reporting.

Dashboards remain useful for recurring reviews and standard metrics. q3 complements them when users need to investigate an unexpected result, explore a new question or understand what may be contributing to a change. Data teams can continue maintaining the underlying model and governance while business users gain a more flexible way to investigate it.

Data governance matters when employees use AI to explore company information. q3 works with the existing Power BI semantic model and its established user access rules, rather than introducing an independently defined version of the organisation’s data.
According to the current product description, q3 is hosted in a dedicated Microsoft Azure subscription configured for each organisation. Eunoia handles Azure setup, access and permissions, connection validation and initial testing, then provides administrator handover and introductory training. Organisations should confirm the details of their specific deployment and security requirements during implementation.

Business users do not need to formulate every question as a new dashboard requirement. They can start with language they already use in meetings and explore the available governed data through a conversation. This makes q3 relevant to people in sales, finance, operations and leadership roles who regularly need to understand the factors behind reported performance.

For data teams, the opportunity is to reduce routine requests for follow-up breakdowns while keeping control of the definitions and structures behind the answers. q3 is designed to support analysts, not replace their judgment or the more specialised investigations they perform.

Sales and commercial teams

A sales manager can investigate whether a change in revenue reflects customer numbers, order frequency, pricing or differences across products and territories, provided the relevant measures exist in the Power BI model.

Finance teams

A finance professional can examine a variance against plan, ask which business units contributed to it and continue exploring the figures through comparisons or visualisations.

Operations teams

An operations leader can explore inventory patterns, compare locations or investigate which products may need attention when stock is limited, using the data available through the semantic model.

Leadership

Executives can move beyond headline KPIs to examine the factors influencing performance. A conversation that begins with a reported result can progress into questions about business priorities, risks and possible next steps.

These examples illustrate potential workflows rather than verified customer outcomes. The quality and scope of any answer depend on the data and definitions available in the organisation’s Power BI model.

Business intelligence provides the structured view organisations need to monitor performance. Conversational analytics adds a way to explore questions that emerge from that view. q3 brings these approaches together by helping users investigate governed Power BI data through natural-language questions and follow-up analysis.

For organisations that have invested in Power BI and want to make that investment more useful across business teams, q3 offers a practical next step: keep established reporting in place while making it easier to understand what sits behind the numbers.