How a Retailer Used AI to Query Data in Plain Language

The Challenge

Business analysts, operations managers, reporting teams and decision-makers at the retail company all needed data to do their work, whether to prepare reports, follow operations or make decisions. Not all of them, however, had the technical query knowledge required to retrieve that data themselves.

Users without that knowledge found it hard to access data directly. To answer their questions and make decisions, they relied on IT or analytics teams, which created a dependency between teams and slowed both reporting and the decisions that depended on it. Each new question meant another request to someone else, and in practice data remained accessible mainly to specialists.

The Solution

With cognipeer Studio, our team configured an AI assistant that let users ask questions about data in natural language. Instead of writing queries or waiting for a specialist, they could describe what they wanted to know in their own words. Questions could come from any of the roles that needed data, from analysts and reporting teams to operations managers and decision-makers.

The assistant translated each question into the appropriate query logic and returned the result as a clear output that users could understand and interpret. Decisions stayed with the people asking the questions: the assistant supported their judgement by making the relevant data easier to reach, while interpretation and next steps remained theirs.

Implementation

1. Focused on the people who needed data. We centred the solution on analysts, operations managers, reporting teams and decision-makers who needed answers without writing queries.
2. Configured natural-language input. Users could ask about data in everyday language rather than technical syntax.
3. Translated questions into query logic. The assistant converted each request into the appropriate query logic for the data being asked about, so users did not need to know how to write the query themselves.
4. Presented interpretable results. Outputs were returned in a clear form that users could understand, interpret and use in their own analysis and decisions.

Business Impact

Access to data became broader across the retail company, no longer limited to people with technical query skills. Users could get answers to their own questions, and IT and analytics teams were no longer the only route to the data needed for decisions. Reporting and decision-making could move at a quicker pace, with less waiting on other teams.

• Wider access to data for non-technical users across the business.
• Faster reporting on questions that previously needed help from IT or analytics teams.
• Quicker decision-making supported by clear, interpretable results that users could act on.
• Less dependency between teams when users needed data.

Technology Behind the Solution