Helping a Well-Known Coffee Chain Access Data Through Conversational AI

A coffee chain worked with Relevance Digital to make data more accessible to employees without database-querying expertise. Using Gemini and BigQuery, we developed a conversational analytics solution that allows staff to ask questions in plain language without writing SQL.

The collaboration has since expanded into AI enablement, procurement intelligence and training assistant work.
 

Make data accessible beyond technical teams.

The coffee chain wanted non-technical employees to query information held in BigQuery using everyday language.

Writing database queries requires knowledge of Structured Query Language (SQL). The objective was to provide a conversational way to access that information, allowing employees to express their questions without having to write the underlying query.
 

Conversational analytics powered by Gemini.

We developed a solution that connects natural-language questions to the coffee chain’s data in BigQuery.

Gemini supports the conversational experience, while BigQuery provides the underlying data environment. Together, they allow non-technical staff to interact with structured information through questions rather than query syntax.

The work brings AI application development and data engineering together around a defined need: helping more employees access information through an interface they can use.

A more direct way to ask questions about data.

The solution gives the coffee chain’s non-technical staff a way to query BigQuery without writing SQL.

It demonstrates a specific application of conversational AI: making an existing data environment accessible through everyday language. Employees can focus on the question they want to ask, while the application connects that request to the underlying data.

Exploring further applications across the organisation.

The collaboration with the coffee chain has grown beyond conversational analytics to include AI enablement, procurement intelligence and training assistant work.

These areas extend the scope of the relationship into additional organisational needs. They represent further work within the collaboration, rather than completed outcomes of the initial analytics solution.

The project illustrates a practical starting point for AI adoption: identify a specific information need and develop an application that connects employees to the relevant data.