QueryReader, Benchmark's natural language to SQL platform, letting business users answer their own data questions safely, grounded in the real schema.
A leading omnichannel retail company operates hundreds of stores alongside a growing online business.
The company wanted business users to access data through natural language queries, enabling them to retrieve accurate insights without writing SQL, while ensuring responses aligned with the underlying database schema.
The company's transactional and reporting data sits in MySQL databases with hundreds of tables.
Business teams know exactly what they want to ask but not how to ask it in SQL.
The analyst team had become a bottleneck for the whole organization, and the backlog of routine data requests kept growing.
The company wanted business users to answer their own questions safely, with queries guaranteed to match the real schema.
Every operational bottleneck reported was matched to the workstream(s) that resolved it.
Non technical staff could not write SQL for their own questions.
Business users type questions in natural language and receive correct, ready to run MySQL queries.
A React interface lets users ask questions, run queries, and view results without any SQL knowledge.
A small team handled every ad hoc query request for the entire business.
Business users type questions in natural language and receive correct, ready to run MySQL queries.
Hand written queries often referenced wrong tables or columns and failed.
Google Gemini generates each query grounded in the live database schema, so results match real tables, columns, and keys.
Routine data pulls took days from request to result.
Business users type questions in natural language and receive correct, ready to run MySQL queries.
New joiners struggled to understand an unfamiliar and complex database.
A React interface lets users ask questions, run queries, and view results without any SQL knowledge.
Business users answered routine questions themselves in minutes.
The analyst team shifted from repetitive pulls to higher value work.
Schema grounded generation removed the most common query errors.
Teams acted on same day data instead of waiting for the queue.
New staff explored the database through plain English instead of learning its structure first.
Turnaround for routine data pulls likely dropped from days to minutes.
Query failures from schema mismatches were likely reduced significantly through schema aware generation.
Data access likely expanded to every business team, not just those with SQL skills.
