Visa Cut Data Reporting From Days to Seconds

Visa's new AI agent turns multi-day data reports into instant answers, saving employees up to 10 hours of work per week.

Visa's Authorize.net team faced a common problem: slow, cumbersome data reporting. Getting insights from their massive payments dataset could take days. To solve this, they built a new system using two key technologies: ClickHouse Cloud, a high-speed database designed for analytics, and LibreChat, an open-source platform for building AI chat applications. They connected these tools to create a conversational business intelligence agent. Now, instead of writing complex queries or waiting for manual reports, employees can simply ask the AI agent questions in plain English about payments data. The agent understands the request, queries the ClickHouse database, and returns an answer almost instantly. This transformed a multi-day process into a sub-second interaction.

The results for Visa are significant and quantifiable. The company reports that each user of the system reclaims between eight and ten hours of productive time every week. This isn't just a minor improvement; it's a fundamental change in how their teams access and use data. For other businesses, especially those with large datasets, this case study provides a powerful blueprint. It demonstrates how combining open-source AI tools with a modern, fast database can deliver massive efficiency gains. CTOs and developers can see a practical, real-world application of conversational AI that moves beyond hype and delivers tangible business value, showing that sophisticated data analysis is no longer limited to specialized data scientists.

This project highlights a major trend in business intelligence: the shift from static dashboards to dynamic, conversational interfaces. As large language models become more capable, companies are increasingly looking for ways to make data more accessible to non-technical users. The success of Visa's initiative suggests that this “data democratization” is becoming a practical reality. By allowing anyone to query complex data through simple conversation, businesses can make faster, more informed decisions. This approach also reduces the burden on dedicated data teams, freeing them to focus on more strategic work instead of fulfilling routine report requests. We can expect to see more companies adopt similar models, pairing AI front-ends with high-performance data back-ends.

This is a real-world example of conversational AI delivering massive, measurable efficiency gains in a large enterprise, providing a blueprint for how other companies can make complex data accessible to non-technical teams.

Visa reclaimed 8-10 hours of productive time per user each week by replacing a multi-day reporting process with a sub-second conversational AI agent. This significantly accelerates data-driven decision-making and frees up technical staff.