[PLACEHOLDER] How a financial automation platform saves $4.5M/year with 70% AI resolution

A financial automation platform that serves 500,000 small and midsize businesses replaced its previous AI support stack with DevRev, hit a 70% resolution rate across every customer segment, and now saves $4.5M a year.

Company

The platform runs financial workflows for more than 500,000 small and midsize businesses, with 8 million members and users across in-product, web, and mobile. Every day, millions of transactions flow through it — and every one of them is a potential support conversation.

Challenge

Their existing AI support solutions were not keeping up. Resolution rates were low, coverage across product surfaces was uneven, and the team could see the cost of the gap: they had set an internal savings target of $5M a year that competitive AI tools were not going to reach.

They needed a platform that could operate at the scale of their transaction volume, deliver consistent answers across every channel a customer might use, and — before any of that — prove itself on their own data, not on a vendor demo.

Solution

The evaluation ran for three weeks against 200,000 real production customer queries drawn from every customer segment. The bar was concrete: exceed a 30% AI resolution rate on real questions, or the deal did not move forward.

DevRev's AI agent blew past the threshold. On the strength of that result, the team greenlit a full rollout across 100% of their customer segments in 15 weeks, spanning in-product, web, and mobile. In parallel, they deployed Computer for enterprise search to serve their internal teams — 600,000 search queries a year, answered instantly from the source of truth instead of routed through a person.

Impact

The rollout hit the numbers the team set out for, and then some.

The core lesson for the team was that the shape of the evaluation mattered as much as the win. Running 200,000 real questions through the system, in production shape, produced a signal that a scripted vendor demo never could — which is why the deployment moved as fast as it did once the number came back.