Paysafe improves planned vs delivered work predictability by 30% with DX
Paysafe, a leading payments platform, needed clearer visibility into how its large engineering organization was performing. With teams working differently across various tech stacks, leaders lacked a unified way to see where friction existed or whether improvements within engineering were translating into greater value for customers. “I’m always trying to figure out, are we actually getting more work out the door?” says Ahu Chhapgar, CTO.
Previously, Paysafe relied on internally built DORA and delivery metrics to understand how teams were operating, but these metrics only offered a partial view. They didn’t capture the underlying developer experience or show whether engineers actually felt enabled, and they lacked external benchmarks for context. Even so, Chhapgar had been hesitant to look for outside solutions after negative experiences with other tools. “The productivity tools I used before felt almost punitive: They would alert us to when a developer’s PR metrics were low, but they felt more designed to criticize. That’s not the culture we want,” he says. “We needed a way to understand whether engineers truly felt enabled, and whether the changes we were making were actually helping us deliver more value.”
That search for a more holistic view ultimately led Paysafe to DX. What stood out to Chhapgar was DX’s fundamentally different approach to understanding developer productivity. DX began by listening to developers—identifying where they experienced friction throughout the SDLC—and then layering in comprehensive system data. “When you ask developers, they’ll tell you what they experience, and when you combine that with what your systems are telling you, you get powerful insights into what’s really going on,” Chhapgar explains.
DX also offered an unbiased, external perspective with clear peer benchmarking, which was critical for aligning leadership on where Paysafe stood and where it needed to improve. “The data from DX wasn’t biased in any way,” Chhapgar says. “When you can show, ‘Here’s how we compare with our peers,’ that’s when people really start paying attention.”
Today, DX has become an integral part of how Paysafe runs its engineering organization. The company meets bi-weekly to review tech metrics, and with DX, leaders now have a holistic view of engineering health. For Chhapgar, the most meaningful shift has been watching teams act on DX data organically. “I was delighted to see my whole team, with zero instruction from any leader, take that input, go away, and come back a month later reporting the improvements they’d made,” he says. The result is a clearer, shared understanding of where engineering can improve and a more coordinated approach to making it happen.
Beyond improving overall engineering health, the platform has also helped Paysafe roll out AI tools more effectively. The company started with GitHub Copilot, then expanded to tools like Cursor and Windsurf as developers explored which ones fit best into their workflows. With DX, Paysafe can now objectively measure these tools, comparing AI users and non-users across key engineering metrics. “Being able to share an external party’s view of how we’re performing, and whether we’re improving or not, has made it easier to drive clarity and consensus at the executive level.”
With DX in place, Paysafe’s engineering organization now operates with greater predictability and shared understanding. Planned work delivered per quarter has risen from around 60% to over 90%, and discussions around engineering performance are grounded in data rather than perception. “Technology is now seen as an enabler,” says Chhapgar. “The strong uplift in product delivery has helped move the conversation beyond engineering execution and onto the next set of cross-functional improvements. Having an external perspective from DX gives us a neutral way to understand what’s improving and where to focus next.”
Looking ahead, Paysafe plans to continue its use of DX to empower engineers, guide improvement efforts, and measure the impact of AI tooling as adoption continues to expand. The goal remains the same: “We want to give engineers the tools and data to take ownership over their productivity and deliver impact,” says Chhapgar. “When high-quality engineers feel empowered, they move the needle in meaningful ways.”
