Moov Innovation & Technology Culture

How does your team use AI in the engineering design process, and what benefits have you observed?

Our team leverages AI to enhance various stages of the engineering design process. For example, we use AI-driven tools embedded into our integrated development environments to automate repetitive tasks, such as generating boilerplate code or optimizing configurations, freeing up engineers to focus on higher-value activities.

Additionally, during the design phase, AI serves as a powerful resource for education and research. Team members utilize AI to dive into industry-specific or technical information, gaining a deeper understanding of the challenges they’re solving. Whether it’s used for exploring new programming frameworks, understanding complex regulatory requirements or reviewing detailed documentation for similar systems, AI accelerates knowledge acquisition and ensures the team is well-prepared before implementation begins.

Moov uses AI to automate and enhance learning, which improves efficiency and promotes creativity. This empowers the team to tackle design challenges with clarity and confidence.

What challenges have you encountered when implementing AI technologies in engineering design, and how have you addressed them?

In the payments space, one of the biggest challenges is the limited availability of publicly accessible data sets. This makes it difficult to train AI models on relevant data or validate their outputs effectively. To address this, we encourage best practices for validating AI outputs such as cross-referencing multiple sources and relying on team expertise to scrutinize AI-generated insights. While this requires extra effort, it helps ensure that AI contributes reliable value to our engineering design process.

Another challenge is maintaining a balance between AI automation and human oversight. While AI excels at speeding up tasks like code generation or research, it occasionally makes suggestions that may not align with our design principles or goals. We mitigate this by embedding AI in a collaborative workflow where it acts as a co-pilot rather than an autonomous decision-maker.

Adopting AI tools has required cultural adjustments. Some team members were initially skeptical of relying on AI or adapting to new workflows. We’ve addressed this by promoting transparency around AI tools, offering training and encouraging experimentation, which has turned AI into a trusted ally.

What excites you most about your team’s future when it comes to leveraging AI in innovative ways?

What excites me most is the transformative potential of AI to redefine how we approach engineering problems. With the rapid evolution of AI capabilities, we see opportunities to build systems that adapt to change and anticipate it. Imagine an AI-powered platform that dynamically optimizes payment routing or predicts and mitigates fraud in real time — these are possibilities we’re actively contemplating.

The ability to scale innovation quickly is a thrilling prospect. AI can turn what used to take months into something achievable within days, enabling us to test and deploy new features faster than ever. Moreover, as we integrate generative AI into our development processes, we anticipate uncovering entirely new use cases that push the boundaries of what’s possible in fintech.

Ultimately, the relationship between human creativity and AI’s computational power drives our excitement, keeping us at the forefront of innovation in the payments space.

How does innovation show up in your company culture?

Innovation doesn’t come from product teams dreaming up big ideas in isolation. The real innovation, the most valuable stuff, comes from the dot connectors, the people who pick up on patterns in the chaos. 

At Moov, those dot connectors are everywhere. Sometimes it’s a customer support engineer noticing the same question about configuring a widget over and over again. That pattern might be the key to what we should add, or more interestingly, what we should remove from the product. Because sometimes the most innovative move isn’t adding something new; it’s removing a feature, simplifying a process or eliminating a roadblock. 

At Moov, we’ve killed features that looked perfect on a whiteboard but wouldn’t move the needle in the real world. It takes courage to admit when something adds complexity instead of value, and it takes humility to roll up our sleeves and fix it again.

What’s one recent innovation that improved user or employee experience?

At Moov, our payment link feature lets businesses collect payments or send payouts. Originally, these templated payment forms took up to six seconds to load and become interactive on mobile browsers due to heavy use of JavaScript on the client side. Six seconds is an eternity when someone’s trying to pay you.

Rather than chasing small performance gains or vanity metrics, we rebuilt the experience from the ground up. The team reimagined the entire rendering strategy, delivering critical content first and then progressively hydrating the page as additional assets load. The result was sub-two-second load times on mobile and under one second on desktop. For customers, that time is money. 

Beyond the performance gains, it was also a meaningful win for the team. They had the autonomy and trust to rethink the problem from first principles, collaborate across disciplines, and ship something fundamentally better. Having the opportunity to collaborate on something new and ambitious, and then watching it succeed together, makes the work incredibly rewarding.

How do you balance experimentation with stability?

We treat payment infrastructure like home utilities. When you turn on a faucet, water should come out immediately and be clear. That’s the standard. In money movement, “boring” is a feature. 

Boring doesn’t mean we avoid experimentation. It means we’re disciplined about where experimentation happens. If it touches the movement of money, we move carefully. We test aggressively behind the scenes. We introduce change deliberately. Stability and trust come first. If we’re building a new developer experience or internal workflow, we move fast. We prototype. We gather feedback and iterate quickly. Balancing experimentation with stability isn’t about splitting the difference. It’s about knowing what must be rock-solid and where we have room to explore.

What’s your rule for releasing fast — and what KPI proves it works?

At Moov, we're building the systems that software companies use to move money. That means every release has the potential to affect real financial transactions, so our definition of "shipping fast" is a little different.

Our rule is simple, we don't consider a feature "launched" just because it's deployed to production. We release new capabilities to a small group of pilot customers first.

When we launched support for card payments, for example, we started with two pilot customers before making it broadly available. That approach exposes real-world edge cases early while keeping the impact of any issues intentionally small. Shipping fast shouldn't mean taking unnecessary risks.

The KPI we care about isn't deployment frequency by itself. Its deployment frequency measured alongside change failure rate, how often a release introduces an issue that requires a fix or rollback. Anyone can ship often. The real test is whether the failure rate stays flat or even improves as release cadence increases. If it starts climbing, you've bought speed by introducing hidden risk. When you're building infrastructure that moves money, that's not a trade we're willing to make.

Which standard or metric defines “quality” in your stack?

Moov isn't a layer bolted onto someone else's payment processor. We connect directly to the card networks (Visa, Mastercard and American Express), the Federal Reserve for automated clearing house and FedNow and The Clearing House for real time payments. Owning those connections means owning the failure modes instead of inheriting someone else's black box.

But that's not how we define quality. The real test is whether a developer can integrate with Moov without becoming a payments expert. Concepts like “For Benefit Of” accounts, ISO 8583 card messaging, National Automated Clearing House Association file formats, reconciliation and network rules should stay behind our API, not become our customers' problem.

We measure that with our quality processing rate: the percentage of transfers processed correctly end to end. If a customer has to learn how National Automated Clearing House Association files or card authorizations work just to troubleshoot an integration, we've failed. Our customers don't care whether an issue started with our cloud provider or a payment network. To them, it's all Moov and that's the standard we hold ourselves to.

Name one recent AI/automation shipped and its impact on the team or business.

We had a project that had been fully scoped for almost a year. It touched four engineering teams and, by our estimates, would have required a dedicated five-person team working for roughly three months. It wasn't blocked because it lacked value; it simply kept losing priority to smaller projects that could be delivered more quickly.

In April 2026, we tackled it using our internal agentic coding pipeline instead. One engineer directed the work across four codebases from start to finish and the project was completed in 29 days. That's roughly one-third of the calendar time we'd originally estimated and more than a 90 percent reduction in engineering effort.

That's the outcome we're looking for with AI. We're less interested in whether it can generate a function a little faster and more interested in whether it allows one engineer to successfully deliver work that previously required pulling an entire team away from their roadmaps. The goal isn't to replace engineers. It's to remove the coordination overhead that keeps great engineers from tackling bigger problems.