Avant Innovation & Technology Culture

How does your team stay ahead of emerging technology trends while scaling fast?

We stay ahead by being very intentional about where we experiment and where we standardize. As we scale, we invest heavily in strong technical foundations — clear architecture, shared platforms and disciplined engineering practices — so teams can move quickly without reinventing the wheel. On top of that foundation, we encourage small, focused bets on emerging technologies, whether that’s new data tooling, AI capabilities or developer productivity improvements. We learn fast, measure impact, and scale what works. Just as importantly, we stay close to real customer problems. Trends matter, but we only adopt new technology when it meaningfully improves outcomes for our users or our teams.

What recent product or feature are you most proud of — and what impact has it had?

One of the things I’m most proud of recently is how we’ve modernized and unified our customer and decisioning platforms. By simplifying complex systems and improving how data flows across the business, we’ve enabled faster product iteration, better risk decisions and a more seamless customer experience. The impact has been tangible: Teams can ship improvements more quickly, experiments reach customers sooner, and we’re able to personalize experiences at a much greater scale. It’s not a single flashy feature, but a set of foundational improvements that have unlocked speed, reliability and innovation across the organization.

How do you create a culture where innovation and experimentation are encouraged daily?

Innovation starts with psychological safety and clarity of purpose. We work hard to create an environment where teams feel empowered to question assumptions, test ideas, and learn from outcomes — good or bad. We set clear goals and metrics, then give teams autonomy in how they get there. Small experiments are encouraged, and learning is celebrated, not just success. We also prioritize strong feedback loops, so insights from customers and data quickly inform the next iteration. When people know their ideas are valued and that experimentation is part of the job, innovation becomes a daily habit rather than a special event.

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

Our rule is simple: Green tests mean it's merge-ready and anything on main is production-ready. Every PR requires passing automated tests across the full stack plus a peer review — no manual gate beyond that. If the pipeline is green and a reviewer has signed off, it ships. That keeps the path to production short, predictable and free of the risk that accumulates when changes sit unreleased.

We validate this with DORA metrics. Deployment frequency tells us we're not hoarding changes into risky big-bang releases. Change failure rate shows whether our automated coverage is actually catching regressions — not just running. Mean time to recovery keeps us honest about our ability to respond quickly when something does go wrong. Together, they give us a real-time feedback loop on the health of our delivery system, not just a feeling of velocity.

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

Honestly, our stack isn't exotic — the tools are largely the same ones good engineering teams use everywhere. What defines quality for us is the institutional commitment behind them.

Testing is a required part of every change, not a best-effort add-on. We've invested in the infrastructure to run large test suites fast enough that they don't create the bottleneck engineers learn to route around. A slow or unreliable pipeline doesn't raise quality, it just adds friction while people find workarounds. Keeping that pipeline trustworthy at scale takes ongoing investment and organizational will and we treat it as a first-class concern.

Code review reinforces the same culture. It's a craft conversation, not a compliance checkbox. At the system level, DORA metrics tell us whether all of it is actually producing stable, fast delivery. But the metrics are downstream of the culture. You can't DORA your way to quality if the underlying commitment isn't there.

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

We've been deploying generative AI to automate workflows that span multiple systems and require contextual judgment — work that previously needed a person to manually pull information from several places and make a call.

One example: We automated a customer-facing workflow that previously required human intervention at multiple points to read, interpret and act on incoming requests — the kind of task that doesn't follow a script and can't be handled with rules-based logic alone.

What made this tractable with AI is the judgment involved. It requires understanding context, inferring intent and making decisions that depend on synthesizing information across systems. We solved it with a combination of an AI agent and a workflow tool and the result was a meaningful reduction in the manual effort and lag that comes from humans acting as the connective tissue between systems.

That success has changed our internal calculus on what's automatable. We're now actively identifying other complex, multi-system workflows that are on the table in a way they weren't before.