Identity Digital Innovation & Technology Culture

How is your team integrating AI and ML into the product development process, and what specific improvements have you seen as a result?

We’re gradually bringing AI into our workflow, using large language models to help with coding questions and some automated test generation. This has cut down on time spent troubleshooting and improved our code quality by catching edge cases earlier. It has also reduced the cognitive load on engineers, letting us focus more on core development tasks.

What strategies are you employing to ensure that your systems and processes keep up with the rapid advancements in AI and ML?

A few of us on the team stay up to date on AI and ML advancements and new tools. Some engineers are experimenting with running local LLMs implemented into their integrated development environments for coding assistance, and we regularly update those models using tools like Ollama. This keeps our workflows aligned with the latest capabilities in AI while ensuring our models stay relevant.

Can you share some examples of how AI and ML have directly contributed to enhancing your product line or accelerating time-to-market?

We’re planning to release an improved domain search experience using our AI-driven DomainEngine, which could significantly enhance our ability to recommend relevant domain names to our customers. On the development side, AI helps engineers get answers more quickly, especially when working with unfamiliar languages or frameworks. This has cut down bottlenecks and helped us ship features more quickly.

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

We sit at the foundational layer of the internet, which means we can’t choose between stability and speed. We have to deliver both.

A big part of how we do that is by treating our channel partners, the registrars who sell our domains, as a direct extension of our product team. They’re on the front lines with customers every day. When we pay close attention to their feedback, we get an early read on where the market is moving. The surge in demand for .ai domains from startups, or creators gravitating toward .studio and .live, showed up in those conversations before it showed up anywhere else. 

On the engineering side, we’ve built a decoupled architecture that gives us flexibility without overexposure to risk. Our core registry operations, including DNS resolution, run on infrastructure that is expected to be up 100 percent of the time. That’s non-negotiable. By separating that from our application and API layers, our teams can ship new features and iterate quickly without touching these mission-critical systems.

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

The one that stands out most is our role in launching GlobalBlock, a brand protection service we built in close collaboration with GoDaddy and the Brand Safety Alliance. To understand why it matters, you have to know what brand protection on the internet looked like before it. If you were a company trying to keep bad actors from squatting on your name, your only real option was defensive registration: buying up hundreds of domain variations you’d never use, just to block others from getting them. It was expensive, exhausting and still left gaps.

GlobalBlock flips that completely. We brought together Identity Digital and more than 40 other global registries under the Brand Safety Alliance to create the first cross-registry blocking service of its kind. A trademark owner can now block their brand name across more than 700 top-level domains in a single transaction. The GlobalBlock+ tier goes further, catching look-alike variations and typos that bad actors commonly exploit for phishing and impersonation.

What I’m proudest of isn’t just the product itself; it’s what it represents. Identity Digital has long been a pioneer in domain blocking through our DPML service.

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

We start by changing the question. It’s now “How do we manage domain registrations more efficiently?” but “How do we use AI to understand what a customer is actually trying to build?” That shift in framing changes what the team focuses on and ultimately what they ship.

From there, it comes down to two things: big swings and honest data. We give our product managers and engineers room to experiment with new technologies, including machine learning approaches that have no guarantee of working. But every experiment has to answer to the user. If we can’t show that something reduces friction for the person on the other end, it doesn’t matter how clever the engineering is.

The last piece is making sure our internal breakthroughs don’t stop at our own walls. When we build something that works, we ask how it can work for the broader ecosystem, too. Making our tools available as turnkey APIs for registrar partners is a direct result of that thinking. It means the standards we hold ourselves to internally end up raising the bar for how domains get discovered and sold across the industry.