Identity Digital Innovation, Technology & Agility

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.