GameChanger Innovation & Technology Culture

We aim to establish ourselves as an innovative leader, eager to embrace cutting-edge technologies and push boundaries to create the best possible products. For our users, we hope that integrating computer vision will streamline their experience, allowing them to continue enjoying stats and videos with less effort.

We are using computer vision to alleviate the burdens of people using our product, automating away anything getting in the way of coaches coaching and parents enjoying young athletes’ games.

What is the unique story that you feel your company has with AI? If you were writing about it, what would the title of your blog be?
Our early work in downtime detection focused on recognizing the rhythm of youth sports, pinpointing when play starts, pauses and shifts. These weren’t polished, high-budget broadcasts. They were handheld cameras, unpredictable framing and all the beautiful chaos that comes with youth games.

As one of the earliest hires on the computer vision team, I helped build the models and infrastructure that made this possible. That foundation challenged us to move beyond traditional rules and build systems that could adapt to real-world conditions.

Today, we’re taking that even further. Our action recognition work now focuses on helping AI interpret how the game unfolds, not just when. It’s about watching the game like a coach, not a camera.

What are you most excited about in the field of AI right now?
A major milestone came when our downtime detection models could reliably trim full-length games down to only the moments that matter, at scale and in the wild. That meant our AI wasn’t just working; it was interpreting.

I was directly responsible for building the model architecture and training pipeline that made this possible. After months of iteration and experimentation, seeing our system accurately cut out downtime across thousands of videos felt like watching the model understand the game for the first time.

Now, what excites me most is pushing our action recognition even further, moving toward systems that grasp the tempo, intensity and tactical nuance of each play across different sports. What we’ve built so far is just the beginning. There’s a whole wave of AI-driven features we’re preparing to roll out.

How do you learn from one another and collaborate?
One of the greatest challenges we faced was the sheer variability of youth sports video — shaky footage, inconsistent angles and unpredictable pacing. Our downtime detection and action recognition models had to adapt to all of it.

What started as scrappy prototyping evolved into robust, production-scale systems, thanks to close collaboration between CV, engineering and design. We still operate with a startup mindset: fast iteration, open feedback loops and constant refinement.

We treat our models like living products, constantly learning and evolving based on real-world data. Continuous learning isn’t just about staying current with research — it’s about staying close to our users, our data and each other.

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

Maintaining a culture of learning and continuous self-improvement is foundational for us. We champion learning through multiple avenues, from grassroots forums like hackathons, lunch-and-learn sessions and focus guilds to more structured investments such as conferences and instructor-led courses.

When early adopters and innovators see potential in a new technology, they demonstrate its value through demos, tech talks and other knowledge-sharing forums. That peer-driven adoption model allows great ideas to scale organically before they ever become mandates.

Once we decide something should scale across the product, strong technical foundations are non-negotiable. You can’t move fast on the frontiers of technology if your platform is fragile. We continuously invest in reliability, observability and developer productivity so experimentation can happen safely and at scale, without compromising the customer experience.

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

I’m a big fan of many things within the GameChanger platform, but if I had to pick a recently shipped experience that stands out, it would be the Game Highlights feature.

This experience takes game data and broadcast video and automatically produces a professional-quality recap featuring key moments with contextual play information. The highlights are crisp, engaging and give fans a quick way to catch up — and hopefully see their favorite youth athlete featured.

What makes this especially meaningful to me is how many capabilities came together to power it: scorekeeping and stats, play-by-play analysis,1080p live video streaming and computer vision-based gameplay detection. It’s a great example of cross-team alignment creating something greater than the sum of its parts. The reception from users has been incredibly positive, and it reinforces how powerful it is when we connect our technical depth directly to an emotional user experience.

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

Over my time at GameChanger, I’ve seen incredible learning and innovation translate into real impact for our users and business. Innovation requires two things: clarity and space. If you don’t give people clear goals, they drift. If you don’t give them space, you suffocate creativity.

We invest heavily in hiring great people, and when you hire great people, you have to trust them. That means allowing room for exploration, messy prototypes, and unconventional thinking. When something shows real potential, leadership’s role is to step in and ask: How do we turn this into something valuable at scale?

Daily innovation isn’t about grand gestures. It’s about empowering smart teams with clarity, trust and the freedom to build.