We're always exploring new ways to put personal health data to work. As the world's largest personal health data platform that connects over 700 wearable devices and health data types, our job is to help healthcare organizations turn that data into meaningful products and experiences. That means we spend a lot of time thinking about what's possible, not just what's already been done.
Being AI-first isn't a tagline for us. It's a shift in how our teams operate.
How quickly we can move from idea to prototype. How ambitiously we can scope a project. How much creative energy we can direct toward solving hard problems in healthcare rather than wrestling with infrastructure.
Building Faster, Dreaming Bigger
The new AI development tools our teams have been using have fundamentally changed our internal velocity. What used to take two months to scope, staff, and figure out can now take a few days. That speed doesn't just save time, it unlocks a different kind of thinking. When the cost of trying something drops, your teams start asking better questions. They take on projects they might have shelved. They run experiments that teach you things you couldn't have learned otherwise.
We recently put that to the test in a way that had nothing to do with our core healthcare work, on purpose. Our CEO, Drew Schiller, wrote a blog post about how he built Fan Pulse, a concept app that crowdsources fan reactions to live sporting events using real-time wearable heart rate data. The idea: fans sign up, pick their teams, and when the game heats up, the app surfaces a collective "fan pulse,” an aggregate heartbeat for each fanbase in real time. A buzzer-beater upset? Watch the heart rates spike.
It was a deliberate departure from our day-to-day. And that was exactly the point.
What a Fun Experiment Taught Us About the Real World
Using AI-assisted development tools, the team rapidly built a backend, a website, and iOS and Android apps, and distributed everything internally for testing. Heart rate data is something we handle at scale every day. We have deep expertise in personal health data infrastructure. We were confident.
It was harder than we expected, and not for the reasons you'd think.
Most consumer wearables don't expose real-time heart rate data to third-party apps. In a market full of fitness trackers, only Apple Watch and a narrow set of WearOS-compatible Android devices actually support it. And even then, both platforms require an active workout session on the device to stream continuous heart rate — there's no lightweight "monitor for two hours" API. The result: battery drain, phantom workouts logged to your health history, and a user experience that felt anything but seamless.
The gap between "the data exists" and "the data is accessible in the way you need it" turned out to be significant, even for a team that lives in this data every day.
Why Experiments Like This Matter
Not every experiment ends with a product launch, and that's fine. What Fan Pulse gave us was real: hard-won insight into the current state of real-time wearable data access, stress-tested thinking in a completely different context, and a reminder of exactly the kinds of friction our platform exists to reduce for the healthcare organizations building on top of it.
That friction is exactly the problem Validic Inform was built to solve. Healthcare's core systems were designed for encounters: episodic, structured, clinician-initiated. They were never built to handle the continuous, high-volume data streams that wearables generate. Inform is the persistent normalization layer that sits between your device ecosystem and your EHR: 700+ integrations, a single data standard, and enterprise-grade governance. It's what lets developers and healthcare organizations stop fighting the infrastructure and start turning ideas into real products, faster than ever before.
That's what it looks like to operate as an AI-first company. Not just using new tools to go faster, but using that speed to run more experiments, learn more, and ultimately build better products for the customers who depend on us.
We had a great time building it. We learned real lessons. And we'll keep running experiments like it.
