Transitioning from the fast-paced consumer worlds of Shopify, BlackBerry, and Wealthsimple to the high-stakes environment of senior living might seem like a leap. But for Dominique Simoneau-Ritchie, CTO of Inspiren, it was a natural evolution.
At Inspiren, the mission is deeply human: using technology to help seniors in assisted living and memory care live longer, healthier lives. They’ve recently been recognized as one of Fast Company’s most innovative healthcare companies of 2026, and after sitting down with Dominique, it’s easy to see why.
Here are three things I learned from our conversation about building AI that actually matters.
You Go Fast by Investing in the Right Technology at the Right Time
There’s a common myth teams face when building: You either invest in robust technology or you move fast. Dominique fundamentally disagrees. Her philosophy? You go fast by investing.
At Inspiren, she inherited a modern stack (TypeScript, Node) but focused her energy on building a data platform that aggregates hardware signals with Electronic Health Record (EHR) data.
"I fundamentally believe that you go fast by investing in the right amount of technology at the right time. You can build scalably, reliably, and securely with just the right amount of time for the stage that you’re at."
By building this foundation first, her team can now deploy new AI models much more quickly. It’s not about rip and replace, but about creating a springboard for future innovation.
Don’t Build AI Features. Build AI-Powered Workflows
One of the most refreshing parts of our chat was Dominique’s take on AI hype. While every company is rushing to bolt an AI assistant onto their product, Inspiren takes a workflow-first approach.
Their users (caregivers and clinicians) are often on 12-hour shifts, managing dozens of residents and constant interruptions. The last thing they need is a chatbot. They're looking for a way to prioritize who needs help right now.
Inspiren's approach is:
- Privacy by design: They use computer vision to detect falls, but the images are blurred on the edge (the device itself) so personal data never leaves the room.
- Invisible AI: Dominique doesn't lead with AI when talking to customers. She talks about solving the problem of ‘alert fatigue’ by using models to distinguish a resident asking for a glass of water from a high-risk fall situation.
- The real moat: As Dominique puts it, "The moat isn't AI. There’s going to be a new model tomorrow that’s better. The moat is our hardware, our years of data, and the proprietary workflows our users create."
Measuring the Toil of Engineering
We’ve all heard the debate: Does AI actually make engineers more productive? Dominique has the data to prove it. Since adopting AI tools, her team has more than doubled their PR (pull request) output per week.
She isn't just measuring volume for the sake of it. She’s looking at how AI can automate the toil — the repetitive, soul-crushing parts of engineering like writing tests or monitoring dashboards for exceptions.
"I want to be the most responsive engineering team. When we have an error, before a customer reports it, we’ve noticed and automatically fixed it. I am so excited about the toil that AI can automate for engineers."
By systematizing best practices, like using AI to proactively ensure 100% test coverage or HIPAA compliance, she’s making the ‘right way’ to build the ‘easy way.’
Listen to the full episode of Actually Intelligent to hear more from Dominique Simoneau-Ritchie about why your "moat" isn't a model, how to build for users who aren't "bleeding edge" AI adopters, and why the best AI features are the ones your customers don't even know are there.
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