In our latest episode of What the Tech from Boast, we sat down with Arjun Puri, CEO and Co-Founder of Symbiotic AI, to discuss democratizing precision heart health, why SR&ED tax credits should be a badge of honor, and how personal tragedy sparked a mission to reduce variability in life-or-death medical decisions.
Arjun's journey started with personal loss. His mother had a heart attack. The family—including multiple doctors—researched treatment options extensively. Despite doing everything right, she had another mild heart attack shortly after, leading to a hospital readmission.
"That really stuck with me. It highlighted how difficult these treatment decisions were. Even when you think you're doing everything right, there's still uncertainty. I realized this wasn't an isolated experience—it's common. And there's variation in how these decisions are made by clinicians across the system."
Today, Symbiotic AI is building Revaz AI—an AI-enabled clinical decision support solution specifically focused on coronary artery disease, the leading cause of death and disease globally. Revaz helps clinicians choose between treatment options like medical therapy, stenting, or bypass surgery by generating personalized risk predictions for each pathway.
And they're doing it with SR&ED tax credits as a cornerstone of their capital strategy.
The Problem: Two Doctors, Same Patient, Different Conclusions
Arjun explained the challenge simply: "Two clinicians can look at the same patient and come to very different conclusions. That variability has real-life implications for people like you and me, our loved ones. There's a human cost and a system cost."
The human cost: Patients like Arjun's mother experiencing avoidable readmissions, additional procedures, prolonged suffering.
The system cost: Hospitals and health insurers bearing expenses that could have been prevented with more optimal treatment decisions the first time.
Why the variability exists: Treatment decisions today are complex and often rely primarily on experience or generalized guidelines from trials that may be 10 years old and don't represent the patient sitting in front of the clinician.
"Cardiologists are foremost experts in what they do. They train for years. We have so much respect for them. But there's a reality they face—they're doing the best they possibly can with the tools available to them today. We're trying to give them better tools so they can fight this battle more effectively."
The Solution: Revaz AI – Personalized Risk Predictions for Heart Health
Revaz takes comprehensive patient data and generates personalized risk predictions for each treatment pathway applicable to that patient.
Instead of relying on generalized guidelines, clinicians get a patient-specific view of expected outcomes using data that's already being collected—but that doctors don't have time to crunch.
"The goal of Revaz isn't to replace clinical judgment. It's to make it more precise and more data-driven with the data we're already collecting. It seemed like a feasible problem to solve, but also a very impactful problem."
The key insight: It's not enough to build an accurate AI model. You need clinicians and end users to trust it, understand it, and actually use it in real decision-making moments.
Building Advocates, Not Just Customers
One of Arjun's most important realizations: "We couldn't just approach this as a modeling problem. If it doesn't fit into real-world clinical workflows, it won't matter how good the models are. It won't be used."
Symbiotic AI leaned heavily into co-developing with end users—conducting customer discovery with cardiologists every single week across Australia, South Korea, India, the US, Canada, and Taiwan.
"The core problem is consistent across jurisdictions. The workflows and constraints can differ, but the variability issues are relatively common everywhere Revaz would ultimately be used."
The goal: Build something clinicians would be upset to lose. Build something they'd advocate for to their hospital CEOs and CFOs. Discover friction points and understand how the information would actually be used.
"We wanted to build a minimum viable product that's much more aligned to how clinicians actually think and work every day, rather than what is technically possible with the tech we have today."
The result: Symbiotic AI is now moving into prospective trials with marquee partners like the University of Ottawa Heart Institute, the Ottawa Hospital, and heart cath labs in Edmonton and Calgary.
The Alberta Ecosystem: Why Arjun Stayed
When we asked Arjun about building in Alberta, he emphasized the ecosystem support and talent migration post-COVID.
"It doesn't hurt that you've got beautiful mountains close by. But over the past few years, we've seen migration of talent wanting to explore what's outside major metropolises. There was a big push 5-7 years ago to diversify Alberta's economy—to think about what other problems we can solve, where the transferable skill sets are from traditional energy sectors, and how we can apply that success to tech, pharma, or digital health."
That diversification, combined with talent migration, has led to flourishing entrepreneurship in Calgary and Edmonton.
Support sources Arjun mentioned:
- University of Calgary innovation programs
- Platform Calgary
- Government of Alberta programs
- Strong advisor networks
SR&ED Tax Credits: A Badge of Honor, Not Just Funding
One of the most powerful moments in our conversation came when Arjun emphasized the strategic importance of SR&ED tax credits.
"Scientific research and experimental development tax credits have been a key part of our capital strategy as we've been building in areas where there is uncertainty. If you aren't doing that already, work with the right partners to explore whether it's applicable to you. It can significantly influence how you decide to grow your company."
Why it matters beyond cash:
"It de-risks you for the future of your company. We often talk about Canada being the space where fundraising is tough. But it's tough because a lot of programs are available to support you through early R&D. Investors—especially in the VC community—want to see you've taken advantage of these programs as a building block before they say, 'It's de-risked enough. I see your vision. I want to buy into it.'"
Arjun's perspective: "It's a badge of honor. More people should be touting, 'Yeah, I got SR&ED because I earned it.' It's an entitlement."
The virtuous cycle: SR&ED validates you're tackling technological uncertainty ? demonstrates to investors you've de-risked early R&D ? proves the government believes in what you're doing ? unlocks venture capital for scaling.
Problem-Obsessed, Not Solution-Obsessed
Arjun shared a philosophy that defines Symbiotic AI's approach:
"With the team we've built—both our core team and fantastic advisors—it allows us to be problem-obsessed, not fall in love with the solution. It's ultimately the problems we're solving that customers and end users are going to value."
The scientific method in practice: Run experiments to get signal. Scale experiments to get more data. Decide how you want to grow—private investment, organic product revenue, or channel partnerships with large incumbents.
"That's the segue into thinking about how you want to grow your company next once you've built that team. The critical ingredient has been making sure we bring on people who are passionate about the mission and the problem we're solving."
What's Next: Prospective Trials and Market Entry
Symbiotic AI has completed retrospective efficacy and health economic studies showing how accurate Revaz is and what potential savings to health systems and insurers can be.
Now they're moving into prospective trials to demonstrate how this works in real-world clinical settings.
The challenge: Clinical data is complex, inconsistent depending on who's collecting it, and fragmented across various sources and systems. A big part of Symbiotic's work has been making that data usable in a way that produces reliable, clinically meaningful outputs.
Early signals: Clinicians across six countries have validated the core problem and expressed interest in the solution. The MVP is aligned to how they actually work, not just what's technically possible.
Key Takeaways
Problem-obsessed beats solution-obsessed – Focus on the problems customers value, not falling in love with your solution.
Build advocates, not just customers – Co-develop with end users until they'd be upset if the product was taken away. They become your evangelists.
SR&ED is a badge of honor – It validates you're tackling genuine technological uncertainty and de-risks you for investors. Tout it proudly.
Responsible AI requires workflow integration – Accuracy isn't enough. If it doesn't fit real-world clinical workflows, it won't be used.
The virtuous cycle of non-dilutive funding – SR&ED ? validates innovation ? proves de-risking ? attracts venture capital ? scales company.
Alberta's ecosystem is thriving – Talent migration post-COVID + economic diversification from energy = flourishing health tech community.
Personal tragedy sparks powerful missions – The most impactful companies solve problems founders experienced acutely and discovered were systemic.
Listen to the Full Episode
Want to hear Arjun's full story about his mother's heart attack, why SR&ED should be a badge of honor, and how Symbiotic AI is reducing variability in life-or-death medical decisions?
Listen to the full episode of What the Tech from Boast.
About What the Tech from Boast
What the Tech features conversations with brilliant minds behind new and exciting tech initiatives. Hosted by Paul Davenport, Boast.AI's Head of Content.
Learn More About R&D Tax Credits
If you're innovating in health tech, AI, or any industry and want to access non-dilutive funding that validates your work, Boast.AI can help you maximize Canadian SR&ED tax credits.
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