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Transforming the way people overcome paralyzing fears.
We are a health tech company based in Toronto building the future of exposure therapy. Accessible, measurable, life changing.
team behind
ExpO HeaIth
ai-native sensing & immersion platform unlocking exposure therapy challenges co-founder Cass B. experienced first hand for 10 years.
Cass Blonbou
Co-founder & CEO
Sandra Feng
Co-founder & CTO
Founding story: experiencing the problem
Years getting up close - Cass partnered with health professionals over multiple years to get a clear diagnosis for his cognitive disorder.
He then made progress spending thousands of dollars trying many available treatment options (most options did not help much or plateaued).
Hundreds of hours later, including studying the DSM-5 and promising research papers, we decided to roll up our sleeves and build better tools together for the gold standard treatment approach (exposure therapy) leveraging latest research findings and technologies.
Taking the leap - We left dream roles at Amazon and Microsoft in 2025 (ai devices & services product manager, xr design engineer) to launch Deep Interaction Lab.
Problem scale and cost implications
Building ExpO Health
Why now: in early 2025, we noticed a rapid acceleration of breakthrough technologies we believe key to create a 100x better exposure experience (simple, measurable, life changing). Many leading research organizations are competing, which drives performance up and costs down for anticipated infrastructure spend required to build and operate ExpO health.
We anticipate this trend will continue, and enable sound margins when we reach our growth stage. This introduces an opportunity to start R&D now, patent and go to market early, and scale with confidence.
How it started - We started working backwards from our wedge customers (people like Cass):
- Wrote a PR/FAQ the "day 1" way,
- Designed the core loop the "green jungle gym" way (sensing, ai processing, world-gen, feedback),
- Mapped the 4 areas of risks the Cagan way and prioritized de-risking outliers.
With clarity, we met a few advisor friends to stress test fundamentals (painful problem, sticky solution, big enough market, willingness to pay, relevant traction signals), and refine our plan to tackle headwinds (regulatory, distribution, business model).
Past experience building regulated hardware and software came in handy to spot and think through these proactively.
How it's going - We moved quickly on our initial prototype: dataset sourcing, cleanup, model training (compute gods if you hear us please have mercy on our GPUs).
In parallel we are talking with clinical therapists, health stakeholders, and organizations supporting deep tech startups.
If you are curious about our work, we'd love to hear from you!
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