Founder and Former CEO/President of Supportiv
Founding CEO (2017–2019) · President (2019–2026)
I lost a close friend to depression. At his funeral, I made myself a promise: I would use everything I knew about technology to fix the gap that took him. He wasn't alone in his suffering — he just had no place to go that was immediate, anonymous, and human. That gap is why Supportiv exists.
Supportiv was the first emotional, mental, and social wellbeing platform to let users express their struggles in their own words — and connect them to human-led peer support in under 30 seconds, anonymously, 24/7. I also built the first application of AI in emotional and mental wellbeing, using NLP to match users to others facing similar struggles in real time.
HOW IT WORKED
A user types "What's your struggle?" in their own words. In under 30 seconds, they're placed in a small group chat — with others navigating similar emotions, facilitated by professional moderators. No video. No real names. No waitlists. Just immediate, stigma-free human connection.
Enterprise clients including Walmart, Optum, Costco, and Aetna CVS deployed Supportiv as a mental health benefit for their workforces.
AWARDS & RECOGNITION
PRESS
WHY SUPPORTIV WON
Most mental health platforms make you download an app, create an account, fill out forms, and wait days for an appointment. Supportiv did the opposite. One question — "What's your struggle?" — and you were talking to real people who had been through the same thing, in under 30 seconds, with no signup, no identity, no barrier.
No app to download. No account. No PHI collected. No clinical assessment forms. Just a single onboarding question and AI/NLP routing to precision-matched peers — live, in under a minute.
Every chat was synchronous, live-moderated, and clinically supervised. Competitors offered asynchronous forums, untrained volunteers, or appointment-based therapy. We offered something in between — and faster than any of them.
Peers weren't random. Our AI matched users to others navigating the same specific struggle — not just the same broad topic. A two-tier system extended this to occupation-based matching for healthcare workers, first responders, military, and teachers.
Most platforms stop at validation. Our moderators guided every conversation through a full arc: venting → empathetic validation → compassion from peers → coping skills → collaborative problem-solving → healing techniques. Clinical-grade outcomes without clinical barriers.
AI sorted for potential crisis situations from the first message. No open forums where trolls, bad advice, or judgment could reach someone in a vulnerable moment. Every chat protected, every session traceable to quality metrics.
There aren't enough therapists. There never will be. Supportiv's trained moderators weren't a workaround — they were the answer. By turning psychology students into professional peer facilitators, we created a scalable human layer that reached people long before a therapist ever could, at a fraction of the cost and without a waitlist.
The result: a platform that was simultaneously more accessible, more private, faster, and higher quality than anything else in the market — and the only one where a majority of users were men.
SUPPORTIV ACADEMY
Scale without quality is just noise. One of the hardest problems we solved at Supportiv was ensuring that every chat — across thousands of daily sessions — met a clinical standard of care. The answer was Supportiv Academy.
Working alongside our advisor Dr. Alejandro Martinez, we built a rigorous curriculum to transform psychology students into world-class peer support moderators. The program ran 100+ days, combining coursework, scenario testing, and clinical assessment — with an acceptance rate below 10%. More applicants were turned away than accepted.
Training didn't end at graduation. Our clinical team provided daily check-ins and weekly group supervision — continuous coaching modeled on how elite clinical practices develop their staff. Every moderator was always learning.
On the technology side, AI triaging flagged chats that needed clinical review in real time. Star ratings from chat participants fed directly back into the training loop — giving us a closed-feedback system that made quality compound over time, not degrade with scale.