Inspiration
As someone who’s seen friends struggle to find a therapist that clicks—wasting time, money, and emotional energy—I knew there had to be a better way. Choice paralysis, confusing insurance rules, and bad first matches were keeping people from getting the care they needed. That’s when I set out to build TherapyMatch: a platform that takes the guesswork out of mental health care.
What it does
TherapyMatch lets users complete a 5-minute assessment to share their mental health needs, preferences, and budget. Our AI matches them with qualified therapists, and users can book free 15-minute intro video sessions to test rapport. We also handle insurance verification, clear pricing, and a trusted review system—all in one easy-to-use platform.
How we built it
I started by mapping out core user needs (from first-time therapy seekers to budget-conscious users) and page structures. I built the front end with React for a responsive experience, used Node.js for the back end, and leveraged MongoDB to store user/therapist data. The AI matching algorithm integrates OpenAI API, and we synced therapist calendars with Google Calendar API to streamline booking. AWS hosted the platform, and Stripe API managed payment commissions.
Challenges we ran into
The biggest hurdle was refining the AI matching algorithm to balance clinical relevance (therapist specialization) with intangible factors like communication style. Insurance verification also posed challenges—different providers had inconsistent API data formats, requiring custom parsing logic. We also had to design a review system that was both user-friendly and compliant with moderation rules.
Accomplishments that we're proud of
We created a platform that truly centers user experience: progress-saving for assessments, clear insurance cost breakdowns, and a free intro session model that reduces risk for users. The matching algorithm now delivers accurate, personalized results, and we’ve built a responsive design that works seamlessly on mobile (critical for on-the-go booking).
What we learned
I deepened my understanding of mental health care logistics—from insurance billing to therapist licensing rules—and how to build technology that’s empathetic first. I also learned the importance of iterative testing: user feedback reshaped the assessment flow and matching criteria to be more intuitive.
What's next for this project
We plan to expand the therapist network to cover more regional insurance providers, add session reminder tools (via SMS/email), and refine the AI to incorporate long-term user feedback. We also want to partner with mental health organizations to make the platform more accessible to underserved communities.
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