Team: Brivy

School Affiliation: Washington University, Texas A&M

Description: Brivy is an AI-powered tool that automates mental health session documentation, saving clinicians hours by instantly generating insurance-ready notes and session insights.

Problem:

Mental health clinicians in small-to-medium-sized private practices spend 10+ hours per week on documentation, leading to burnout, delayed insurance reimbursements, and less time with patients. Many providers struggle to complete notes on time, affecting both cash flow and work-life balance.

Solution:

Brivy automates documentation by recording sessions and instantly generating SOAP notes with ICD-10 codes and session insights. This eliminates manual note-taking, reduces clinician fatigue, and ensures faster, more accurate insurance reimbursements.

Market:

Our target customers are mental health practice owners managing 3-10 clinicians (therapists, counselors, nurse practitioners). Current research includes:
• Managed Facebook marketing for 4 mental health clinic owners since Jan 2024, gaining direct insight into operational struggles (delayed reimbursements).
• Conducted 6 in-depth interviews with mental health practice owners, confirming documentation as a top pain point.
• Hosted a webinar (Jan 2025, 40+ RSVPs, 15 live attendees): 100% reported interest in AI documentation, 67% were comfortable using AI in healthcare documentation.

Competition:

There are many sophisticated AI medical scribe tools (Abridge, DeepScribe, Heidi Health), but many are generalized and few cater specifically to mental health providers. Brivy fits a gap in the market by serving mental health clinicians working in small-to-medium sized practices. Our solution is tailored specifically for these practices through its note type, billing codes, and session insights.

Value Creation:

Brivy enables clinicians to see more patients by reducing documentation time, directly increasing revenue. Long-term sustainability comes from a SaaS subscription model with multiple pay tiers, targeting small-to-medium mental health practices.

The Team:

Connor Alder | MD Candidate 2028, Washington University
Peyton Smith | BS Computer Science 2024, Texas A&M

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