Case Study — Mental Health & Health Tech

Maya Health integrates Speak AI to power practitioner tools for psychedelic-assisted therapy

Maya Health embedded Speak AI’s interactive media players and back-end APIs into their platform to capture, transcribe, and analyze practitioner sessions, generating insights that support better treatment outcomes for psychedelic-assisted therapy.

Platform integration | Practitioner tools | Treatment insights

The challenge

Maya Health is building a platform designed to support psychedelic practitioners, including clinics, retreats, and research studies. The team, including VP of Product Alex Edwards and others, recognized that effective psychedelic-assisted therapy requires careful documentation and analysis of practitioner sessions to understand what works and improve outcomes over time.

Practitioners needed tools to capture audio and video notes from clinical sessions without disrupting the therapeutic process. The platform needed to automatically analyze those recordings to generate insights that could benefit both practitioners and patients. And the aggregated data needed to inform the development of better therapeutic protocols and self-healing approaches.

  • Session capture without disruption: Practitioners needed embedded recording tools that fit naturally into their workflow without creating friction during clinical sessions.
  • Automated insight generation: Manually reviewing and analyzing session recordings is impractical at scale. The platform needed automated analysis to surface treatment patterns and outcomes.
  • Data for research and protocol development: Aggregated session data could inform the development of improved therapy approaches, but only if capture and analysis were systematic and consistent.

The solution

Maya Health integrated Speak AI’s front-end media tools and back-end APIs to build a comprehensive practitioner data collection and analysis system.

Interactive front-end media player and recorder

Speak AI’s embeddable media player and recorder were integrated directly into Maya Health’s platform, giving practitioners a native recording experience. Clinicians could capture session notes without leaving the platform or using separate tools.

Back-end APIs for data collection

Speak AI’s APIs handled the back-end data pipeline, routing practitioner recordings through automated transcription and analysis. This created a seamless flow from session capture to structured, analyzable data.

Automated session analysis

Recorded sessions and entries were automatically analyzed to generate insights about treatment patterns, session content, and practitioner approaches. This data supported both individual practitioner improvement and broader protocol development.

Research-grade data collection

The systematic capture and analysis of practitioner sessions created a growing dataset that could inform research on treatment outcomes, support evidence-based protocol development, and help improve self-healing approaches over time.

“I am thrilled to have this project underway, and I can’t wait to get this in front of our prospective client base. Thank you for your continued support!”
Alex Edwards, VP of Product, Maya Health

Key takeaways

  • Health tech platforms can embed recording and analysis natively: By integrating Speak AI’s media tools and APIs directly into the platform, Maya Health gave practitioners a seamless experience without third-party app switching.

  • Automated analysis creates scalable practitioner insights: Manual review of clinical sessions does not scale. Automated transcription and analysis made it practical to generate insights from every session, not just a selected sample.

  • Systematic data collection supports evidence-based therapy development: Consistent, automated capture across practitioners and sessions created a dataset that could inform treatment protocol improvements and research initiatives.

  • API-first integration enables custom health tech workflows: Speak AI’s API and webhook infrastructure allowed Maya Health to build custom data flows specific to their clinical workflow requirements rather than adapting to a rigid off-the-shelf product.

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