Building an AI Sleep Coach: How Rest is Making CBTI Principles Accessible to DIY Sleep Hackers
Guests Martin Siniawski, CEO and co-founder, Rest Ignacio, CTO, Rest You'll hear how they: Discovered the sleep use case from podcast app user behavior (10% of users, but high willingness to pay) Used jobs-to-be-done research to identify "DIY sleep hackers" as an underserved segment Chose CBTI (Cognitive Behavioral Therapy for Insomnia) as their foundation—a clinically proven approach with 80% efficacy Evolved from text chatbot to voice-first AI using Vapi for voice and OpenAI for reasoning Built a memory system that remembers user context (like traveling, having a dog) with time-based relevance Created dynamic agendas that drive daily conversations based on sleep data, program stage, and user compliance Managed parallel development paths (text via OpenAI Assistants and voice via Vapi) Moved from massive system prompts to RAG for general sleep knowledge, keeping user data in prompts Navigated wellness vs. medical product positioning with clear guardrails against diagnosis and medication advice Used weekly error analysis with domain experts (sleep therapists) to drive product iterations Built LLM-powered evals for safety boundaries and experimented with Hamming for voice testing Resources & Links Rest – AI sleep coach app Vapi – Voice agent platform Rest uses Langfuse – Observability and evals platform Hamming – Voice testing platform AI Evals Maven Course by Hamel Husain and Shreya Shankar (Get 35% off with Teresa's affiliate link) Chapters 00:00 Introduction to Rest and Its Founders 00:33 The Origin Story of the AI Sleep Coach 02:07 Exploring the Podcast App and Sleep Use Case 03:35 Transitioning to a Dedicated Sleep Audio App 05:47 Understanding User Segments and Sleep Challenges 07:45 Introduction to the AI Sleep Coach 13:14 The Role of Voice in the AI Sleep Coach 18:46 Daily User Interaction and Features 21:30 Prototyping and Early Learnings 28:09 Navigating Ethical and Regulatory Concerns 30:39 Navigating the Line Between Health and Wellness Apps 31:00 Incorporating Adjacent Disciplines into the App 32:15 The Power of 24/7 Availability 32:53 Evolution of the Chatbot and Error Analysis 34:49 User Experience Improvements and Voice Integration 46:49 Implementing Memory and Personalization 50:18 Dynamic Agenda and User-Centric Conversations 57:37 Evaluation and Guardrails 01:00:05 Future Roadmap and Enhancements 01:03:38 Combining Data Layers for Enhanced AI 01:06:00 Conclusion and Final Thoughts