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