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