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Just Now Possible

Teresa Torres

How AI products come to life—straight from the builders themselves. In each episode, we dive deep into how teams spotted a customer problem, experimented with AI, prototyped solutions, and shipped real features. We dig into everything from workflows and agents to RAG and evaluation strategies, and explore how their products keep evolving. If you’re building with AI, these are the stories for you.

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  • 23 episodes
  • fortnightly
  • Avg 1 hr 5 min
  • English

Episodes 23

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  • #13
    Dec 11, 2025 · 1 hr 7 min

    Building Mowie: How a Concierge Service Became an AI Marketing Platform

    Guests Chris O'Connor – CEO, Mowie Jessica Valenzuela – Co-Founder, Mowie What we cover in this episode How Mowie evolved from a concierge marketing service to an AI-powered platform The "document hierarchy" architecture: how Mowie builds and maintains context about each business Why they moved from structured schemas to loosely structured markdown for intermediate processing Using Simon Sinek's Golden Circle framework to validate early product-market fit How Mowie generates quarterly content calendars and weekly posts across email and social media The three mini-calendars: public events, business-specific events, and recommended campaigns Building traceability so customers can see which context documents influenced their content Using customer approvals, edits, and regeneration requests as lightweight evals Connecting marketing performance back to point-of-sale data for attribution What's next: deeper attribution, omnichannel expansion, and digital out-of-home displays Resources & Links Mowie AI — AI marketing platform for SMBs Simon Sinek's Golden Circle — The framework Mowie used for early validation Chapters 00:00 Introduction to Mowie AI 00:06 Meet the Founders: Chris and Jessica 00:45 Understanding the Target Customers 01:35 Challenges Faced by SMBs in Marketing 04:20 The Evolution of Mowie AI 05:40 How Mowie AI Works for Businesses 08:16 Onboarding and Data Collection 14:56 Content Strategy and Calendar Creation 22:29 Technical Challenges and Solutions 29:41 Iterative Development and Feedback 37:23 Automated Content Calendar Approval 39:19 Content Calendar Creation Process 40:14 Marketing Pillars and Campaigns 42:11 Transparency and Traceability in Recommendations 45:23 Generating Weekly and Quarterly Calendars 48:24 Customizing Campaigns for Specific Events 54:15 Evaluating Campaign Performance 58:48 Customer Feedback and Document Hierarchy

  • #12
    Dec 4, 2025 · 54 min

    From Prototype to Production: How Perk Built a Voice AI Agent That Makes 10,000 Calls a Week

    Guests Steven Payne, Product Manager, Perk Gabriel Stock, Senior Engineering Manager, Perk Philipe Steiff, Senior Software Engineer, Perk What we cover in this episode How Perk's team identified an AI use case by connecting prior experimentation with a real operational problem Why they chose Make.com for prototyping—and shipped to production without touching backend code The evolution from a single prompt to structured conversation stages (IVR handling, booking confirmation, payment request) How breaking up the agent's task dramatically improved reliability Building two eval systems: classification for success rates and LLM-as-judge for conversational behavior Why the team still listens to calls manually even with automated metrics The challenge of prompt engineering for voice: numbers, booking references, and text-to-speech markup Lessons learned from expanding to German (prompts in native language improve results) How this project uncovered other operational problems they didn't know existed Resources & Links Perk Make.com – No-code automation platform used for the prototype Twilio – Voice/telephony provider 11 Labs – Text-to-speech provider (used in early experiments) Chapters 00:00 Introduction to the Team 01:54 Understanding PERK's Mission 02:59 Challenges in Travel Booking 07:27 AI Solutions for Customer Care 09:52 Prototyping with AI and Voice 17:00 Implementing AI in Production 25:51 Learning Through Trial and Error 26:40 Prompting Challenges and Solutions 27:58 Iterating on Prompts and Evaluations 30:08 Scaling and Production Challenges 32:43 Advanced Evaluation Techniques 35:32 Real-World Applications and Success 49:07 Future Directions and Expansion 53:53 Conclusion and Team Reflections

  • #11
    Nov 20, 2025 · 1 hr 6 min

    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

Showing 21–23 of 23 episodes