From Prototype to Production: How Perk Built a Voice AI Agent That Makes 10,000 Calls a Week
What happens when you combine a real customer problem, a no-code prototype, and a team willing to listen to every single call?
In this episode of _Just Now Possible_, Teresa Torres talks with Steven Payne (Product Manager), Gabriel Stock (Senior Engineering Manager), and Philipe Steiff (Senior Software Engineer) from Perk—a company that helps businesses eliminate "shadow work" like travel booking and expense management. They share how they built a voice AI agent that calls hotels to verify virtual credit card payments, preventing travelers from arriving to find their rooms unpaid.
What started as a hackathon experiment in Make.com became a production system handling over 10,000 calls per week across multiple languages. Along the way, the team learned hard lessons about prompt engineering for voice (numbers, pronunciation, and a very "Karen-like" first version), how to break a single monolithic prompt into structured conversation stages, and why listening to actual calls beats any amount of theorizing.
You'll hear how they:
- Built a working prototype without writing a single line of backend code
- Structured the call into discrete stages (IVR, booking confirmation, payment) to improve reliability
- Created two eval systems: one for call success classification, another for conversational behavior
- Scaled from five calls a day to tens of thousands per week while maintaining quality
This is a detailed look at building AI for real-time human interaction—where the stakes are high and the feedback is immediate.
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Building an AI Sleep Coach: How Rest is Making CBTI Principles Accessible to DIY Sleep Hackers
What if you could get personalized sleep coaching—inspired by the same principles that cost thousands of dollars and have year-and-a-half waitlists—through a voice AI that checks in with you every morning?
In this episode of Just Now Possible, Teresa Torres talks with Martin Siniawski (CEO and co-founder) and Ignacio (CTO) from Rest about how they built an AI sleep coach inspired by Cognitive Behavioral Therapy for Insomnia (CBTI) principles. The journey started when they noticed users of their podcast app were listening to content to fall asleep, explored sleep audio solutions, and eventually pivoted to an AI-powered voice coach when LLMs emerged.
They share how they evolved from basic chatbots to a sophisticated voice-first system with memory, dynamic agendas, and RAG—all while navigating the tricky line between wellness and medical products. Their "one bite of the apple at a time" approach to building AI offers practical lessons for teams tackling complex, personal AI products.
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Turning Vendor Chaos into Answers: How Xelix Built an AI Helpdesk
Accounts payable inboxes can see 1,000+ vendor emails a day. Xelix's new Helpdesk turns that chaos into structured tickets, enriched with ERP data, and pre-drafted replies—complete with confidence scores.
In this episode, Claire Smid (AI Engineer), Emilija Gransaull (Back-End Tech Lead), and **Talal A.** (Product Manager) walk us through how they scoped the problem, prototyped with “daily slices” (Carpaccio-style), and built a retrieval-first pipeline that matches vendors, links invoices, and drafts accurate responses—before a human ever clicks “send.” We dig into tricky bits like vendor identity matching, Outlook threading, UX pivots from “inbox clone” to ticket-first views, and the metrics that prove real impact (handling time, stickiness, auto-closed spam). We close with what’s next: targeted generation, multiple specialized responders, and more agentic routing.
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When AI Becomes Your SRE: How Incident.io Is Automating Incident Response
When your site goes down, every second counts. For years, Incident.io has helped engineering teams coordinate through chaos—getting the right people in the room, keeping stakeholders informed, and restoring order fast.
Now, they’re building something new: an AI SRE that can actually help diagnose and respond to incidents.
In this episode, Teresa Torres talks with Lawrence Jones (Founding Engineer) and Ed Dean (Product Lead for AI) about how their team is teaching AI to think like a site reliability engineer. They share how they went from simple prototypes that summarized incidents to a multi-agent system that forms hypotheses, tests them, and even drafts fixes—all from within Slack.
You’ll hear how they:
- Identify which parts of debugging can safely be automated
- Combine retrieval, tagging, and re-ranking to find relevant context fast
- Use post-incident “time travel” evals to measure how well their AI performed
- Balance human trust and AI confidence inside high-stakes workflows
This is a masterclass in designing AI systems that think, reason, and collaborate like expert teammates.
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Building Trainline’s AI Travel Assistant: How a 25-Year-Old Company Went Agentic
Trainline—the world’s leading rail and coach platform—helps millions of travelers get from point A to point B. Now, they’re using AI to make every step of the journey smoother.
In this episode, Teresa Torres talks with David Eason (Principal Product Manager) Billie Bradley (Product Manager), and Matt Farrelly (Head of AI and Machine Learning) from Trainline about how they built Travel Assistant, an AI-powered travel companion that helps customers navigate disruptions, find real-time answers, and travel with confidence.
They share how they:
- Identified underserved traveler needs beyond ticketing
- Built a fully agentic system from day one, combining orchestration, tools, and reasoning loops
- Designed layered guardrails for safety, grounding, and human handoff
- Expanded from 450 to 700,000 curated pages of information for retrieval
- Developed LLM-as-judge evals and a custom user context simulator to measure quality in real-time
- Balanced latency, UX, and reliability to make AI assistance feel trustworthy on the go
It’s a behind-the-scenes look at how an established company is embracing new AI architectures to serve customers at scale.
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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