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.
All content for Just Now Possible is the property of Teresa Torres and is served directly from their servers
with no modification, redirects, or rehosting. The podcast is not affiliated with or endorsed by Podjoint in any way.
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.
Building Trainline’s AI Travel Assistant: How a 25-Year-Old Company Went Agentic
Just Now Possible
1 hour 8 minutes
6 days ago
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.
Just Now Possible
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.
