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Building Trainline’s AI Travel Assistant: How a 25-Year-Old Company Went Agentic

Building Trainline’s AI Travel Assistant: How a 25-Year-Old Company Went Agentic

Listen to this episode on: Spotify | Apple Podcasts

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.

Show Notes

Guests

  • David, Principal Product Manager at Trainline
  • Billie Bradley, Product Manager, Travel Assistant at Trainline
  • Matt Farrelly, Head of AI and Machine Learning at Trainline

Key Takeaways

  • AI assistants need both scalable reasoning and deep domain context to be useful.
  • Tool design and guardrails are as critical as prompt design in agent systems.
  • LLM-as-judge evals make it possible to measure open-ended systems without massive labeling costs.
  • Even legacy companies can move fast when they embrace experimentation and tight PM–engineering collaboration.

Chapters

00:00 Introduction and Team Introductions
00:51 Overview of Trainline's Mission and History
02:30 AI Integration in Trainline's Services
05:08 Challenges and Solutions in AI Implementation
06:52 Building and Iterating the AI Travel Assistant
14:58 User Experience and Guardrails
22:26 Technical Challenges and Solutions
34:29 The Challenge for Product Managers in AI
34:55 Billy's Background in AI
35:42 The Rapid Evolution of AI Technology
37:14 Managing Information Overload
37:58 Collaboration Between Product Managers and Engineers
38:42 Trainline's Approach to Machine Learning
39:36 Scaling Up: From 450 to 700,000 Pages
40:21 Challenges in Data Retrieval and Processing
45:55 Evaluating AI Assistants
48:22 The Role of LLM as Judges
50:19 User Context Simulation for Real-Time Evaluation
01:06:56 Future Directions for Trainline's AI Assistant

Full Transcript

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