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.
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1:08:34
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1:08:34
Powering Government with Community Voices: How ZenCity Built an AI That Listens
How do you use AI to help city leaders truly hear their residents?
In this episode, Teresa Torres talks with Noa Reikhav (SVP of Product), Andrew Therriault (VP of Data Science), and Shota Papiashvili (SVP of R&D) from Zencity, a company that powers government decision-making with community voices.
They share how Zencity brings together survey data, 311 calls, social media, and local news into a unified platform that helps cities understand what people care about—and act on it. You’ll hear how the team built their AI assistant and workflow engine by being thoughtful about their data layers, how they combined deterministic systems with LLM-driven synthesis, and how they keep accuracy and trust at the core of every AI decision.
It’s a fascinating look at how modern AI infrastructure can turn noisy, messy civic data into clear, actionable insight.
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Building AI Coworkers: How Neople Is Making Agents Work Where You Work
What if your next teammate was an AI coworker — one that could answer support tickets, process invoices, or even draft your next email — and your _non-technical_ colleagues could teach it how to do those tasks themselves?
In this episode, host Teresa Torres talks with Seyna Diop (CPO), Job Nijenhuis (CTO & Co-founder), and Christos C. (Lead Design Engineer) of Neople, a company creating “digital coworkers” that blend the reliability of automation with the empathy and flexibility of AI.
They share how Neople evolved from simple response suggestions to fully autonomous customer service agents, the architecture that powers their conversational workflow builder, and how they designed eval loops that include their _customers_ as part of the quality process.
You’ll learn how the team:
- Moved from “LLMs will solve everything” to finding the right balance between code, agents, and guardrails
- Designed evals that run in production to detect hallucinations before an email ever reaches a customer
- Helped non-technical users build automations conversationally — and taught them decomposition along the way
- Turned customers’ feedback loops into eval pipelines that improve product quality over time
It’s a fascinating look at how one startup is rethinking what it means to “work with AI” — not as a tool, but as a teammate.
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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