Inside Just Eat's AI Lab: Voice Agents & Agentic Commerce

Inside Just Eat's AI Lab: Voice Agents & Agentic Commerce

Author: Demetrios May 26, 2026 Duration: 1:18:38

Guthrie Cooper (Senior Group Product Manager, AI & Robotics) and Nidhi Sharma (Global Head of Engineering AI & Incubation) from Just Eat Takeaway.com join the MLOps.community to pull back the curtain on how one of Europe's largest food delivery platforms is running an internal innovation engine. From autonomous delivery robots to agentic AI voice assistants, they share what it actually takes to build like a startup inside a 40,000-person company.


Inside Just Eat's AI Lab: Voice Agents & Agentic Commerce // MLOps Podcast #377 with Just Eat Takeaway.com's Guthrie Cooper (Senior Group Product Manager, AI & Robotics) and Nidhi Sharma (Global Head of Engineering AI & Incubation)


🤖 Delivery Robots — How JET partnered with RIVR and DELIVERS.AI to deploy physical AI ground robots in Zurich, Milton Keynes, and Bristol, and what the first pilots taught the team

🧠 AI Incubation at Scale — How Nidhi's team built a dedicated incubation unit to fast-track AI experiments without the red tape of a large enterprise

🎙️ AI Voice Assistant — The story behind JET's new voice-first food ordering experience, and the ML challenges of building a conversational concierge at scale

🦾 Physical AI vs. Software AI — Why deploying wheeled-legged robots in real cities is fundamentally different from shipping a model update, and the MLOps implications

🚀 Corporate Innovation Playbook — The frameworks Guthrie and Nidhi use to move from idea to pilot in weeks, not quarters, inside a large org

📦 Innovation as a Platform — How JET is thinking about turning its delivery infrastructure and AI capabilities into a reusable platform for new business lines

🔗 Startup Partnerships — What makes a good external innovation partner (vs. building in-house), and how JET evaluates robotics and AI startups for pilots

⚡ Agentic AI & Accessibility — How agentic AI is being used to make food ordering genuinely accessible for blind and low-vision users


Whether you're an ML engineer at a large company trying to get AI into production, a product leader navigating corporate innovation, or a startup founder looking to partner with a platform player — this conversation is packed with practical lessons.


🔗 Links & Resources:

Just Eat Takeaway.com: https://www.justeattakeaway.com

RIVR (physical AI delivery robots): https://www.rivr.ai

DELIVERS.AI (UK delivery robots): https://www.delivers.ai

Prosus (JET parent company): https://www.prosus.com

MLOps.community: https://mlops.community


⏱️ Timestamps

[00:00] AI Innovation Incubator Strategy

[03:16] Everyday Convenience Expansion

[07:03] Context Ownership in Ecosystems

[17:35] LLM Integration and Discovery

[24:02] Whoop Notifications Grievances

[33:01] Expanding Beyond Food

[48:20] Innovation Lab Failures

[51:22] Rory Sutherland's Alchemy

[1:03:23] Latency and Conversational Design

[1:13:42] Drone Delivery Efficiency

[1:18:06] Wrap up


#AgenticCommerce #VoiceAI #DroneDelivery


Hosted by Demetrios, MLOps.community is a space for honest, meandering talks about the real work of making artificial intelligence systems actually work. This isn't about hype or theoretical papers; it's about the messy, practical, and often surprising journey of taking models from a notebook into a live environment. You'll hear from engineers and practitioners who are in the trenches, discussing the tools, the frustrations, and the occasional breakthroughs that define the day-to-day. The conversations are deliberately relaxed, covering everything from traditional machine learning pipelines to the new world of large language models and even the intangible "vibes" of team culture and process. Each episode peels back a layer on what "production" really means, whether that involves deploying a predictive service, managing an agentic system, or maintaining reliability as everything scales. Tuning into this podcast feels like grabbing a coffee with colleagues who aren't afraid to dig into the technical nitty-gritty while keeping the tone conversational and accessible. It's for anyone who builds, manages, or is just curious about the operational backbone that allows AI to deliver value, offering a grounded perspective often missing from the broader conversation.
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