2026-06-07 –, Doddington Forum
Tesco is applying AI and Data Science at scale to solve some of the most complex problems in retail. From personalisation to optimisation and decision support, our systems power millions of customer interactions and operational decisions every day. In this talk, we highlight how these capabilities come together in modern AI-driven customer experiences, and why Tesco is at the forefront of applying AI in real-world, high-impact settings.
We briefly introduce Tesco’s Meal Planner to highlight the technical challenges behind AI-driven customer experiences. A key challenge behind the scenes is translating recipes into products that customers can actually buy. We approach this by connecting recipes, ingredients, and products in a way that enables the system to move from meal ideas to a ready-to-shop basket. This requires balancing richer reasoning over customer needs and preferences with the practical realities of a live retail environment, such as a constantly changing product catalogue, cost, and availability.
We then turn to one of the most important aspects of deploying AI systems at scale: Evaluation and how it helps to ensure that the system behaves reliably. When AI assistants support customer journeys, even small errors can degrade the experience or lead to incorrect outcomes. We present our evaluation framework, which combines multiple techniques to assess both system behaviour and response quality. This allows us to identify issues early, enforce consistent standards, and continuously improve performance.
Overall, this talk offers a practical view of how Tesco applies AI and Data Science to real-world problems, combining strong technical foundations with robust evaluation to deliver reliable and impactful customer experiences.
Julie Huang is an AI Data Scientist at Tesco, where she works on production AI agent systems for retail, with a focus on evaluation, reliability, and agentic user experiences. She has contributed to Tesco’s Meal Planner Agent, working across LLM evaluation, scalable automated red-teaming and guardrails to help make AI agents safer and more measurable in real-world settings.
Her broader work spans applied machine learning, recommendation systems and agent evaluation. Beyond Tesco, Julie contributes to open-source research on terminal-agent reinforcement learning, where she has worked on scalable verifiable environments generation and RL post-training.
Kareem is an AI Engineer at Tesco working on the upcoming customer shopping assistant. Particular focuses include personalisation, guardrails, and the custom evaluation framework in use for the system at scale. Before this, he was a Machine Learning Engineer in the Personalisation team at Tesco, developing and scaling core personalisation capabilities.
Kareem is also the co-founder and CTO of Carbon Glance, a B2B SaaS startup in the climate compliance & analytics space - solving CBAM readiness for importers, manufacturers and service providers.
Kareem holds an MSc in Computer Science from the University of Edinburgh and a Computer Science BSc from the University of Southampton, where his research interests spanned deep learning over graphs and natural language processing.