Most AI systems today rely on Retrieval-Augmented Generation (RAG) or APIs to access external knowledge. While effective in controlled environments, these approaches often fail in production when faced with real-world data: incomplete coverage, stale information, and unreliable sources. In this talk, we share practical lessons from building a web-scale retrieval layer designed specifically for AI agents. We’ll explore the key challenges of working with web data, dynamic content, inconsistent structures, adversarial noise, and the architectural decisions required to handle them.
Topics include retrieval strategies (search vs direct fetch), ranking signals (relevance vs trust), structuring unstructured data, and the trade-offs between latency, precision, and coverage. Through real-world failure cases and design patterns, attendees will gain a clearer understanding of how to build AI systems that remain grounded, reliable, and useful beyond demos.
Boris Toledano is the Co-founder and Chief Operating Officer of Linkup, a cutting-edge AI technology company dedicated to bridging the gap between Large Language Models and the real-time web.
