Aaron Linsky of Bridgewater Associates and Nikhil Benesch of turbopuffer explain how permission-aware retrieval over a 100-million-document research vault broke a conventional search setup, and why native filtering over a clustered vector index is needed to keep search fast without sacrificing recall.
Vinoo Ganesh, CEO of Kepler, explains how the company builds a verifiable financial research platform. Models cite numbers but never write them. Deterministic code performs the math, a citation registry built from more than 30 million SEC documents anchors every figure, and a post-processing filter strips anything untraceable. The talk covers hybrid search and re-ranking on Turbopuffer, tenant isolation, and why plain RAG produced thousands of wrong numbers in evaluations.
Dylan Couzon, developer relations engineer at Qdrant, argues that frontier-class models now run on hardware people can own, but the memory that makes an assistant personal still lives in someone else's cloud. He covers why owning inference gives autonomy while owning memory gives continuity, the CPU, RAM, and disk model for LLMs, why retrieval beats stuffing everything into the prompt, and a live offline demo of a drone building searchable memory with Qdrant Edge, plus opt-in shared memory.
Shawn, CTO of PolyAI, explains why voice agents are harder than text agents: voice adds time, and good conversation depends on adapting to the person on the line. He describes Dialog-RSN-1, an audio-native model that predicts turn-taking, replies in text with citations, and writes the transcript last for auditability, along with training on real noisy calls, latency and what agents should do while thinking, voice personality, benchmarking, harness ownership, and the shifting bottleneck from producing content to validating it.
Benjamin Clavié argues that the industry has mistakenly generalized from the "easy case" of coding agents to the much harder domain of general knowledge work. He highlights that code offers durable, explicit cues and narrow tasks, while knowledge work involves implicit meaning, contextual understanding, and agents needing to reconstruct intent. Clavié traces human solutions through "tool loops" and "organization loops," demonstrating how optimized tools and hierarchical agentic structures (like searcher agents generating memos) are essential to overcome performance ceilings and significantly bridge the "oracle gap" between AI and human knowledge workers.
Exa's CEO, William Bryk, discusses the impending AI search boom, predicting machines will soon outpace human searches dramatically. He explains Exa's evolution from a consumer search engine to an API for AI agents, driven by the realization that LLMs need external, 'perfect' retrieval to overcome the limitations of traditional search and combat societal misinformation. Exa leverages neural networks, embeddings, and customized features to provide fast, accurate, and cost-efficient information, with plans for a private data marketplace, aiming to deliver 'a year of research in a second' for AI agents and foster a deeply informed society.