Sequen AI

In this episode, Zoe Weil, co-founder and CEO of @sequen_ai, joins us to discuss how ranking systems can use live behavioral signals to optimize for purchases rather than clicks or search relevance alone. Zoe explains why approaches developed at data-rich social platforms don’t necessarily translate to retailers and how Sequen uses scroll speed, hover duration, and other in-session interactions to infer shopper intent without relying on user identity. We explore the company’s large event models and its architecture of specialized models that transform streaming events into features for downstream rankers. Zoe describes the infrastructure required to serve dynamic rankings within milliseconds, why the team chose Rust for its backend, and how evaluation harnesses connect model improvements to business outcomes. We also discuss the tradeoffs between advertising and organic revenue, continuous model improvement, and the boundary between useful personalization and surveillance. 🗒️ Full show notes: twimlai.com/go/780. 📖 CHAPTERS =============================== 00:00 - Introduction 02:56 - Sequin’s Ranking Intelligence Layer 07:26 - Difference with Search and Relevance 13:20 - Session Live Signaling 15:57 - Surveillance Capitalism 18:45 - Building a Low-Latency Stack in Rust 20:52 - Model Architecture 26:47 - Reranking Products and Surfacing Complementary Items 33:03 - Balancing Organic and Ad Revenue 34:56 - Learning from Implicit Feedback 36:42 - Infrastructure 39:34 - Recursive Stack and Evaluation Harnesses 42:31 - Recursive Ranking Intelligence 44:12 - Large Event Models 46:56 - Time-Series Modeling 48:49 - Complexity of Scaling Real-Time Feature Transformations 49:46 - Future Directions