How Glean CEO Arvind Jain Solved the Enterprise Search Problem – and What It Means for AI at Work

Training Data

Years before co-founding Glean, Arvind was an early Google employee who helped design the search algorithm. Today, Glean is building search and work assistants inside the enterprise, which is arguably an even harder problem. One of the reasons enterprise search is so difficult is that each individual at the company has different permissions and access to different documents and information, meaning that every search needs to be fully personalized. Solving this difficult ingestion and ranking problem also unlocks a key problem for AI: feeding the right context into LLMs to make them useful for your enterprise context. Arvind and his team are harnessing generative AI to synthesize, make connections, and turbo-change knowledge work. Hear Arvind’s vision for what kind of work we’ll do when work AI assistants reach their potential.

Hosted by: Sonya Huang and Pat Grady, Sequoia Capital

00:00 - Introduction

08:35 - Search rankings

11:30 - Retrieval-Augmented Generation

15:52 - Where enterprise search meets RAG

19:13 - How is Glean changing work?

26:08 - Agentic reasoning

31:18 - Act 2: application platform

33:36 - Developers building on Glean

35:54 - 5 years into the future

38:48 - Advice for founders