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
How Glean CEO Arvind Jain Solved the Enterprise Search Problem – and What It Means for AI at Work | Training Data podcast - Listen or read transcript on Metacast
