In this episode of Gradient Dissent, Lukas Biewald sits down with Arvind Jain, CEO and founder of Glean. They discuss Glean's evolution from solving enterprise search to building agentic AI tools that understand internal knowledge and workflows. Arvind shares how his early use of transformer models in 2019 laid the foundation for Glean’s success, well before the term "generative AI" was mainstream.
They explore the technical and organizational challenges behind enterprise LLMs—including security, hallucination suppression—and when it makes sense to fine-tune models. Arvind also reflects on his previous startup Rubrik and explains how Glean’s AI platform aims to reshape how teams operate, from personalized agents to ever-fresh internal documentation.
Follow Arvind Jain: https://x.com/jainarvind
Follow Weights & Biases: https://x.com/weights_biases
Timestamps:
[00:01:00] What Glean is and how it works
[00:02:39] Starting Glean before the LLM boom
[00:04:10] Using transformers early in enterprise search
[00:06:48] Semantic search vs. generative answers
[00:08:13] When to fine-tune vs. use out-of-box models
[00:12:38] The value of small, purpose-trained models
[00:13:04] Enterprise security and embedding risks
[00:16:31] Lessons from Rubrik and starting Glean
[00:19:31] The contrarian bet on enterprise search
[00:22:57] Culture and lessons learned from Google
[00:25:13] Everyone will have their own AI-powered "team"
[00:28:43] Using AI to keep documentation evergreen
[00:31:22] AI-generated churn and risk analysis
[00:33:55] Measuring model improvement with golden sets
[00:36:05] Suppressing hallucinations with citations
[00:39:22] Agents that can ping humans for help
[00:40:41] AI as a force multiplier, not a replacement
[00:42:26] The enduring value of hard work
