Uploaded August 2025 | Updated September 2026, 2 weeks ago
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:
0:00 Intro
01:00 What Glean is and how it works
02:39 Starting Glean before the LLM boom
04:10 Using transformers early in enterprise search
06:48 Semantic search vs. generative answers
08:13 When to fine-tune vs. use out-of-box models
12:38 The value of small, purpose-trained models
13:04 Enterprise security and embedding risks
16:31 Lessons from Rubrik and starting Glean
19:31 The contrarian bet on enterprise search
22:57 Culture and lessons learned from Google
25:13 Everyone will have their own AI-powered "team"
28:43 Using AI to keep documentation evergreen
31:22 AI-generated churn and risk analysis
33:55 Measuring model improvement with golden sets
36:05 Suppressing hallucinations with citations
39:22 Agents that can ping humans for help
40:41 AI as a force multiplier, not a replacement
42:26 The enduring value of hard work
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:
0:00 Intro
01:00 What Glean is and how it works
02:39 Starting Glean before the LLM boom
04:10 Using transformers early in enterprise search
06:48 Semantic search vs. generative answers
08:13 When to fine-tune vs. use out-of-box models
12:38 The value of small, purpose-trained models
13:04 Enterprise security and embedding risks
16:31 Lessons from Rubrik and starting Glean
19:31 The contrarian bet on enterprise search
22:57 Culture and lessons learned from Google
25:13 Everyone will have their own AI-powered "team"
28:43 Using AI to keep documentation evergreen
31:22 AI-generated churn and risk analysis
33:55 Measuring model improvement with golden sets
36:05 Suppressing hallucinations with citations
39:22 Agents that can ping humans for help
40:41 AI as a force multiplier, not a replacement
42:26 The enduring value of hard work
