Topic
30 facts from 5 episodes, in date order. These are what speakers said on the shows, each linked to the moment it was said. They aren't independently verified.
2019
Arvind Jain says Glean started in early 2019, before large language models and the term generative AI were widely discussed.
“Glean started in early 2019. You know, in those days, nobody was talking about large language models, and the term generative AI did not exist to my knowledge at the time.”
2019
Jain says Glean was probably the first company to use transformer models in the enterprise.
“we were probably the 1st company to use transformers in the enterprise, you know, in in that day”
2019
Jain says transformer models in 2019 could match content by meaning but could not generate text or reason as models do today.
“In 2019, the transformers had no capability to generate information, they had no, like, none of the reasoning capabilities that models have today.”
2026
The Glean Work AI Index 2026 found that 87% of surveyed workers use AI, 73% say it makes them more productive, and on average they report saving 13 hours per week.
“87% of workers now use AI. 73% say it makes them more productive, and on average, they report saving thirteen hours per week, a third of a full work week.”
2026
Only 13% of workers in the Work AI Index survey said their organization is performing significantly better as a result of AI.
“And yet only 13% say their organization is performing significantly better as a result.”
2026
The Work AI Index surveyed 6,000 knowledge workers: 3,000 in the United States and 1,500 each in the United Kingdom and Australia.
“So, it's 6,000 knowledge workers, 3,000 in the US. and then, uh, 1.5 K in each of the UK and Australia.”
2026
The survey was developed with eight founding members of the Work AI Institute, which is Glean's internal research center.
“it was developed in partnership with our eight founding members of the Work AI Institute, which is our internal research center at Glean.”
2026
Hinds says about 36% of AI sessions fail, meaning the worker must start over or do significant rework.
“We're seeing about 36% of all AI sessions fail, meaning a worker goes to use the technology and it's not successful.”
Undated
Glean uses out-of-the-box large language models, such as GPT, to synthesize answers from retrieved information.
“we don't actually use our own models, you know, we use out of the box models like, you know, GPT or Um.”
Undated
Glean uses a fine-tuned small language model for spell checking user queries.
“for spell checking, um, we, we have a fine tuned version of a language model that will do it for us.”
Undated
Glean builds source-system permissions into its indexing so it retrieves only documents the user may access.
“Index indexing technology is the permission for baked in right into our, you know, uh, index indexing system.”
Undated
Glean trains its custom models only on content it considers safe, to avoid leaking information through embeddings.
“we typically would train them on, um, content that we think is safe to train on.”
Undated
Jain says the shift to SaaS made finding information harder but also made enterprise data readable, helping make Glean viable.
“One of them was SaaS, the the whole SaaS transformation, um, which made the problems so much more worse. But also it actually made the problem tractable.”
Undated
Jain says Google's culture, where engineers had great autonomy and resources, strongly shaped his management style at Glean.
“Google was a really special company, like it was very different from.”
Undated
Jain uses a Glean agent to generate up-to-date tutorials on internal systems by checking design docs against recent Jira tickets and code commits.
“I actually ask Glean to actually, gen, you know, give, generate a latest, you know, up to date tutorial on that topic for me”
Undated
Jain says Glean had no churn dashboards until some customers left in a recent quarter.
“we had some churn that happened last quarter. And then, like, you know, suddenly got worried, like, okay, what's happening?”
Undated
A Glean finance employee used Glean to build a customer risk report drawing on Salesforce, shared Slack channels and product usage dashboards.
“look in Salesforce to get out the list of all the customers.”
Undated
Glean builds golden question-and-answer sets for each customer, often from real conversations such as Slack threads, to test changes.
“we build these golden sets, you know, like for each one of our customers.”
Undated
Glean uses large language models as judges to compare new answers with the golden responses.
“you also use AI to sort of, uh, cause, you know, like these are subjective answers. Then you have to actually also use LLMs as judge”
Undated
Glean checks each line of a model's answer against the input it supplied, using the match for citations.
“line by line, you know, we'll take every, um, you know, the response of the model.”
Undated
When Glean cannot find support for a line in its source material, it suppresses the line or omits the citation.
“when we don't see it, we either suppress it or we actually would. Don't show, don't show the citations.”
Undated
Jain says he does not shrink his team because of AI and instead wants each person to do ten times more work.
“I don't actually shrink my team size. Um, uh, I sure do grow it, but I want every to be able to do ten times more work”
Undated
Arvind Jain said a common Glean agent turns meeting recordings into action items and updates Salesforce notes automatically.
“you take these meeting recordings, you figure out, like, you know what you talked to the customer, uh, what were the action items? And then the agent goes, updates the notes, you know, in Salesforce with that.”
Undated
Jain said Glean's work on fine-tuning its own models for specific product use cases did not pan out, so the company used existing models instead.
“some of our fine tuning work, building, building models, uh, for specific use case within our product. Like, you know, didn't, didn't really pan out for us.”
Undated
Glean's Work AI Institute developed the AWARE framework to address AI agent security, governance and compliance.
“Glean's Work AI Institute developed the AWARE framework to tackle agent security, governance, and compliance.”
Undated
Pradeep Mannakkara says Cvent's pilot and rollout of Glean bought the company about nine months of time by satisfying business users' needs for self-service.
“one of the things that we realized with Glean as we kinda did that pilot initially and then rolled it out was that actually bought us probably about 9 months of time, candidly speaking”
Undated
Pradeep Mannakkara says Cvent's agent sprawl happened because the Glean platform enabled employees to create agents.
“the sprawl, so to speak, happened because of the Glean platform, and it enabled them.”
Undated
Pradeep Mannakkara says Cvent asked Glean for moderation capabilities and they arrived within roughly three to four months.
“we also worked with Glean, said, Hey, we need a way to moderate this stuff, cause it's going to be a hot mess. And I call it within a 3, 4 month time, the capability came in.”
Undated
Glean is a work AI platform that began as enterprise AI search, helping employees find relevant information within an organization.
“Sure, so Glean is a a work AI platform. Um, historically, we started in enterprise AI search, you know, solving the problem of how do we find the right, relevant information within an organization?”
Undated
Tsavo Knott characterizes Glean as an enterprise search product that finds a finished document but not how it was made.
“So glean, you know, you might have heard of Glean here and there, but it's kind of this, you know, enterprise search. It'll find you the artifact.”
Episodes