Glean says its context layer cuts token use by 30 to 50 percent

Transcript

Transcribed by machine (Whisper) from the episode’s audio, so a word may be off here and there.

1. 15:09

   So we are fundamentally the context and intelligence layer for your enterprise. So it's a platform. You bring it, you connect Glean with all of your enterprise systems. And now we understand, like, of course, you know, all the data, all the knowledge where it sits. We connect with structured data systems like Databricks and Snowflake. We connect with unstructured systems like your documents. And once you've connected Glean with all of your enterprise systems, we now become that gateway.

2. 15:36

   And AI, think of us as an AI gateway. And it has a few different capabilities. One of the capabilities is that we can actually retrieve any information from any system, given any task, any question that is given to us. Another one is that we can actually help you take an action inside those enterprise systems with the right security. Yet another one is that, you know, you can actually ask us, hey, how is this task being done today in the enterprise? And we can actually bring the right sort of.

3. 16:00

   you know, with mind, like, you know, how work gets done. Actually, you can do that? Like, you can ask Lean saying, hey, how does this person do, again, using this group? Is this part of a task? Yes. Yeah, so we'll actually go back and share that because behind the scenes, we're building task-based memory. We're building personal, individual memory. Like, for any given person, we deeply understand who they are, what role do they play in the enterprise, how do they like to read and write, what are their preferences, what are they, what are the things that are expert on, and what are the things they need to get done this week.

4. 16:27

   So we know all of that. And this memory infrastructure is also another piece of infrastructure that we build as part of this platform. And so now this platform is available for all of your AI applications inside a company to use. So you may be building some agents, let's say building those agents in Vertex in Google or Bedrock in AWS. And so you'll connect it with that clean MCP gateway, which brings all of that enterprise context and intelligence into those agents now.

5. 16:54

   So when these agent builder platforms now, they don't have to go and figure out how to connect with all enterprise systems. As an agent builder, like all of that connectivity is pre-established for you. So you just focus on what task you're trying to actually go and build. Similarly, we can actually serve as a backend inside your cloud co-work or chat GPT. And we just make those tools better, make them more company context aware, smarter, you get better answers.

6. 17:20

   and we actually also make them faster and cheaper. Because interestingly, what happens with AI today, Cloud is actually super smart. You can go and give it an arbitrary task and ultimately it'll figure out how to solve that. But it does it in a brute force manner. It'll spend lots and lots of cycles, first assembling just even the raw materials that it needs, the basic context to actually finish this task and then starts to work with and apply the model intelligence on it.

7. 17:45

   So that first part of that, we can actually short circuit. cloud can just instantly get all the context through clean you know our customers have reported we have actually done studies that it actually reduces token consumption by 30 to 50 percent so that's a that's a big use case in fact a lot of companies actually right now investing in glean for that reason because cost is a real real issue for them so that's so coming back to your question so that actually makes us the platform right you know it's a platform that makes all your other AI agents and your AI products more capable, better, faster, cheaper. But then we do come with our own first-party products. We have our own agent builder. We have our own Glean co-worker, which we think is powerful, more powerful. And in fact, you can think of it as a super set of

8. 18:31

   ChatGPD, Claude, Gemini all combined into one. So that's our strategy. So build that platform, build it in an enterprise-grade manner with the right securities, safety, governance, capabilities but then also keep investing in our end-user applications. So you will continue to encourage other agents to be built on top of Glean? 100%. In fact, our R&D team is divided into two. There's a core platform team which delivers this context and intelligence platform to the enterprise and then there is the the Glean co-work team, which is building the first-party product experience on top of this platform, but they get no extra rights to use this platform than what Cloud and ChatGPD gets.

9. 19:16

   In fact, that's the working model. The platform team doesn't even test Glean co-workers. They actually only work with Cloud and ChatGPD as the primary test platforms. How much of the use of