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"Everyone from top to bottom really believes in the mission and feels like an owner. This is many times said at startups, but we really feel it here. Also, the customer and market opportunity that we have is second to none. The team feels like a family above anything else, and we all trust and support each other."

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GPTW Bay Area 2023-Glean

Employees say: 
 
"Everyone from top to bottom really believes in the mission and feels like an owner. This is many times said at startups, but we really feel it here. Also the customer and market opportunity that we have is second to none. The team feels like a family above anything else, and we all trust and support each other."

75 Best Small and Medium Workplaces in the Bay Area

These are the best workplaces in the U.S. that employ between 100 and 999 people.

GPTW Medium 2023-Glean

"Everyone from top to bottom really believes in the mission and feels like an owner. The team feels like a family above anything else, and we all trust and support each other."

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Glean | Fortune

Glean Company Profile, Stock Price, News, Rankings | Fortune

Want generative AI that actually delivers results? It's harder than it looks. Two leading AI startups that specialize in corporate readiness explore the models and techniques tech leaders must consider to deploy effective AI systems. Open source or closed? Fine-tune your LLM, or retrieval-augmented generation (RAG)? And just how good must your organization's data be? We dig in.

From Rag to Riches

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Great guys, so we're going to talk a little bit in this session about you know how enterprises can really get AI to work at scale and deployments, and a lot of that has to do the title of the session is from Rag to Riches and rag here is retrieval augmented Generation, which some of you. Mayo is a technique for helping AI get the data it needs and the context it needs to give good answers, but we know, you may not be familiar with my 22 panelists. So Dawa contextual AI, just 30 seconds. What is it? What, what do you guys do as a company? Yeah, so you mentioned rag. Reg is really about making Gen AI work on your data, right? So I think one of the The interesting thing about where we are right now is we have these amazing language models and amazing G AI, but the question is how do you make that work on your data, and the problem to solve there is the context problem. How do you give it the right context? So that's what we're focused on enterprise startup, uh, solving hard problems with context engineering, essentially. Got it. And, and Arvind, similarly, Glen, for those who don't know Glen, what's Glean do? Yeah, well, I mean, think of GLEA like GPD but inside your company. It's a place where your employees go and ask any questions or give it work, and GLEN will use all of the world's knowledge as well as all of the internal company's context to, to actually answer those questions or complete those tasks. And so in many ways, you know, I think, you know, the underlying technology, the concept that we use is similar, which is understand your enterprise, understand your people, your data, your knowledge. And bring that as useful context, ah, combine that with the power of language models to actually solve, you know, real tasks and answer real questions. And you guys have seen like tremendous adoption, right? You've just hit 200 million in, in annual revenue run rate, is that right? Yeah, that's right. So we just a couple of months back, you know, we, you know, announced that milestone, I guess we did today, but the, uh, but that's exciting, you know, business is growing for us, uh, rapidly. Great, um. So Dawa, you sometimes talk about sort of RAG 2.0, that there's this kind of new way of doing retrieval augmented generation. What is that really and how is that? How are people doing rag today that might be different than how people thought about doing it, uh, maybe right after chat GPT came out in, in 2023? Yeah, or even before that, right? So RAg is older than, than chat GPT. So, um, so my claim to fame originates is, is I'm an AI researcher, right? So I was the project lead on the original RAg project at Facebook AI Research. And at the time, the way we did things, vector databases didn't exist. The model we gave the data to was not called a language model. It was just a sort of generative model, but it wasn't a language model. So if you look at where we are now compared to then, it's just a very different idea even. So, so the core framework is still the same. You're still doing retrieval to give context to the generative models so that it can deliver value. Uh, but the way you do that is very different. Um, and that's why when we started contextual, we were really obsessed with this context problem. I still think that's the most exciting, most interesting problem right now. So we have these amazing language models, even if you freeze all technology, and you just say, OK, this is, you know, whatever GPT 4 5.2 or Gemini 3, we just keep that. There's still a decade of innovation that we can do just from focusing on the context. Um, and so the, the way you really want to do that is not just a vector database, right? There's a lot more going on. We're, we're doing agentic rag these days. We're using retrieval as a tool, and there are many more tools that you can use. So that's why we, we were looking for a new. We called it RAG 2.0. I don't know if that was the right name really, but I think the core observation was that what you're building there is a system. So it's not a model, right? The language model is maybe 10, 20%, and so it's the same for something like Glean, right? That's an extremely complicated system. And the language model is just a very small part of that, and I think that's why we need to have something like a rag, right? But then if you want to do that the right way, then you have to start from the systems assumption, and that's what, that's what we've been doing. And if you do that the right way, then you get much, much higher accuracy. So that's really what we're seeing kind of across the board is. Enterprise AI adoption is all about high value use cases and if you really want to get there, then you need high accuracy and that's what we do much better than anybody else. Interesting. And, and Arvin, for getting this to work well, Enterprise search is key. How, how do you do that successfully? And when you, uh, you know, there's a lot of data out there that a lot of enterprise, uh, uses of, of AI are not really scaling. They're not getting full deployment. What do you think the pitfalls are there? Why, why, where are companies going wrong? Yeah, I mean, see, first of all, enterprise environments are very, very complex. Um, there is lots of information, lots of data across hundreds of thousands of different systems. Data is permission, it's governed, like not everybody in the company can access our data. So when you sort of start naively, and you say that like, look, I'm gonna actually make AI work in my enterprise. I'm gonna connect AI with all of my systems, that's when you start to sort of run into. all these challenges. Why AI is so hard. Like, first of all, just connecting all of the enterprise information, like bringing that context into AI is hard because that means you need to build integrations to all these different enterprise systems that you have, understand the data models, understand the APIs, you know, to connect with that data. Then the second challenge you run into is that, well, oh, you know, I, I launched AI and I connected it with my Um, with my documents, but you didn't actually take care of security. And you have now AI having access to really sensitive information inside your company, and everybody in the company can then go and use it. So, so like when you, like, you know, like this, this, you know, we're all in the very early stages of making AI work. There was like this tremendous pressure from the executives, you know, uh, team, you know, within the company. Um, we had to do something, make AI work, and we didn't really, they properly like sort of prepare. For it and how to, how to make it happen correctly for us like at Glean like you know we've been in business for now almost 7 years, so we're just like, you know, like you know you're one of one of the original sort of enterprise AI focused companies. The, the first application that we deliver to our customers is, is search, which is, you know, we go in and connect Glean with all of our enterprise systems and we sort of understand more robustly, first of all, you know, what is all this. Information that you have inside the company, what information is up to date, uh, is high quality, what information has become obsolete at this point, because, you know, when you think about context again, you have to make sure that you're using the right context and making AI work on it as opposed to making AI work on scale. How much of that sort of data cleaning can be automated and how much of that is still kind of a manual process? Someone has to look at a human has to look at it, say, yeah, this is out of date. We're not actually going to put that in the database that we allow the AI system to access. Yeah, I mean, I think like when all these projects failed, like, you know, like if somebody needs to take the blame, so who took the blame, you know, the, uh, the startups, you know, and the product companies will tell you that, hey, your data sucks, you know, it is bad and it's your fault, right? So go and fix it. But the reality is that humans cannot fix, you know, the data. You cannot suddenly change our, our habits. You certainly can't like get people to. You know, actively maintain, manage information, keep it up to date, remove the steal information out. That's not how we work. Like, you know, it's, you know, we're going to work the same way as we work today. And so the, the right solution is for AI itself to figure it out. AI has human capabilities, and if you have the right AI, it will actually you know what information is good and what is not, and it will actually make careful use of, like, you know, you know, the information the right way and again, like, you know, the right security. Do, do you agree with that? Is that you can do that. Yeah, and, I, I think one of the most sort of misunderstood sayings I often hear from people is garbage in, garbage out, which is total BS. I think like what, what you want is garbage in information out, and that's why you want to use AI in the first place, right? So I think that is a very common misconception. So maybe we're not quite there yet, but In the long run, what you want to do is just apply AI to your data and all data in an enterprise almost by necessity is going to be garbage. So the question is how do you get the useful information out of that? Absolutely. I'm going to try to get one question from the audience in a second. So think of a question and I'll hopefully get. At least 11 in the audience, um, there's a lot of talk out Arvin, uh, uh, about sort of context engineering as like the next frontier of how to get Enterprise A out of work. What's your view on that and, and how are you, how is Glean working with things like MCP, you know, and scripts and all of these new ideas about giving more context to models? Yeah, I mean, I, I think that that's exactly like the new frontier of innovation is like for any given task, like how do you. build the right context and context is dynamic. Like it's not that like, hey, I have this task, and you suddenly just like you go into all these systems and pull some right pieces of information and you're done. It's actually iterative process where, you know, models and agents are going to be solving complex tasks and Every step of that execution, you have to sort of like figure out like what's the best way to bring the best information to these agents so that they can do their work properly. Um, so, so that's, that's, that's absolutely the key. Like, you know, what we do at GLEAN is, we are, I mean, we think of ourselves as we're trying to build, you know, your enterprise brain. Um, the idea behind GLEAN is you connect Glean with all of your different systems inside the company. So now we understand. You know who your different people are, um, what are the different business processes, projects, teams, uh, product lines that you offer, so sort of building a deeper understanding of your enterprise and also work like, you know, what are the different business processes and how do individuals go and actually execute those business processes today without AI, like, you know, what are humans doing? So sort of if you observe, if you actually connect with all the systems and you observe all the human activity, you're able to sort of then bring, you know, that collective human intelligence. of enterprise in one place and you do that and this is basically the repository of context and now from here, like when you actually ask AI to do some, you know, some piece of work you've got to sort of now, you know, from that repository pick, you know, the right context, you know, and that right context really is like, you know, well how did humans in the past did similar things, you know, in this company so that AI can sort of use that as the as the baseline to start and then do do its work and that's how it sort of really improve accuracy and. And and build these agents that are uh that are capable of actually extending, you know, that human intelligence and, and, and, and automate work. Alright, I wanna get, I wanna get one question for the audience. Who's got a question? And if no one does, I've got, oh, right over here is a guy, uh, with a question. Please state your name and where you're from. Rick Chavie with VX, uh, enterprise scaling platform. One question we, we are getting with our customers is around. If they have a lot of M&A and they've got multiple ERPs, e-commerce, CRMs, whatever, is coming up with that consistent framework unified to be able to apply the agents or any other technology for that matter to give that sort of single view of, of revenue execution or or whatever application you're looking at. So I'm, I'm wondering how. With your models, you know, how you sort through all that confusion, or in some cases systems may not even be really automated. You have to, yeah, and it's, do you need a single source of truth, or can you work across multiple systems? You should work across multiple systems again, so I, I think in a, in the complexity of an enterprise you are not going to have everything in one place. Right, so that's why we have things like MCP, some of the information you might want to index so that you can do faster retrieval over it, but, but in the end I think you just have to accept that you are dealing with a sort of messy reality of an enterprise and now you need to have an AI system that can deal with that. So part of that is understanding what you need to look for and so you know, doing the right tool calls, hooking up into the right ERP systems. A lot of the complexity that that that. that you solve with something like a glean or also with our platform is really about hooking up all this data. Now you have this, so you have this kind of data layer then you have The intelligence layer and then the missing part is the context layer. Like how do you get all of the right little bits of context from that in an iterative way so that intelligence can be useful. Arvin, I, I want to play something you said. You talked about having the AI learn the existing context and the existing workflows to sort of replicate those, which is probably a good place to start. But there's this tension there because a lot of people want what they really want to do with AI is reinvent workflows and change workflows. Can you do both at the same time? How, how does that work? Yeah, in fact, actually what I was saying was, see, there is that human intelligence, the human way of doing things today in your enterprise, and that's the, that's the starting point. So when you, when you, when you ask AI to actually solve a new problem, like either, you know, you let it loose, you know, with no sort of, uh, baggage, you know, to deal with, so to speak, like, you know, like let AI actually go and figure out. From, from scratch, like how to solve that task, or you say that, well, look, you know, like, you know, there's, there's some value in you knowing, like, you know, how we've been doing this work in the past. And you actually bring that context in and then, but you don't like, you know, you don't ask AI to sort of replicate that exactly what the, you know, what, how you were doing that business process before that actually just serves as the inspiration, the starting point, and AI can actually make its own decisions, you know, on top of that. So, that's how it works. So like, you know. Most of the use cases like as people are building these agents today, what we're seeing is that AI converges faster and is able to add value and you know, sort of like, you know, conforming to like you know how the business really wants to work if you bring in that, you know, context. Interesting, um, I wanna do a couple quick rapid fire rounds because we're, we're running a little low on time. Um, if what do you think is the most overhyped sort of, uh, AI technology at the at the moment? uh, what, what do you have to say? Agents, agents, Arvin. I, I, I, I, I would say that any technology, you know, any cool demo, like, you know, it seems to make a complex business process somehow like, you know, magical and automated right away because none of them is gonna work. Interesting, but I think on agents, so I think agents. Is most overhyped but also probably most underhyped. It depends on the time scale you're looking at. Interesting. I was, well, that's my next question is, you know, in, in the next year, what do you think's gonna come along? What, what, what, what's on the roadmap that you think's gonna make the biggest difference, to kind of enterprise implementation, Ivan, what do you think? Well, I mean, I, I think the models like are delivering amazing capabilities every year. Ah, this year was a big one for, for us in terms of the, you know, the, the Like the level of agents, the complexity of tasks we can handle through the thinking models. So I actually don't know what's coming next year, but again, like, you know, to, to what you said before, I think we're very happy with the underlying model technology. And, and now it's time for us to sort of really build robust integrations into enterprise systems and building a deeper understanding of. Um, your enterprise processes and automate them, but for me, like what, what, what's most exciting is that next year AI is going to become proactive. Today, like most of the AI use inside the enterprise is reactive in the sense that I, as an individual, have to go to AI, make it, ask it to do something for me, and it does it, and very few people do that. People don't have time to actually make it happen. So next year I think we're gonna see the, see this flip and AI. OK. Do, what do you, what do you see coming? Yeah, I, I think test time compute to your point is, is really, I think a lot of people are underestimating what this means. So this is really going to change everything. We are just getting started, um, and so when you hook up all your data and then you have these smart reasoning models, uh, that can do so much more, we are going to unlock all of these very complex use cases that really seemed like science. Fiction and that's already happening everywhere now. I mean, like even with things like coding, right, but how good we are at that now with AI. I, I don't think I would have expected that even a year ago. So, uh, it's going to come very quickly. I, I think next year is really going to be the year of, uh, of ROIs and through complex. Well, we're, we're out of time. We'll have to invite you guys back next year to see if your predictions are right. Thank you very much for, uh, for joining us. Thank you. Thank you.

Arvind JAIN, Co-founder and CEO, Glean Ahmed MAZHARI, President, Microsoft Asia Suthen Thomas PARADATHETH, CTO, Grab  Stephanie SY, Founder and CEO, Thinking Machines Moderator: Ayesha KHANNA, Co-Founder and CEO, Addo AI, Co-chair, Fortune Brainstorm AI Singapore

Brainstorm AI Singapore 2024: AI will have a $1 trillion impact in Southeast Asia, says Microsoft Asia president

Arvind JAIN, Co-founder and CEO, Glean Ahmed MAZHARI, President, Microsoft Asia Suthen Thomas PARADATHETH, CTO, Grab  Stephanie SY, Founder and CEO, Thinking Machines Moderator: Ayesha KHANNA, Co-Founder and CEO, Addo AI

Arvind JAIN, Glean, Ahmed MAZHARI, Microsoft Asia, Suthen Thomas PARADATHETH, Grab, Stephanie SY, Thinking Machines, Ayesha KHANNA,  Addo AI

Brainstorm AI Singapore 2024: How Quickly Can AI Be Scaled?

 SUSIE GHARIB: Julie, thank you so much for joining us. In your role as CEO of Accenture, you spend a lot of time talking to and advising CEOs who run Fortune 500 and Global 500 companies. And we want to just get a little bit of a window into what's top of mind with this group. So let me just begin with the topic of the pandemic and the tricky question of, do you continue remote working or do you begin to bring your workers back into the office? What are they asking, and what are you telling them is the best strategy right now? JULIE SWEET: Really, the topic is not so much about if they should bring workers back or even, in fact, how to bring workers back-- because I think most companies have really been focusing on health and how to manage it. It's really about the pace, and also the uncertainty about what remote working should look like in the future. And what I would say is that most CEOs are taking more of a, we know it's going to be different, but we're not sure yet what the right answer is approach. And so there's a lot more dialogue that's less what was really happening early on, which was, the world's going to change, and it's all going to go remote working. And there's much more, I think, a thoughtful approach, that there's going to be a lot of experimentation. SUSIE GHARIB: Let's talk a little bit about digital transformation. We've seen many companies that have stepped up their purchases of new technologies because of the pandemic. What questions are you getting now about the need and the pace for digital technology adoption, and what kind of technologies that they should be acquiring? JULIE SWEET: There are two kinds of companies right now. There's the leaders who pre-COVID had been investing a lot. And even pre-COVID, our research showed that they were doing twice as well as those who were well behind, in the bottom 25%. The leaders are asking us, how do we widen the gap even further? Those who were behind are asking us, how do we make up-- not just catch up, but actually leapfrog? SUSIE GHARIB: And what are you telling them? JULIE SWEET: Well, we're telling them, first of all, that there is a real opportunity in sometimes being slow means that you can leapfrog by looking at what the leaders have done and taking advantage of the experience of really the last five or six years. If you take a step back, what's really happened right now is that there's been a massive acceleration of what, in January, we thought would take 10 years. And we believe now it will really take five years to re-platform global business in the cloud and have digital transform every part of the enterprise. SUSIE GHARIB: Where does China fit in, given all of the tensions and uncertainties between the US and China? What are the questions or the concerns that the CEOs who you speak with have about the future of their China business, their China operations? And as a China expert yourself, Julie, what kind of moves are you telling them that they should make? What kind of advice are you giving them? JULIE SWEET: China remains an absolutely critical market. What we're seeing, though, is beyond China, there's a broader conversation about what is the crisis doing geopolitically and competitively in different parts of the world, because of the resilience of different countries-- starting with China, but not limited to China. And we are still seeing many companies looking at investments in China and really thinking through, how can we work with governments and other companies to find a path forward? But really, what's important is that there's a much broader conversation going on right now about Europe, about LATAM, and other parts of APAC as well. SUSIE GHARIB: I'd like turn to racial diversity. This has been a very important subject all across corporate America. As you know, many CEOs have made promises and pledges to their workforce to say that they want to do more and do better in terms of having a diverse workplace. When they talk to you about this issue, do you believe that they are taking the right steps to meet their goals? And what are you advising them of other ways that they can meet their goals? JULIE SWEET: First of all, I'm very optimistic, because I believe the commitment that you are hearing from CEOs across the country is real. And you see that in the work, for example, at the Business Roundtable. I've been participating in that work, as have most CEOs. And it's real. I think that's important, because it's really the first question. And the reason we know it's real is because companies are taking actions. At Accenture, for example, we set external goals about how we want to improve representation overall and at the leadership level. But the conversation that CEOs are having is very much about, how do we achieve these goals through data, measurement, and action, and having a clear plan. And that's why I'm so much more optimistic than ever before that we are going to move the needle. SUSIE GHARIB: What about what you're doing at Accenture? You've been very upfront with your 500,000 employees about your goals to make the company racially diverse. What are you doing? JULIE SWEET: So we're doing three things. First is we set the goals. And those goals are about both improving the number of managing directors, our leaders, as well as overall representation. The second thing we're doing is to create an action plan around how we're going to achieve those goals. And our entire leadership team has stepped up to be accountable for doing so. The third thing that we're doing is that we've introduced mandatory training about how to identify and speak up about racism. And this builds on what was already mandatory training about unconscious bias. But I think as a country and a company, we saw that there are differences between unconscious bias and actual racism. And we felt it was important to call out those differences and to help make people aware and ensure that they will speak up. SUSIE GHARIB: Some experts who I've talked to on this topic says it has to be intentional on the part of senior leadership, the CEO to hit these goals, and there should be incentives to meet those targets. What are your thoughts on that? JULIE SWEET: I think these goals are like every other business goal. Right? You hold leaders accountable. You set the goals. You have an action plan. You measure progress. And you hold them accountable. So let me share what we're doing at Accenture more broadly around inclusion and diversity. We're introducing a scorecard for our top 500 leaders globally. It's called the Shared Success Scorecard. And it has what we call the Fundamental Five. We will be measuring our leaders on sales, revenue, and profitability, as well as the retention of key talent, our best people, and inclusion and diversity goals being met. This is how you help move the needle, because you demonstrate that inclusion and diversity and investment in people are as important as our first three goals, which are all financial in nature. SUSIE GHARIB: Julie, you know, 2020 has been a pivotal year for just about every business all around the world. It's also been your first year as CEO of Accenture. From everything that you've observed and learned this year, what do you believe is the single most crucial action that every CEO should make now so they come out as leaders? JULIE SWEET: It's a great question. And I have to say, it's been quite a first year as CEO. But if you just take a step back, there's so much, of course, that you can say about what's necessary. But if there was only one action, I would say it's invest in your people. We are at a time of unprecedented change. And we were saying that pre-COVID. Our organizations and our people, and our communities, will only thrive if we are learners, if there is a deliberate strategy to bring people along the journey to embrace the change. And so whatever your business, every business today is a people business. And so the one thing I would say is you have to invest in your people, because then the organization will take you where you need to go. 

The Accenture CEO says workforce location and digital transformation are top of mind.

Julie Sweet shares insights gleaned from consulting with top CEOs

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The Accenture CEO sites workforce, digital transformation.

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