Knowledge Systems: The New GTM Stack — Jeffrey Wang, Exa

>> Hey everybody, I'm Jeff. I guess I was >> Hey everybody, I'm Jeff. I guess I was introduced, but I'm the co-founder of introduced, but I'm the co-founder of introduced, but I'm the co-founder of Exa, and today going to give a talk on Exa, and today going to give a talk on Exa, and today going to give a talk on turning go-to-market into an AI turning go-to-market into an AI turning go-to-market into an AI engineering problem in the spirit of engineering problem in the spirit of engineering problem in the spirit of this this this AI engineering fair. And just a quick AI engineering fair. And just a quick AI engineering fair. And just a quick show of hands just to like understand show of hands just to like understand show of hands just to like understand the audience, like raise your hand if the audience, like raise your hand if the audience, like raise your hand if you're a technical. you're a technical. you're a technical. Okay, great. Okay, so I kind of Okay, great. Okay, so I kind of Okay, great. Okay, so I kind of oriented this talk around like oriented this talk around like oriented this talk around like go-to-market go-to-market go-to-market as presented to to engineers. So, happy as presented to to engineers. So, happy as presented to to engineers. So, happy that I did that. that I did that. that I did that. Cool. So, first just to like ground the Cool. So, first just to like ground the Cool. So, first just to like ground the ground like what what Exa is cuz it's ground like what what Exa is cuz it's ground like what what Exa is cuz it's sort of relevant inside of this sort of relevant inside of this sort of relevant inside of this presentation. Exa is a Exa is a search presentation. Exa is a Exa is a search presentation. Exa is a Exa is a search engine for agents. engine for agents. engine for agents. Think like agents are really smart, but Think like agents are really smart, but Think like agents are really smart, but they don't have access to the web. We're they don't have access to the web. We're they don't have access to the web. We're like this web MCP web tool that agents like this web MCP web tool that agents like this web MCP web tool that agents can access. We power Cursor, we power can access. We power Cursor, we power can access. We power Cursor, we power Cognition, we power a lot of the AI Cognition, we power a lot of the AI Cognition, we power a lot of the AI ecosystem at this point. ecosystem at this point. ecosystem at this point. And And And before we start, I also just want to before we start, I also just want to before we start, I also just want to like talk about, you know, especially to like talk about, you know, especially to like talk about, you know, especially to the technical audience, like why should the technical audience, like why should the technical audience, like why should you even care? you even care? you even care? Like why should you care about Like why should you care about Like why should you care about go-to-market? I guess this audience go-to-market? I guess this audience go-to-market? I guess this audience cares about go-to-market cuz you chose cares about go-to-market cuz you chose cares about go-to-market cuz you chose to go to this to go to this to go to this go-to-market talk, but I think there's go-to-market talk, but I think there's go-to-market talk, but I think there's this like funny narrative right now, this like funny narrative right now, this like funny narrative right now, which is like people are like, "Oh, like which is like people are like, "Oh, like which is like people are like, "Oh, like product is the only thing that matters."

product is the only thing that matters." product is the only thing that matters." Or "Distribution is the only thing that Or "Distribution is the only thing that Or "Distribution is the only thing that matters." And there's all sort of like matters." And there's all sort of like matters." And there's all sort of like all sorts of like Twitter flame wars all sorts of like Twitter flame wars all sorts of like Twitter flame wars like like oh, is Glean going to succeed like like oh, is Glean going to succeed like like oh, is Glean going to succeed because they're really good at because they're really good at because they're really good at distribution, but they're like what what distribution, but they're like what what distribution, but they're like what what the heck is their product? And then and the heck is their product? And then and the heck is their product? And then and other people are like, "Oh, the like the other people are like, "Oh, the like the other people are like, "Oh, the like the the product needs to be super good cuz the product needs to be super good cuz the product needs to be super good cuz agents agents agents you know, agents shop for the product, you know, agents shop for the product, you know, agents shop for the product, so they'll shop the for the best so they'll shop the for the best so they'll shop the for the best product." And so, my view and my product." And so, my view and my product." And so, my view and my experience in the last few years is that experience in the last few years is that experience in the last few years is that you just kind of have to do both. Like I you just kind of have to do both. Like I you just kind of have to do both. Like I think you have to get product right and think you have to get product right and think you have to get product right and you have to get go-to-market right. Like you have to get go-to-market right. Like you have to get go-to-market right. Like you got to build this thing, it's got to you got to build this thing, it's got to you got to build this thing, it's got to be good, and then you got to get it into be good, and then you got to get it into be good, and then you got to get it into people's hands. If you don't do both people's hands. If you don't do both people's hands. If you don't do both things, then you don't have a company. things, then you don't have a company. things, then you don't have a company. So, that's kind of my view on the So, that's kind of my view on the So, that's kind of my view on the matter. And I I say like a really funny matter. And I I say like a really funny matter. And I I say like a really funny thing also is like as a technical person thing also is like as a technical person thing also is like as a technical person when you start a company or you start when you start a company or you start when you start a company or you start some some sort of project, like very some some sort of project, like very some some sort of project, like very much so the bias is like, "Hey, I'm much so the bias is like, "Hey, I'm much so the bias is like, "Hey, I'm going to just build the thing. I'm going going to just build the thing. I'm going going to just build the thing. I'm going to make it really really freaking good, to make it really really freaking good, to make it really really freaking good, right?" Like that's kind of like the right?" Like that's kind of like the right?" Like that's kind of like the bias you have as like an engineer. bias you have as like an engineer. bias you have as like an engineer. That's the bias we had when we started That's the bias we had when we started That's the bias we had when we started X.ai and we were like honestly pretty X.ai and we were like honestly pretty X.ai and we were like honestly pretty bad at go-to-market. Like we bad at go-to-market. Like we bad at go-to-market. Like we >> [laughter] >> [laughter] >> [laughter] >> we were not doing enough marketing, we >> we were not doing enough marketing, we >> we were not doing enough marketing, we were not doing enough sales.

were not doing enough sales. were not doing enough sales. Um but I think the cool thing about Um but I think the cool thing about Um but I think the cool thing about about um go-to-market particularly in about um go-to-market particularly in about um go-to-market particularly in 2026 is you can treat go-to-market like 2026 is you can treat go-to-market like 2026 is you can treat go-to-market like an engineering problem and particularly an engineering problem and particularly an engineering problem and particularly an AI engineering problem. And so I an AI engineering problem. And so I an AI engineering problem. And so I think that's like a super exciting think that's like a super exciting think that's like a super exciting thing. Like it's like more fun for thing. Like it's like more fun for thing. Like it's like more fun for engineers than ever to do go-to-market engineers than ever to do go-to-market engineers than ever to do go-to-market cuz you can automate things. You can you cuz you can automate things. You can you cuz you can automate things. You can you can do so much as one person and uh can do so much as one person and uh can do so much as one person and uh etc. Also um I want to make this etc. Also um I want to make this etc. Also um I want to make this interactive. If if anybody has questions interactive. If if anybody has questions interactive. If if anybody has questions at any point, please please ask cuz I'm at any point, please please ask cuz I'm at any point, please please ask cuz I'm aware there's a lot of talks and aware there's a lot of talks and aware there's a lot of talks and I don't want to bore you. I don't want to bore you. I don't want to bore you. Cool. So um cool. So so the hypothesis I Cool. So um cool. So so the hypothesis I Cool. So um cool. So so the hypothesis I have is if you're an engineer or or if have is if you're an engineer or or if have is if you're an engineer or or if you're anyone, you can treat you're anyone, you can treat you're anyone, you can treat go-to-market like an engineering go-to-market like an engineering go-to-market like an engineering problem. So first, I guess like what problem. So first, I guess like what problem. So first, I guess like what does what do go-to-market teams do? So I does what do go-to-market teams do? So I does what do go-to-market teams do? So I have like a laundry list of things here have like a laundry list of things here have like a laundry list of things here of things that go-to-market teams do, of things that go-to-market teams do, of things that go-to-market teams do, but here are a few. Like one is you got but here are a few. Like one is you got but here are a few. Like one is you got to research to research to research like your customer, right? You got to like your customer, right? You got to like your customer, right? You got to research your targets. You have to find research your targets. You have to find research your targets. You have to find out information about your about out information about your about out information about your about targets. You have to find the right targets. You have to find the right targets. You have to find the right people at particular companies. You have people at particular companies. You have people at particular companies. You have to build POCs. Um there's like a just a to build POCs. Um there's like a just a to build POCs. Um there's like a just a ton of stuff you have to do, right? Um ton of stuff you have to do, right? Um ton of stuff you have to do, right? Um so you know, I'm not going to I'm not so you know, I'm not going to I'm not so you know, I'm not going to I'm not going to list everything here, but like going to list everything here, but like going to list everything here, but like what is the grand unifying theme? Well, what is the grand unifying theme? Well, what is the grand unifying theme? Well, go-to-market is a data problem, right?

go-to-market is a data problem, right? go-to-market is a data problem, right? So you have all you have this like So you have all you have this like So you have all you have this like entire world of uh of of what your entire world of uh of of what your entire world of uh of of what your product does and then and this entire product does and then and this entire product does and then and this entire world of like all your potential world of like all your potential world of like all your potential customers and you're just going to like customers and you're just going to like customers and you're just going to like learn and figure out what your world learn and figure out what your world learn and figure out what your world looks like. And so this is my this is my looks like. And so this is my this is my looks like. And so this is my this is my uh proposal. It's a data problem and we uh proposal. It's a data problem and we uh proposal. It's a data problem and we have to solve from a data perspective. have to solve from a data perspective. have to solve from a data perspective. Cool. So okay, so what is data that is Cool. So okay, so what is data that is Cool. So okay, so what is data that is relevant? Uh I propose that you need relevant? Uh I propose that you need relevant? Uh I propose that you need basically a live model of your world basically a live model of your world basically a live model of your world that agents can act on. And so, what that agents can act on. And so, what that agents can act on. And so, what does that mean? Okay, well, one is you does that mean? Okay, well, one is you does that mean? Okay, well, one is you have a ton of internal data, right? have a ton of internal data, right? have a ton of internal data, right? There's all this information that you There's all this information that you There's all this information that you know about your customers, about people know about your customers, about people know about your customers, about people that are at your company, that are at your company, that are at your company, uh uh uh data about how people use the product. data about how people use the product. data about how people use the product. That's like internal data that you know. That's like internal data that you know. That's like internal data that you know. And then there's all sorts of external And then there's all sorts of external And then there's all sorts of external data, right? Like there's over 60 data, right? Like there's over 60 data, right? Like there's over 60 million companies in the world, and million companies in the world, and million companies in the world, and there's like billions of people, like there's like billions of people, like there's like billions of people, like over a billion that are on LinkedIn, for over a billion that are on LinkedIn, for over a billion that are on LinkedIn, for example, and all sorts of stuff are is example, and all sorts of stuff are is example, and all sorts of stuff are is is like happening every day, right? Like is like happening every day, right? Like is like happening every day, right? Like there's all this news. And so, when there's all this news. And so, when there's all this news. And so, when you're building like this data you're building like this data you're building like this data go-to-market system, um it's important go-to-market system, um it's important go-to-market system, um it's important to keep in mind just like all the to keep in mind just like all the to keep in mind just like all the different sources that exist and and and different sources that exist and and and different sources that exist and and and uh uh uh and are available to your agents.

and are available to your agents. and are available to your agents. And cool. So, I'm going to like go And cool. So, I'm going to like go And cool. So, I'm going to like go through, hopefully pretty fast, just all through, hopefully pretty fast, just all through, hopefully pretty fast, just all the different components of what we've the different components of what we've the different components of what we've built at Exa. And just for like context, built at Exa. And just for like context, built at Exa. And just for like context, uh I've been really passionate about uh I've been really passionate about uh I've been really passionate about this for a long time. So, like Exa was this for a long time. So, like Exa was this for a long time. So, like Exa was launched in launched in launched in uh the middle of 2023, and so we were uh the middle of 2023, and so we were uh the middle of 2023, and so we were post-GPT-4. And post-GPT-4. And post-GPT-4. And GPT-4 was really incredible, cuz it GPT-4 was really incredible, cuz it GPT-4 was really incredible, cuz it could actually, even then, even though could actually, even then, even though could actually, even then, even though it's way worse than like Fable or it's way worse than like Fable or it's way worse than like Fable or whatever, like it could actually just whatever, like it could actually just whatever, like it could actually just automate entire automate entire automate entire parts of go-to-market. And so, from the parts of go-to-market. And so, from the parts of go-to-market. And so, from the beginning, I've been thinking about our beginning, I've been thinking about our beginning, I've been thinking about our go-to-market from go-to-market from go-to-market from from a very, very uh AI agent-first from a very, very uh AI agent-first from a very, very uh AI agent-first perspective. And so, we're going to go perspective. And so, we're going to go perspective. And so, we're going to go over two interfaces that we have that over two interfaces that we have that over two interfaces that we have that help us, and then two agents. help us, and then two agents. help us, and then two agents. Cool. Cool. Okay, the first is what we call Cool. Okay, the first is what we call our ICP dashboard. And the ICP dashboard our ICP dashboard. And the ICP dashboard our ICP dashboard. And the ICP dashboard is a product that we have internally is a product that we have internally is a product that we have internally that answers the question, like what is that answers the question, like what is that answers the question, like what is our world? Like what is the world of our world? Like what is the world of our world? Like what is the world of customers and use cases that we care customers and use cases that we care customers and use cases that we care about? And what we actually do is we go about? And what we actually do is we go about? And what we actually do is we go ahead and use Exa, and again, Exa is ahead and use Exa, and again, Exa is ahead and use Exa, and again, Exa is this like uh this like uh this like uh arbitrarily powerful search engine for arbitrarily powerful search engine for arbitrarily powerful search engine for AIs essentially. And we just classify AIs essentially. And we just classify AIs essentially. And we just classify basically like every possible company basically like every possible company basically like every possible company that is inside of our total addressable that is inside of our total addressable that is inside of our total addressable market. And I kind of blurred out some market. And I kind of blurred out some market. And I kind of blurred out some of the details on like how much money we of the details on like how much money we of the details on like how much money we make from each category and stuff like make from each category and stuff like make from each category and stuff like that. But yeah, we have like categories that. But yeah, we have like categories that. But yeah, we have like categories like model providers, AI coding like model providers, AI coding like model providers, AI coding platforms like say Cursor, go-to-market platforms like say Cursor, go-to-market platforms like say Cursor, go-to-market intelligence tools.

intelligence tools. intelligence tools. And this makes up our TAM and we have an And this makes up our TAM and we have an And this makes up our TAM and we have an understanding of literally like understanding of literally like understanding of literally like almost every company within those almost every company within those almost every company within those segments. segments. segments. And then for each of those companies we And then for each of those companies we And then for each of those companies we can deep dive, right? So here's the can deep dive, right? So here's the can deep dive, right? So here's the example of SpaceX. We can see how much example of SpaceX. We can see how much example of SpaceX. We can see how much annual spend we could anticipate them to annual spend we could anticipate them to annual spend we could anticipate them to have then all this like metadata about have then all this like metadata about have then all this like metadata about the company. So we have a list of all the company. So we have a list of all the company. So we have a list of all the companies and then a ton of data the companies and then a ton of data the companies and then a ton of data about each company. about each company. about each company. How do we do this? Again, we're able to How do we do this? Again, we're able to How do we do this? Again, we're able to do this because Exa is this search do this because Exa is this search do this because Exa is this search engine. We take the internet, we crawl engine. We take the internet, we crawl engine. We take the internet, we crawl it, we train we train embeddings to do it, we train we train embeddings to do it, we train we train embeddings to do web search really well. And so basically web search really well. And so basically web search really well. And so basically from a technical perspective you can from a technical perspective you can from a technical perspective you can think about Exa as like embeddings over think about Exa as like embeddings over think about Exa as like embeddings over the internet. And when you have the internet. And when you have the internet. And when you have embeddings over the internet you have embeddings over the internet you have embeddings over the internet you have this like arbitrarily powerful semantic this like arbitrarily powerful semantic this like arbitrarily powerful semantic filtering and slicing and dicing of any filtering and slicing and dicing of any filtering and slicing and dicing of any type of data that you want. And so we type of data that you want. And so we type of data that you want. And so we use that to generate this this like use that to generate this this like use that to generate this this like gigantic list of potential ICPs. Cool. Next, we have a tool we call Cool. Next, we have a tool we call Request Lens. Request Lens, what is Request Lens. Request Lens, what is Request Lens. Request Lens, what is Request Lens? Well, it's basically a Request Lens? Well, it's basically a Request Lens? Well, it's basically a system where anytime something system where anytime something system where anytime something significant happens with any of our significant happens with any of our significant happens with any of our customers, we're alerted.

customers, we're alerted. customers, we're alerted. Someone signed up, someone used a ton of Someone signed up, someone used a ton of Someone signed up, someone used a ton of searches, someone stopped using searches, someone stopped using searches, someone stopped using searches, someone showed up that we searches, someone showed up that we searches, someone showed up that we really really care about. All these really really care about. All these really really care about. All these things are signals that we are notified things are signals that we are notified things are signals that we are notified about and that our team can act on. Cool. Cool. So those are the two interfaces that we So those are the two interfaces that we So those are the two interfaces that we have and then I'll go over two types of have and then I'll go over two types of have and then I'll go over two types of agents that we have. So agents that we have. So agents that we have. So one is coding agents. So our one is coding agents. So our one is coding agents. So our go-to-market team go-to-market team go-to-market team is crazy crazy crazy deep on agents. So is crazy crazy crazy deep on agents. So is crazy crazy crazy deep on agents. So like like our our our engineering team like like our our our engineering team like like our our our engineering team uses a lot of agents, but our uses a lot of agents, but our uses a lot of agents, but our go-to-market team is like like you could go-to-market team is like like you could go-to-market team is like like you could look you could look at some of their look you could look at some of their look you could look at some of their like devin spend and like other agent like devin spend and like other agent like devin spend and like other agent spend. It's really freaking high. And spend. It's really freaking high. And spend. It's really freaking high. And that's because everybody on our that's because everybody on our that's because everybody on our go-to-market team is constantly asking go-to-market team is constantly asking go-to-market team is constantly asking agents about our customers. agents about our customers. agents about our customers. Uh we have like Uh we have like Uh we have like like account executives that build demos like account executives that build demos like account executives that build demos for our customers. Like it's just this for our customers. Like it's just this for our customers. Like it's just this crazy ecosystem where we have like maybe crazy ecosystem where we have like maybe crazy ecosystem where we have like maybe a dozen different agents inside of our a dozen different agents inside of our a dozen different agents inside of our Slack and anybody can use any of them. Slack and anybody can use any of them. Slack and anybody can use any of them. They all have access to tons and tons of They all have access to tons and tons of They all have access to tons and tons of our internal data. And uh yeah. Anytime our internal data. And uh yeah. Anytime our internal data. And uh yeah. Anytime we want to dig deeper on account, we want to dig deeper on account, we want to dig deeper on account, anytime we want to make a demo, etc., we anytime we want to make a demo, etc., we anytime we want to make a demo, etc., we depend heavily on agents.

Cool. And then I want to talk about Cool. And then I want to talk about another really cool agent that I'm another really cool agent that I'm another really cool agent that I'm pretty proud of. We call it Jeff Bots. pretty proud of. We call it Jeff Bots. pretty proud of. We call it Jeff Bots. Uh or I call it Jeff Bots. Uh Jeff Bots Uh or I call it Jeff Bots. Uh Jeff Bots Uh or I call it Jeff Bots. Uh Jeff Bots is an AI clone of myself. Uh as much as is an AI clone of myself. Uh as much as is an AI clone of myself. Uh as much as possible. So, what is it? Well, possible. So, what is it? Well, possible. So, what is it? Well, basically basically basically this winter break uh this winter break uh this winter break uh I'm sure a lot of you spent that break I'm sure a lot of you spent that break I'm sure a lot of you spent that break playing with Opus 4.5. And I was no playing with Opus 4.5. And I was no playing with Opus 4.5. And I was no different. So, I was in Puerto No, I was different. So, I was in Puerto No, I was different. So, I was in Puerto No, I was in Mexico. I was in Mexico and I would I in Mexico. I was in Mexico and I would I in Mexico. I was in Mexico and I would I had a week off. And so, my goal with had a week off. And so, my goal with had a week off. And so, my goal with that week and with Opus 4.5 was to uh that week and with Opus 4.5 was to uh that week and with Opus 4.5 was to uh just try to make a digital clone of just try to make a digital clone of just try to make a digital clone of myself. And so, I did things like myself. And so, I did things like myself. And so, I did things like analyze analyze analyze like 760 of my emails to figure out what like 760 of my emails to figure out what like 760 of my emails to figure out what my email voice is. Like, oh, I use 18 my email voice is. Like, oh, I use 18 my email voice is. Like, oh, I use 18 words on average per email and I like to words on average per email and I like to words on average per email and I like to end emails with best and not sincerely. end emails with best and not sincerely. end emails with best and not sincerely. Like all all that type of stuff, right? Like all all that type of stuff, right? Like all all that type of stuff, right? So, I made like a like a voice for So, I made like a like a voice for So, I made like a like a voice for myself. myself. myself. And then I also made a decision-making And then I also made a decision-making And then I also made a decision-making framework. So, I made like a framework. So, I made like a framework. So, I made like a decision-making framework where I decision-making framework where I decision-making framework where I analyzed hundreds of decisions I've made analyzed hundreds of decisions I've made analyzed hundreds of decisions I've made in the past. in the past. in the past. And I I analyzed them and I created And I I analyzed them and I created And I I analyzed them and I created evals. So, I actually created evals from evals. So, I actually created evals from evals. So, I actually created evals from those decisions and calibrated this those decisions and calibrated this those decisions and calibrated this agent system to behave like myself.

agent system to behave like myself. agent system to behave like myself. And then finally I gave it like read and And then finally I gave it like read and And then finally I gave it like read and write access to all the data that I write access to all the data that I write access to all the data that I personally have. And there's a cool personally have. And there's a cool personally have. And there's a cool advantage to this because like I advantage to this because like I advantage to this because like I basically have access to every single basically have access to every single basically have access to every single system at the company system at the company system at the company uh cuz I'm in the the the nice seat uh cuz I'm in the the the nice seat uh cuz I'm in the the the nice seat of of having that and so like um yeah, of of having that and so like um yeah, of of having that and so like um yeah, this thing has access to like this thing has access to like this thing has access to like everything. And basically what happens everything. And basically what happens everything. And basically what happens is anybody at the company can use is anybody at the company can use is anybody at the company can use Jeffbot to create drafts of Slack Jeffbot to create drafts of Slack Jeffbot to create drafts of Slack messages that are basically like answers messages that are basically like answers messages that are basically like answers or decisions that are made. And this is or decisions that are made. And this is or decisions that are made. And this is a huge great thing. Like our a huge great thing. Like our a huge great thing. Like our go-to-market team uses it to like draft go-to-market team uses it to like draft go-to-market team uses it to like draft emails, for example. Cool. Um Cool. Um all right, so those those are the all right, so those those are the all right, so those those are the systems that uh systems that uh systems that uh that we have at at Exa. It works pretty that we have at at Exa. It works pretty that we have at at Exa. It works pretty well. Our go-to-market team is very well. Our go-to-market team is very well. Our go-to-market team is very lean, but very productive. lean, but very productive. lean, but very productive. Um and so yeah, I just want to cover Um and so yeah, I just want to cover Um and so yeah, I just want to cover like like like lastly just a few principles lastly just a few principles lastly just a few principles um um um principles I have principles I have principles I have around what it means to be an around what it means to be an around what it means to be an agent-first company. agent-first company. agent-first company. So firstly, to be agent-first you must So firstly, to be agent-first you must So firstly, to be agent-first you must be API-first, right? So like all these be API-first, right? So like all these be API-first, right? So like all these systems that we built, whether they were systems that we built, whether they were systems that we built, whether they were those whether it was those agents or those whether it was those agents or those whether it was those agents or whether it was those GUIs that we have, whether it was those GUIs that we have, whether it was those GUIs that we have, like if there did not exist really good like if there did not exist really good like if there did not exist really good APIs on top of any internal and external APIs on top of any internal and external APIs on top of any internal and external data data data we'd be we'd be out of luck, right? Like we'd be we'd be out of luck, right? Like we'd be we'd be out of luck, right? Like you need to create really good APIs. If you need to create really good APIs. If you need to create really good APIs. If you don't have really good APIs your you don't have really good APIs your you don't have really good APIs your agents are not going to be able to have agents are not going to be able to have agents are not going to be able to have data access. So you can think about this data access. So you can think about this data access. So you can think about this as MCP, CLI, whatever, right? Like it as MCP, CLI, whatever, right? Like it as MCP, CLI, whatever, right? Like it doesn't really matter. Uh you just need doesn't really matter. Uh you just need doesn't really matter. Uh you just need some interface that's programmatic.

Secondly is like I I think there's like Secondly is like I I think there's like still this mistake in still this mistake in still this mistake in the agent world which is made that's the agent world which is made that's the agent world which is made that's like hey, does everything like hey, does everything like hey, does everything need to be a chatbot? need to be a chatbot? need to be a chatbot? Uh Uh Uh I think the answer is no. Like I think I think the answer is no. Like I think I think the answer is no. Like I think I think both GUIs and chatbots are are I think both GUIs and chatbots are are I think both GUIs and chatbots are are both super useful and have their own both super useful and have their own both super useful and have their own benefits. Like uh I don't know how many benefits. Like uh I don't know how many benefits. Like uh I don't know how many people in this room have thought about people in this room have thought about people in this room have thought about dynamic user interfaces dynamic user interfaces dynamic user interfaces but like yes, dynamic user interfaces but like yes, dynamic user interfaces but like yes, dynamic user interfaces are amazing. Like yes, technically AI are amazing. Like yes, technically AI are amazing. Like yes, technically AI can just produce a new UI for any use can just produce a new UI for any use can just produce a new UI for any use case that you have. Like case that you have. Like case that you have. Like just to answer a question, it could just to answer a question, it could just to answer a question, it could produce like an HTML markdown file, produce like an HTML markdown file, produce like an HTML markdown file, right? But I think there is something right? But I think there is something right? But I think there is something really nice about being able to visit really nice about being able to visit really nice about being able to visit the same consistent UX for the same use the same consistent UX for the same use the same consistent UX for the same use cases over time so that you can like cases over time so that you can like cases over time so that you can like learn how to use some tool. Um so yeah, learn how to use some tool. Um so yeah, learn how to use some tool. Um so yeah, I think like having crystallized UIs and I think like having crystallized UIs and I think like having crystallized UIs and then also arbitrarily powerful flexible then also arbitrarily powerful flexible then also arbitrarily powerful flexible chat agents are both important chat agents are both important chat agents are both important components of being agent first. components of being agent first. components of being agent first. And then finally, And then finally, And then finally, uh uh uh you know, there's this question like, you know, there's this question like, you know, there's this question like, "Hey, should you like shop for like "Hey, should you like shop for like "Hey, should you like shop for like Salesforce Salesforce Salesforce or should you like build your own CRM or or should you like build your own CRM or or should you like build your own CRM or something, right?" I actually think this something, right?" I actually think this something, right?" I actually think this is like a false dichotomy. It's like is like a false dichotomy. It's like is like a false dichotomy. It's like like there it's not a choice. Like we like there it's not a choice. Like we like there it's not a choice. Like we don't live in a world where the choice don't live in a world where the choice don't live in a world where the choice is between purchasing SaaS and building is between purchasing SaaS and building is between purchasing SaaS and building things yourself. Like the way I like to things yourself. Like the way I like to things yourself. Like the way I like to think about it is like think about it is like think about it is like you should just be using something that you should just be using something that you should just be using something that is arbitrarily customizable, right? Like is arbitrarily customizable, right? Like is arbitrarily customizable, right? Like whether you like obviously if you build whether you like obviously if you build whether you like obviously if you build something yourself, then it's something yourself, then it's something yourself, then it's arbitrarily customizable cuz you can arbitrarily customizable cuz you can arbitrarily customizable cuz you can write code and make it better at any write code and make it better at any write code and make it better at any given point.

given point. given point. But also if you procure SaaS, um if you But also if you procure SaaS, um if you But also if you procure SaaS, um if you can make that SaaS work on your behalf can make that SaaS work on your behalf can make that SaaS work on your behalf and and and be arbitrarily customizable, then that be arbitrarily customizable, then that be arbitrarily customizable, then that works too, right? Like you don't need to works too, right? Like you don't need to works too, right? Like you don't need to build this like build this like build this like GUI and like have a proactive roadmap as GUI and like have a proactive roadmap as GUI and like have a proactive roadmap as to like what features would make really to like what features would make really to like what features would make really great sense inside of some system. Like great sense inside of some system. Like great sense inside of some system. Like if you can arbitrarily customize the if you can arbitrarily customize the if you can arbitrarily customize the system, even if it's a system you've system, even if it's a system you've system, even if it's a system you've purchased, then you're like pretty good, purchased, then you're like pretty good, purchased, then you're like pretty good, right? So like for example, we use right? So like for example, we use right? So like for example, we use Salesforce. Like we use Salesforce at Salesforce. Like we use Salesforce at Salesforce. Like we use Salesforce at X.ai and uh it's great because uh it's a X.ai and uh it's great because uh it's a X.ai and uh it's great because uh it's a really good good database. It's made a really good good database. It's made a really good good database. It's made a lot of amazing choices around what sales lot of amazing choices around what sales lot of amazing choices around what sales should look like, choices that we don't should look like, choices that we don't should look like, choices that we don't want to make ourselves. And then it want to make ourselves. And then it want to make ourselves. And then it exposes MCP. So all of our agents have exposes MCP. So all of our agents have exposes MCP. So all of our agents have access to Salesforce MCP. Works really access to Salesforce MCP. Works really access to Salesforce MCP. Works really well. Our team uses it every day. well. Our team uses it every day. well. Our team uses it every day. And so yeah, I think infinite And so yeah, I think infinite And so yeah, I think infinite customizability um is is really the customizability um is is really the customizability um is is really the highest order bit. Cool. Um Cool. Um that's that's all I had. that's that's all I had. that's that's all I had. Uh Uh Uh Yeah, does anyone have any questions? Yeah, does anyone have any questions? Yeah, does anyone have any questions? >> Oh, we have time for a few questions. >> Oh, we have time for a few questions. >> Oh, we have time for a few questions. Okay, coming.

>> Hey, um so you said you uh took all your >> Hey, um so you said you uh took all your past decisions. Can you elaborate a bit past decisions. Can you elaborate a bit past decisions. Can you elaborate a bit about that? about that? about that? What What artifacts are those? What What artifacts are those? What What artifacts are those? Usually people don't save like their Usually people don't save like their Usually people don't save like their decisions. Is it Slack? Is it email? Is decisions. Is it Slack? Is it email? Is decisions. Is it Slack? Is it email? Is it other other artifacts? it other other artifacts? it other other artifacts? >> Yeah, good question. I looked at >> Yeah, good question. I looked at >> Yeah, good question. I looked at decisions I made within Slack and email. decisions I made within Slack and email. decisions I made within Slack and email. I mean, a surprisingly large amount of I mean, a surprisingly large amount of I mean, a surprisingly large amount of everything that goes on a company is everything that goes on a company is everything that goes on a company is is is on Slack, right? So like if you is is on Slack, right? So like if you is is on Slack, right? So like if you just read like a ton of Slack history, just read like a ton of Slack history, just read like a ton of Slack history, like you can definitely find hundreds of like you can definitely find hundreds of like you can definitely find hundreds of decisions that you made in the past. decisions that you made in the past. decisions that you made in the past. >> Awesome. Quick question uh over here. Uh >> Awesome. Quick question uh over here. Uh >> Awesome. Quick question uh over here. Uh so your go-to-market team, what's the so your go-to-market team, what's the so your go-to-market team, what's the split between uh are they just all like split between uh are they just all like split between uh are they just all like AI cracked or do they also have like the AI cracked or do they also have like the AI cracked or do they also have like the domain expertise, too? What's the split domain expertise, too? What's the split domain expertise, too? What's the split between between between technical and non-technical? Because technical and non-technical? Because technical and non-technical? Because obviously you need them to like know how obviously you need them to like know how obviously you need them to like know how to do marketing, sales, etc. But then do to do marketing, sales, etc. But then do to do marketing, sales, etc. But then do they also are they also upskilling in they also are they also upskilling in they also are they also upskilling in terms of using AI systems? Are you terms of using AI systems? Are you terms of using AI systems? Are you handing them tools or they building handing them tools or they building handing them tools or they building their own? their own? their own? >> That's a very good question. So our >> That's a very good question. So our >> That's a very good question. So our go-to-market team go-to-market team go-to-market team is comprised of like there's there's is comprised of like there's there's is comprised of like there's there's account executives which like run the account executives which like run the account executives which like run the deals.

deals. deals. There are like sales like SDRs that help There are like sales like SDRs that help There are like sales like SDRs that help with uh demand generation. with uh demand generation. with uh demand generation. And then there are separately they're And then there are separately they're And then there are separately they're separate like a FDE org. So forward separate like a FDE org. So forward separate like a FDE org. So forward deployed engineering organization. deployed engineering organization. deployed engineering organization. And And And what I'll say is that like what I'll say is that like what I'll say is that like everyone that's Okay, everyone that's uh everyone that's Okay, everyone that's uh everyone that's Okay, everyone that's uh not not in FDE not not in FDE not not in FDE is like is like is like has learned how to use AI really well. has learned how to use AI really well. has learned how to use AI really well. So like So like So like the answer is like they're not vibe the answer is like they're not vibe the answer is like they're not vibe coding. They're not generally with you coding. They're not generally with you coding. They're not generally with you know, in some there's some exceptions. know, in some there's some exceptions. know, in some there's some exceptions. They're not generally vibe coding these They're not generally vibe coding these They're not generally vibe coding these interfaces that we have. Um but they're interfaces that we have. Um but they're interfaces that we have. Um but they're using the tools really really well. And using the tools really really well. And using the tools really really well. And like we make sure that we have training like we make sure that we have training like we make sure that we have training sessions and like just make sure that sessions and like just make sure that sessions and like just make sure that people people people really understand how to use these really understand how to use these really understand how to use these tools. And then this funny we have this tools. And then this funny we have this tools. And then this funny we have this funny thing which is like our four funny thing which is like our four funny thing which is like our four deployed engineering organization is deployed engineering organization is deployed engineering organization is actually the one that like does a lot of actually the one that like does a lot of actually the one that like does a lot of the maintenance and feature building the maintenance and feature building the maintenance and feature building uh of these AI systems. And so they're uh of these AI systems. And so they're uh of these AI systems. And so they're both running deals and like like both running deals and like like both running deals and like like supporting deals supporting deals supporting deals um but then also making um but then also making um but then also making everything smoother by like everything smoother by like everything smoother by like doing sales but then also building the doing sales but then also building the doing sales but then also building the sales system. Like it's it's kind of sales system. Like it's it's kind of sales system. Like it's it's kind of it's kind of a funky thing we have going it's kind of a funky thing we have going it's kind of a funky thing we have going on. Yeah.

>> How do you think about uh >> How do you think about uh different security? different security? different security? >> Oh. >> Oh. >> Oh. >> Hey. How do you think about different uh >> Hey. How do you think about different uh >> Hey. How do you think about different uh security boundaries within your security boundaries within your security boundaries within your enterprise? What do you What you said enterprise? What do you What you said enterprise? What do you What you said suggested that you've got Jeffbot which suggested that you've got Jeffbot which suggested that you've got Jeffbot which had runs with all of your full had runs with all of your full had runs with all of your full privileges and then it's available to privileges and then it's available to privileges and then it's available to everybody which suggests that there's everybody which suggests that there's everybody which suggests that there's one security level and everyone can see one security level and everyone can see one security level and everyone can see everything all the time. Is that what everything all the time. Is that what everything all the time. Is that what you're going with or you're going with or you're going with or is there some uh other guardrails in is there some uh other guardrails in is there some uh other guardrails in place? place? place? >> Yeah, that's a good question. We we pay >> Yeah, that's a good question. We we pay >> Yeah, that's a good question. We we pay pretty special we we pay pretty careful pretty special we we pay pretty careful pretty special we we pay pretty careful attention to guardrails. So for example attention to guardrails. So for example attention to guardrails. So for example um in the case of Jeffbot um in the case of Jeffbot um in the case of Jeffbot um um um when I use Jeffbot and I call Jeffbot when I use Jeffbot and I call Jeffbot when I use Jeffbot and I call Jeffbot has access to has access to has access to a ton of systems and it can for example a ton of systems and it can for example a ton of systems and it can for example do reads and writes. However, when do reads and writes. However, when do reads and writes. However, when anybody else calls Jeffbot all can do is anybody else calls Jeffbot all can do is anybody else calls Jeffbot all can do is draft messages and also I don't give draft messages and also I don't give draft messages and also I don't give Jeffbot Jeffbot Jeffbot permissions to permissions to permissions to all of our MCPs and tools in the case all of our MCPs and tools in the case all of our MCPs and tools in the case where other people call it. And so in where other people call it. And so in where other people call it. And so in short it's like pretty short it's like pretty short it's like pretty it's pretty well defined or we we we do it's pretty well defined or we we we do it's pretty well defined or we we we do pay some care to the security. pay some care to the security. pay some care to the security. Yeah. Yeah. Yeah. >> Okay, last question.

>> Um, can you can you share the origin >> Um, can you can you share the origin story of the FDE team? Did that just story of the FDE team? Did that just story of the FDE team? Did that just happen organically or did you happen organically or did you happen organically or did you intentionally do it? I'm just really intentionally do it? I'm just really intentionally do it? I'm just really curious like how that came to exist. curious like how that came to exist. curious like how that came to exist. >> Yeah, for sure. I mean, >> Yeah, for sure. I mean, >> Yeah, for sure. I mean, uh uh uh my my my my philos- my my hypothesis on this is my philos- my my hypothesis on this is my philos- my my hypothesis on this is like, once upon a time the FDE role like, once upon a time the FDE role like, once upon a time the FDE role didn't really exist. Like, Palantir didn't really exist. Like, Palantir didn't really exist. Like, Palantir started calling some people FDEs, but it started calling some people FDEs, but it started calling some people FDEs, but it that was really it. And what tech that was really it. And what tech that was really it. And what tech companies had was like solutions and companies had was like solutions and companies had was like solutions and sales engineers. sales engineers. sales engineers. And then, like account executives. And then, like account executives. And then, like account executives. >> I was a solutions engineer. >> I was a solutions engineer. >> I was a solutions engineer. >> Got it. Yeah, yeah. The thing The thing >> Got it. Yeah, yeah. The thing The thing >> Got it. Yeah, yeah. The thing The thing that I think has changed is that um that I think has changed is that um that I think has changed is that um because of AI, as like because of AI, as like because of AI, as like en- as a technical person that is en- as a technical person that is en- as a technical person that is supporting revenue generation, supporting revenue generation, supporting revenue generation, you can actually not only support the you can actually not only support the you can actually not only support the revenue generation, but then very easily revenue generation, but then very easily revenue generation, but then very easily build the tooling build the tooling build the tooling to smooth everything over. to smooth everything over. to smooth everything over. And make your own life easier, make the And make your own life easier, make the And make your own life easier, make the lives of AEs easier. Like, because of lives of AEs easier. Like, because of lives of AEs easier. Like, because of AI, this is just possible now. Like, AI, this is just possible now. Like, AI, this is just possible now. Like, that's like two that's like two that's like two Before that was like two jobs, and now Before that was like two jobs, and now Before that was like two jobs, and now it's like one job.

it's like one job. it's like one job. Um in theory. Like, now when our team Um in theory. Like, now when our team Um in theory. Like, now when our team grows, like right now it's about eight grows, like right now it's about eight grows, like right now it's about eight or nine FDEs, like what will will it or nine FDEs, like what will will it or nine FDEs, like what will will it scale such that everyone does scale such that everyone does scale such that everyone does everything? Probably not. But, at least everything? Probably not. But, at least everything? Probably not. But, at least right now that's what we have, and I right now that's what we have, and I right now that's what we have, and I think that's a really good working model think that's a really good working model think that's a really good working model to get pretty far. to get pretty far. to get pretty far. >> Eight out of how many? >> Eight out of how many? >> Eight out of how many? >> Uh eight Oh, eight of like how big is >> Uh eight Oh, eight of like how big is >> Uh eight Oh, eight of like how big is our go-to-market org? our go-to-market org? our go-to-market org? >> Or eight You have eight FDEs, and the >> Or eight You have eight FDEs, and the >> Or eight You have eight FDEs, and the size of the company right now is how size of the company right now is how size of the company right now is how many? many? many? >> Oh, the We're about 115 people. >> Oh, the We're about 115 people. >> Oh, the We're about 115 people. >> Okay. >> Okay. >> Okay. >> Yeah.