I Built a $100M Company in 3 Years by Betting on AI Agents | Arvind Jain (Glean)

it's not like AI That's going to replace people I think it's going to be AI enabled people who going to you know replace people who are actually you know AI lards I think we'll be able to do 10 times as much work in the future as we can do today that doesn't mean that we have to work less like I think like the history points more to that you know we figure out new things you know do we're going to all have this amazing theme of Agents around us that are going to actually not only take care of most of our work they understand who we are what we do but they also could actually help us you know like get better take a CEO of a company you know they've got an amazing team of people around them can come right out of the school and you'll have that luy all right welcome everyone my guest today is Arvin the CEO of glean Arvin believes that every employee should have their own team of AI agents to help them get work done and gleen I believe recently passed 100 million AR so super excited to talk to Arvin about the future of work and his top product lessons from building glean welcome Arvin thank you so much for having me so Arvin you had a really long career you've done multiple startups you worked like Google for a long time you know in big Tech we start like a Prov of question right so in big Tech you know you have all these layers of management and then every person has like a very specific role on other team and do you think all this stuff will change fundamentally with AI or AI agents or how will the work environment evolve well AI is certainly going to change how everyone works whether you are in a startup or small company or in a large Enterprise is I think one thing that's also going to be the case for the future is the work that we do today that kind of work I think we'll be able to do 10 times as much work in the future as we can do today it's to say it's the same thing as well like you know if you had to sort of add and subtract numbers like you know with the calculator we can do go one much faster than if we to do it manually the same thing is going to happen with most of our work today like you know all these manual things that you work on AI will be able to actually handle those for us that doesn't mean that you know we don't we have to work less like I think like the history points more to that you know we figure out new things you know to do we figure out how to build more advanced you know Technologies and products but certainly like you know there's a big change coming and I think now goinging back to your question like you know in startup you tend to have broader roles like you like for example you know at my the companies you know that I have started you know then we start like you know I'm actually I play a lot of different roles I play you know I'm the IT person the CH person I'm also a developer I'm also a salesperson I've been a bdr and so there's less specialization in you know in startups you know more in large company and what does AI do to that I don't have a direct answer to that but one thing that I but I do feel is that a lot of like you know lot of the tasks you know across these functions are something that AI will be able to do so in that sense you can imagine a person like you know who has a primary role of like let's say being a developer I he has a lot of mileage through AI I to do a bunch of other things you know which you know you had to have other people to do in the past it's almost like you know everyone will kind of become a manager of yeah agents is that fair to say absolutely

like you know I think it's the like the concept that I'm really excited about and I think I like I'll give you like a real world example you know like take a CEO of a company you know they've got an amazing team of people around them they got their Chief of Staff they got you know maybe one or two you know assistants they got this executive team you know that is capable and then also have a coach like you know is constantly guiding you know you to be a great CEO and all of that help around you like obviously allows you to actually make a much bigger impact on the company than like you know if you're an individual contributor in some ways because you know you have these multiplier forces coming and helping you similarly like go into a nonwork scenario take a tennis player Roger feder like you know he goes and plays you know with a racket and a ball on the court and I do the same but like you know he's he has a team of 50 people around him like you know that's you know who are making sure that you know he's well practiced you know that he's the right coaching to figure out how do I actually play this match properly and here has a world- class performance and I don't right and is you know is that concept like you know we when you come back into AI you know everybody is going to like have a different experience in the future we're going to all have this amazing team of Agents around us that are going to actually not only you know take care of most of our work they understand who we are what we do but they're also going to actually help us you know like you get better like you know they will act as our coaches and so that's the future we're looking at like you know you can know can come right out of the school and you'll have that luxury like you don't have to wait for many years for that to happen this episode is brought to you by merge product leaders cringe when they hear the word integration they're not fun for you to build launch or maintain and they probably aren't what L you to product work in the first place luckily the folks at merge are obsessed with Integrations they built a single API that helps SAS companies launch over 200 100 Integrations in weeks not quarters think of merge like plaid or for B2B SAS companies like ramp dra and electric use merge to access their customers accounting data for Bill reconciliation file storage data for searchable databases and hris data for autoprovisioning Access for their customers employees if you need AI ready data for your SAS product then merge is also the fastest way to get it so if you want to solve your company's integration dilemma once and for all book a meeting and receive a $50 Amazon gift card when you attend that's mer. petery now back to the episode yeah I have a uh Cloud Pro project where I've given it way too much personal information about myself and then I just check in with it every three months you know and it gives me like specific advice on what I should do it's like a very nice per personal coach I

think so you know a lot of people list just are like product managers and you know like product managers they have to do a lot of intern alignment they have to make a bunch of documents artifacts do you think this roow is like well positioned for the future like do you think like H even in your own company right for the PMS like how do you get them to upscale for this AI agent future yeah that's a great question actually for in our own company the product managers are actually very heavy users of AI and you know in glean you can when you think about glean like just for context you know we are you can think of us as a more like Enterprise or more powerful version of chat GPD inside your company like you know it's able to actually help you with all the knowledge of the world it has you know through models and web search but it's also able to actually help you with all the internal knowledge that it's connected to answer questions for you help you with whatever task that you're working on but then we also have this agent platform you know where you can actually go and you know build specific agents for like specific day-to-day processes that you have so that agents like you know so people build agents in glean you can build agents for yourself your department and what I was saying was that when we look at like you know how many agents a product manager is using in our company that's the highest like you know across all the different functions like you know take Engineers product managers sales people support people they they're doing the most and why because I think you know their job is I think is constantly about like you know working with information working with you know data that's being generated by like you know by different people within your organiz ganization you know from your customers product usage and you have to actually make data driven decisions knowledge driven decisions all the time so a simple example I'll give you know how do you decide on what features to prioritize this is a process that in our company you know it has gotten reinvented you know with agents and so before you know in Enterprise software company like you know the way you decide like you know how to prioritize you know some of the new work and features is that you work with some of your most important customers you would look at like you know you would sort of learn from them you know what are the key things they're looking at and then you'll do this research like over data like across like you know 10 or 15 meetings you know that you would do and then build a road map based on that but now what our team does is that you know we you know they have access to all the customer conversations that have ever happened in the company and we have thousands and thousands of them and you know they have built this agent which actually goes and listens to all of those call recordings you know that we have had with customers and the agent is instructed to basically listen for like any requirements any sort of new features that the customer is asking for and you know and then basically collect that after that like you know the agent is instructed to like you know classify those requests into different themes like you know you come up with like maybe six or seven different themes and they put those feature requests in that and then you sort of create this you know big huge you know spreadsheet with all the feedback from all the calls and then you ask the agent to also summarize synthesize and say like you know hey pick the top themes the pick and let's does all of that work for you and you know and this is something that you couldn't do before right you know before you had to work with limited set of information you could only meet a few number of people and now like you know they're able to leverage you know from this Collective knowledge that sitting in the organization which was completely untapped you know before AI but now you can tap into it so like that's how our team is now doing road map planning like you know they feel a lot more confident yeah because then you can spend that time thinking instead of just like trying to gather all information exactly yeah I do something pretty similar like when I wrun a PRD like I may maybe I got too lazy or something but like you know I have this customer community that I talked to and I just ask him some qu questions like hey you know what kind of problem you have what kind of solutions you want and then I have a template to just draft the PRD I don't drive from scratch yeah but yeah it's definitely helped a lot there you know what I think roblox actually has a green but I don't think I've ever used the agent thing before I think I've been missing out so you can actually create these agents that just like automatically every day just like summarize information for you yeah so you can yeah so agents can the way you build agents in glean is like first like you know it's trivial to build an agent like you don't need to be actually a developer or a product manager like you could be a salesperson and you can just describe a piece of work that you do like you know something that you are bothered by like you know

you feel it's too TDS like you know and you just go and in natural language to describe to glean that hey this is the work that I do and can you do it from me from now on like that's basically you know as simple as that and then green will build that agent it's already connected with your Enterprise systems and data so it's actually able to you know provide the right data and access to that agent use the power of llms and then also like you know take actions into those different systems you know to sort of complete the piece of works and now when you look at these agents there are two types of them like you know one of them is like you know something that's you know a person users like you know they like I go to an agent and I ask it to do some work for me and there could be another type of agent you know which just runs in the background autonomously like may maybe based on some triggers right like for example like every time there's a new lead you know that comes you know into our into your Marketing System while do a whole bunch of work and like you know like enhance that lead with some information or whatnot right so you can like yeah you can have agents of both types you know Green okay so the latter one is kind of like a zap Pi for like internal you know company stuff right it's kind of like workflows yeah and yeah I think J is actually like they have very extensive set of actions library and you know and the but an agent is a little bit more than actions Library like it's actually there's a lot of like logic and thinking you know and like so when you think about an agent and how you represent an agent there are some parts of that agent process you know which are about reading information from Enterprise systems some of it is about like you know taking actions into those systems but then a lot of it is about thinking and doing analysis and synthesis and that's where the AI shines and then that's the new thing like you know when you think about Automation in the Enterprise like now you're able to automate things which you know which should would definitely need humans in the past because of the requirement for intelligence makes sense so let's talk a little about like Enterprise adoption of AI so like I recently tweeted that like you know it went viral but like the Tweet was like startups have an

advantage over Tech just because they're allowed to use all the AI tools available and like why do you think some companies have been so slow to adopt this stuff like is it security or just takes a while you know yeah so large Enterprises I think the you know takes time like you know longer time for them to adopt some of the AI Technologies even for us like you know as a company that serves large Enterprises we had to be very careful in terms of what technologies we use inside our company and because ultimately like you know if something bad happens like you know like we are risking like you know our customers in some ways right so I think the there is a natural friction like you know like security is definitely like one of those key points of friction like you know in terms of how do you make sure that your AI is safe when you build these agents that you are actually respecting you know the governance uh requirements the security and the permissions architecture of your Enterprise I don't expose you know information even internally of course not externally but even internally don't expose information to your employees that they should not be you know like reading you know that they should not have access to right a lot of Enterprise data is private in nature so you to sort of solve that so that's definitely one factor and like you know as AI is taking off like you know there are like new types of security attacks for AI like you know you have Pro you know you can be susceptible to prompt injection attacks and so you have to like you know when you build these platforms you need to make sure that like everything is done in a like you know in a button- down manner with the right security controls but that's only one of the challenges like you know there are other challenges like one of them is one of them I would say is inertia and you know you like in a large Enterprise like business processes are more solidified like you have certain way of doing things and humans don't want to change like you know we like you know I you know like let's say that I you know have a busy you know work schedule like you know like I've come in and I have to like finish like 10 different things in the day like I don't have the time to actually think about how I'm going to do this thing differently with AI like I just don't like I'm always just you know it's like you know you're on the treadmill you're just you're going to fall behind if you start to think you know the and so you also have to battle that like and like you know when you're starting a new company you know you are in a reverse situation like you don't get you don't have people you don't have processes and well like you need to actually figure out how to get things done and you know you use the modern tools because they're better than like you know and this you know have the promise of like helping you with you know your business processes without requiring you know people or like you so so I think that's so that's a divide so you know that that's a big factor too like you know which is often underestimated you have to like you know when you think about AI as a large Enterprise like first thing you have to do is you have to educate people like you have to actually like you know AI today feels inaccessible like as a if I'm a HR person and you ask me that hey use an AI Tech or you know build an agent they'll say like what are you talking about I've never built anything like that like you know that seems like technology yeah right and so you have to sort of get to that like you know like train your Workforce you know give them you know some basic fundamental AI tools like let them use chat gbd or let them use glean which is a more you know like more Enterprise you know focused product of like of the same nature and like you know like once they start to get comfortable once they know how to use AI then they will actually like you know both drive that demand for more products and actually adopt and embrace them so those are some of the things you know that we see as roadblocks and then finally of course like people are confused like you know there are as if you are a CIO of a large Enterprise you know you're being bombarded and pitched by like thousands of you know startups and large companies because every company you know whether you're a startup or a company has been around for 50 years you are an AI company today you have ai products and there is a lot like you know to sort of choose from and the it's not you know these decisions are not easy like you know like people don't know like Enterprises you know they have to they also have to be careful they have to think longterm like one of the things that I like hear a lot is they don't know what llm providers are going to win like you know who's going to be winner in the as they don't know what startups are even going to be around in a year or two so in that sort of dynamic industry like you know how do you make big decisions and you know like replace you know like transform your business processes you know with products that may not be around next year right so we these are the practical considerations you know which makes Enterprises go slow yeah and there's also like for example like something as simple as using AI to like take notes during me meetings yeah like you know for like a random employee like me like it sounds like a great idea why don't we have it but like there could be certain meetings that are like very sensitive like maybe like employees getting lay off or something like you don't want all that stuff recorded necessarily so maybe like as someone building products for Enterprises you want to build the right controls right like that like maybe because super prods don't think about and in terms of like getting Enterprise adoption like you know like open and all these other companies are also trying to get Enterprise adoption right so like what

has been glean Secret Sauce is it just like everything you talked about or is there something that yeah well mean there two parts to it like you know one is just the product itself like you know we are focused on the Enterprise and we've been around for a long time you know we you know our product is three times as much older as any other product out there like you know we've been building this for the last six years and part of our product has been like you know like connecting with all of the enterprise system systems data knowledge and it's the largest application of AI today anyway like when you think about AI what are people trying to do number one they trying to use it as a assistant you know that can actually help you with knowledge like they can actually answer questions for you so that's a product that we built over a long time so like you know it's so you know we fit right into the most successful domain for AI right now in the Enterprise but also like the other thing that you know that we believe you know is necessary for adoption in the Enterprise is that you know this is not a technology that's easy to use and you can't just build products and just ship them you know throw them over the fence to your customers and expect them to drive full success with it you Al you know you have to build a collaborative code Market motion and so we do that like you know we have a large Enterprise team you go to market team and you know a lot of you know solution specialist you know AI outcomes you know Engineers that we have you know that you know work closely with you know our customers understand like you know their key business problems you know build a road map with them on AI for the next one year and then execute on it together so a lot of other is also like you know just set Focus again on the Enterprise and thinking of this as a people business not a well like you know I'm only a software and Technology business and I build something and like now you have to figure out how to use it like you know that's not the approach you take yeah because like a chat is just like a very horizontal tool right who knows we're going to put in the chat box like all depends on interesting you made an interesting point there actually by the way like a search or a chat product is easy right like you know in the sense that well it's a blank box go ask any question but actually it turns out that it's actually super hard like lot of people actually have no context or orientation of what AI can actually do for you yeah and so we used to actually you know take pride in giving you very clean interface where there's nothing on the page like besides a box and you know you can just type into that box and we realize that you know people are not using it they're not figuring out like you know what can be done and so we've since then like you know we've taken a lot more proactive approach and you know like you know AI education has become like you know the key theme for us like you know we understand like you know user when they come to our product we know who they are and like now based on their role based on like how much you know they've you know used you know the product and AI we start to now sort of you know try that education for them you know we prompt them we suggest you know things you know for to try out and that has proven to be actually very powerful you know there I think at every company there are these like AI evangelists or people who are power users of the stuff and you know like speaking to them like how can they actually try to drive say they're not a CEO they're just like a employee so how can they try to drive the rest of the company to actually use the stuff maybe you can maybe your strategies to partner with them to get adoption of glean or these other tools right you know how do you yeah we I mean like you know we absolutely need those like every company we have a small fraction of people like you know who are those AI enthusiasts they want to be on the frontier they're the ones making very interesting discoveries one thing that I want to make it make clear I don't think anybody in the world really understands what the current models can do for us all the capabilities that they have because you can only figure it out you know as you know put them to task as you sort of ask them to do things and like you that's where discoveries happen like you know that the model is actually pretty good at doing certain type of you know tasks for you and then for that you need you know those folks like who are the enus Enthusiast and who going to try things out and you know they're going to see you know very bad response you know from Ai and they're going to tweak it work it and ultimately they get to a good place where they have prompted the model in a particular way and it has done something magical for them and when that happens you know that's the time when you want to make sure that you know the product that they're using allows you know makes it really easy for them to share that Discovery with the rest of you know the workforce so you know in gleen we've taken this approach of you know we have a prompt library and agent library and everybody can sort of Tinker you know build agents build prompts when something cool happens you know they get to share it with you know with other people in the company and then we figure out like you know how we take that big Library you know that the enthusiasts are building you know that those uh evangelists are building and make sure that you know in effective way we bringing it to people who are going to benefit from that work yeah I love that like I was using Gemini the other day and they have these gems they can make right with my you know Advanced prompts but they forgot to do the feature let me share my prompts with my co-workers so it's like what's the point like it's like that's like a critical Fe feature to have that's right yeah what about like so you know as you said data is ke right when it comes to AI products like if you have shitty data your AI product is not GNA be good but like the company

like internal data like a lot of times it's kind of bad man like some the documents are out of like you know there's like half complete stuff how do you even know what's good you know like yeah that's a big problem and actually it's not a it's not as if like internal data is worse like you know the data on the Internet is also like you know of the same nature like lot of it is obsolete information and you have to like you know that's the thing that you have to actually understand and work with like we've seen two approaches some Enterprises saying that oh like I'm not ready for AI today and I'm going to actually and first clean my knowledge because you know they tried all these you know poc's with you know different AI products and they didn't work well and many of those products came back and told the customers that well your data is bad and therefore AI is bad right but that's not the right approach like I don't think can never fix your data you can like in the sense that you know data gets produced over a long period of time you know it's naturally going to become out of date you know and some data is going to be of high quality because it's written by a good writer or a subject matter expert some of it is not like you have to actually take all of it like you know the good the bad the fresh you know the out ofd the popular or the non-popular content then you to understand that and figure out like you know what's the right information what's the best information for given any task and for that you have to build a really good search system you have to connect you know your search system with all of this you know data and you to start understanding these signals you to start like figuring out like you know what you know to use in any given scenario for any given task and so we you know when we think about our own technology at glean I would say that a big chunk of it like majority of it is actually spent just trying to understand the Enterprise you know this issue of data quality and make sure that you know we are for like any given task that you're going to do with AI that we can bring the right information to the models and then lend them reason over it to you know produce you know like you know great work yeah so it's like the rag or is it some system that's separate from the L LM right it's like yeah the right our approach yeah so like rag is one way to it's a very specific you know construct in that sense right yes like given any task in the Enterprise you do have to retrieve the right information from within the Enterprise because your models are not trained with that knowledge and then you to make that information available to the models but the overall sequence of how this works is that this is a collaborative process right where you may pick some information then you may ask the model to say that hey this is the task this is what I've picked up so far you want me to actually pick some more data like you can sort of have this you know engagement where they're doing M multiple retrieval passes you know in conjunction with you know the intelligence that the model has to ultimately then like assemble all the information that you need and then work on it and you know produce artifacts that you were trying to produce is it kind of like a deep research or like the Chann of thought stuff like it just it has the information and it ask for more information and like yeah like that so we do that and I think like you also have to you cannot always be doing deep research because most of the times we your users exactly have one second you know to get to like you know what they're trying to get to right and so like you cannot be like sitting there like being over smart and like take you know 20 seconds right you know so you have to like you also understand the context you to understand the use case and sometimes you do a real simple quick path sometimes you don't even it's a question that doesn't even require retrieval because you know it's a general question and the models you know code intelligence can answer that question uh sometimes you don't even use a model because well you know your question was so factual and I can just use my you know reval and like it already knows the answer to it so you have to like you know pick and choose based on like what you're trying to do got it okay so let's talk about like building prod like glean like improving clean itself you know there's always like you know the obvious

stuff like you know you got to talk to customers you gotta think about the problems but like what kind of new skills to people have to build AI products I guess like comfortable of ambiguity or kind of skills have yeah great question so first like I think like the biggest skill like or like you know one of the key skills that you know our team had to learn when the industry suddenly started to move very rapidly was this you know you rely on certain set of Technologies underneath and you're building your systems based on that and if that Foundation is super unstable and it's constantly changing like it's a very hard thing to build you know the house on top of it right and so our team actually felt that like you know stress like you know I would say in 2023 quite a bit you know where we were struggling to figure out well how do you even develop in this new world like nothing is a constant nothing is true like you know what's happening this week you know the other week looks completely different from that so thankfully like you know we're not there like you know I guess in some ways it's a bad thing that Innovation PA may have been little bit like maybe a little bit more mature or slow but I think the you have to learn that like like you to fundamentally change your you know mindset of how you build systems like you know first you to become more iterative right you can't actually like you know take you know two months to design a system and then build it over the next four months you have to actually necessarily go you know in that mvp mode right like you know build something fast and quick you don't know whether you know it's going to last you know like more than a month right and second thing is you know having that mindset of like not getting attached to what you build and constantly reinvent and take you know the new capabilities that you get and like throw what you build like you know you should be ready to throw you know something that you built two months back because well like you know the market caught up and now you can actually get that you know for free in the market like all from the lm's capabilities have increased so those are like you know the main I would say like the key skills like you know which will drive success now is like that agile mindset that you need as a developer as a product manager and change you know quickly and fast and then besides that like you know like of course AI as a technology is fuzzy like it's actually non deterministic and you have to learn with like how to work with you know systems you know which are not precise right you to figure out and this is like I would say that search Engineers always had that training like when we building search at Google you know many years back you always have to deal with imperfect knowledge data information like there's no real formula for like given a question what information should be shown on the top right was by Nature a exercise of judgment right and so I think like you know that skill translates well like I feel like you know folks who actually you know worked on search are going to be generally successful with AI as well oh this is like it's like more like probability based as opposed to you know this is gonna happen for sure right yeah yeah interesting with now you know now you're on your second company right that you started is it second or third second yeah do you have many what are your values for like what kind of principles or values do you have for the company you know I think that's like really important yeah so I think like first I would say that like a company is not much more than the people you know the idea the code base like those are all I think not the minor part of what makes a company is the people and you have to like when you build a startup like you always focus on like you know what's that core like you know who are the people who really care about building this product or this technology or solving this business problem that you know we are solving and you have to have that you know because you know this is going to be a long journey there going to be lots and ups and down there's going to be competition you know economic like you know turns like you know that going to happen so you have to have that like you know like I feel like you know a big part of like you know building a company you know culture is about having that alignment you know with your mission but in terms of our values like our values are probably the same as what any company would aspire to like you know we want you know we are you know we customer obsessed like you know we want to make sure that you know we're making them successful because that's what makes us successful you know we like to move fast we want to have buyers to action we want to create a culture of like teamwork and I think like for me that's the thing that I really care about like you know all our cultural values are obvious like you know you you'll say yeah of course yeah I've heard about it like why not like you know the you know you want people to work hard you want dedication you want alignment to the mission all of that is true the one thing that I feel is very important which I feel like maybe I put a little bit more weight is on the value of you know trust and respect I think you build a great company when you like you know have that positive mindset and you respect you know your colleagues you know for one good thing that they do right and like you know and then and you accept the fact that they have to learn the other things and I think that's a big part of in my opinion you know building a company with the right culture and sustain it you know over the long period of time you know you need that mutual respect and trust you know in your Workforce yeah because you people you know you can go far together you can go fast Al but go far together yeah and nobody people have to realize this like sometimes you know if you're really good like you know maybe you know you're good at like you know engineering and like maybe you are a superstar and you're good at like 10 things and then maybe another person who's only good at two things and you may not respect them then anymore and that creates a bad environment but like the reality is that well you know even with you know somebody being you know good at like two two things you know they can actually add a lot of value and over time you know they will add the third one and the fourth one and so I think that mindset is important like you know it's very hard like you know you people don't realize it I think in my opinion like you know this something that comes over time you know this like this concept of well like you know I respect you about what you are yeah and it also makes work more fun right because you spend like you know eight 10 hours a day with people around yeah fun yeah okay cool well I mean I want to give ask just one more random question so like I've talked to AI Founders who you know like you said because the models are changing all the time the ground is Shifting behind their feet like they don't really have a road map anymore maybe they have like a quarterly plan of what they want to do but they don't have like a fiveyear plan and like but like since you work in the Enterprise space maybe you have like how

do you balance this iter thing with actually having a plan yeah so in my previous company you know we achieved success like you know it's a large publicly company now and did have a fiveyear plan but that Five-Year Plan was actually completely useless in the sense that neither you know did we hold through to that but it never actually had an impact when we actually doing our work and so you know so we learned there itself this is pre AI that like you know plan for a startup plan a year and don't think beyond that maybe at the most you may you know like have like a rough idea of what the next year could be like very rough idea but mostly like Focus your energy on what's what you're going to do this year so that's say like whether AI pre AI like you doesn't matter like that's the as a startup like you know that's the right time Horizon you know here we do that like you know so we do sort of come up with a point of view on like what we should do this year so we do an annual planning but we're not vered to it because we know that things are going to constantly change and so when you the way we execute is we actually create monthly plans and the monthly plans you know take inspiration from the annual plan but it allows us to actually be agile and like you know we can sort of adjust the annual plan like along the way as things change but like all for all practical purposes what we're doing you know we are operating at a monthly City but you do have a in ter of long term vision right like a longterm vision for yeah and maybe like real quick what is the one sentence summary of that div Vision forly yeah so our vision is you know is to you know build an amazing AI team you know around every individual who works you know and this team is going to help them become a 10xer you know that's what we you know we're trying to build towards and so yeah everything that we do is Guided by that like you know builds the best world's best you know AI you know the AI team like you know which is like the team consists of your you know assistants and co-workers and coaches everything you know that you know helps you sort of achieve more grow more and yeah so that's the notar like you know that of course then drives annual pring process as well as our monthly Milestones I love that I feel like I've taxed my productivity with this all these different tools not because of my own talent but just because like I can like Outsource a bunch of work that I don't want to do yeah that's fantastic if you're already there like I feel like you know we have a lot more work to do to bring people there but that's incredible like you know I think like just like you know people I do hear it you know occasionally like especially like from developers you hear that with the new Corde gen tools that like you know they're just moving so much faster than before so any like closing thoughts

or any words of advice for people to get on this track to like 10x yeah I don't I think my M advice is like you know just use AI like just use it more and I think it's important like you know one is of course for entrepreneurs or for developers what you want like what like you know I think the like thinking about you know thinking AI first is going to be helpful but I think like I'm talking about everybody in the world like you know there are so many jobs that are going to get displaced you know with AI and I think like but I also think that it's not like AI That's going to replace people I think it's going to be AI enabled people we're going to you know replace people who are actually you know AI laggards right and I think because you know this is a good technology and I think like you know we just need to you know need to learn that like you we need to sort of you know move forward with it and the that's what I would say that's my code advice yeah I totally I think people listen to this like it takes a little bit longer to learn how to do stuff with AI but once you actually do with AI then you save so much time afterwards it's worth making an initial investment yeah awesome Arvin so where can people find you or glean you know yeah I mean like you know we are on gle. com and my email is my first name arind green. com like you would love to you know connect with you know whoever like wants to learn more about us yeah all right well thanks so much for building awesome Enterprise AI tool really appreciate it thank you