How Tara AI CEO Iba Masood grew to $6.2M revenue and 65 customers in 2024.

hello everybody my guest today is iba masood she is the co-founder and ceo of a company called tara intelligence she's a yc alum and has been part of y combinator y combinator's winter 2015 class she was recently awarded forbes 30 under 30 for the 2018 list under the field of enterprise technology in august 2017 eva became a permanent resident of the united states through the eb-1a award which presents individuals of outstanding ability with the green card ebar are you ready to take us to the top yes i am oh my god thank you for having me i'm so curious about so many things ranging from the green card to the company to everything but let's focus on the company tell us what you do and how do you make money so um my co-founder says and i we started tara intelligence to essentially help founders product managers improve the product development process and so tara uses artificial intelligence to essentially map out product milestones and it also assigns contractors that are freelance developers to actually execute on these milestones um and and you know one of the reasons why we started the company was because we personally had you know we saw a lot of issues with the product management process as it was and uh and we personally felt that you could essentially apply ai to this field specifically by scraping projects off the open web so my co-founder is a roboticist i'm a financial analyst and and we kind of came together because we met freshman year of college but yeah but there is no bigger pain point than the product manager with the scope documents from design trying to communicate the scope clearly efficiently and effectively to the development team understanding what goes in the next two week sprint and then seeing it actually gets delivered and then applying you know you know multipliers per developer because you know this one it's actually one and a half times what they say and this one is actually they're way faster than what they say how do you i mean i could never figure that out manually with a human how do you figure it out with artificial intelligence so it actually took us about two and a half years um to just figure out our data structure so um so say it because he's a roboticist he built a team of um mechanical engineers machine learning engineers and as a team what we did was we figured out that we could actually scrape the open web um essentially open source platforms that already have existing code and existing software projects so we used roughly about our early data set included about 5000 software projects and we used that to train our system to understand that okay with an ios app if the ios app requires a two-sided marketplace here are the typical milestones tasks and this is the typical timeline so it took us a while to actually like get data that was clean and then you know to figure out how to scrub that data and build out the platform but what we did during that time was as we were building out the um the overall neural network we were doing a lot of things manually um at the beginning so we actually brought um product managers on board on our team we had about 10 to 15 contractors early on that were actually you know running the scoping process and we were comparing results between uh what what the ai could do versus what um what cuban product managers um were mapping out and what we found was that there was about 70 percent of um of an actual product manager's time was really going into the scoping process and understanding and figuring out like how how the work should be divided uh week by week and we realized that a lot of a lot of this work could be automated by learning from what had been done in the past and um and specifically by looking at open source projects because we as a company we have this hypothesis that open source projects are one of the most efficiently run uh projects as a whole because typically when people are pursuing side projects they um they have a timeline and they need to get things done quickly but they like to also pursue shortcuts shortcuts that are actually efficient in nature that can use some level of open source code to get uh to get to where you need to be so you know these these kind of hypotheses really formed our um the our early product and uh and so what we were surprised by was that initially we thought real quick before you saw my surprises so what's your pricing model today is it a pure sas platform or is it a marketplace charge it's both so what we do is we have the licensing model uh so large enterprises they typically come in and they have um annual contracts and that's licensing for the scoping software and can you give me generals i mean are we talking like a hundred grand a million ten grand annual like average would you say is what per year so we're looking at about 120k um annual and then um on top of that there's also marketplace charges so one of the things we're introducing over time are api connections api integrations to actually help make the project or the software development process uh more efficient yep yeah so so like when i think of like the closest thing to you that i have personally used i think of top tal which is just hiring developers i still have to manage scope and everything i don't think they have ai on the back end so how do you how do you manage to convince top tier development plat talent to work through your platform form versus top towel or versus going in-house at a company yeah so you know what we found so i actually personally was a freelancer on these platforms me and say it both of us so what we did was um we were we were freelancers on those platforms for about two years or so um 1.5 to 2 years we found that the average developer was being paid about 200 on the leading freelance marketplaces like upwork and they had to uh 200 for a small widget project okay and so they would need to take at least 10 to 18 of those projects in a month so that they could make like a sizable income and we're like okay what if you could make that sizable income with just one project that was typically with like a larger enterprise and had higher payouts so that was like our pitch to developers when we were building out the marketplace very early on and then the other part of it was also equal pay so one of the things we really believe in as a company is we wanted to build a meritocracy where folks that didn't have um you know computer science degree from an ivy league university could join tara but on the basis purely on the basis of their code so we would actually look at their existing github and other platforms to give them a score um that's great there's nobody else doing that give me give me more of the backstory what year did you launch in so we launched in 2015. okay um when this was like in the u.s uh we were initially called gradberry and we were primarily like focusing on full-time uh software positions and we found that there was like a much bigger market in terms of on the contracting side plus we found that companies were more willing to allow ai to make the recruiting decision when it was um primarily for freelance positions um so even with full time they were still not willing to like allow an algorithm to recruit developers specifically that were you know that was meritocratic in nature and what have you scaled to today in terms of total companies using the platform really not companies they're really enterprises how many enterprises using the platform so enterprises we have about 10 a total companies we have about 65. okay 65. got it yep why do you the the ones that are not enterprise why do you not call them enterprise is it just a price point thing or it's the nature of how they use you in nature of how they use us so you know they don't need like you know enterprise level security um and they're typically we classify them as mid-market um and and you know with mid-market we've seen that the need is is primarily and you know some of these are like sporting goods companies some of these are they're not your typical technology companies or i.t companies but they need to innovate and iterate on on their existing products so for example like a sporting goods company wants to make um jersey designers like online uh online designers you know so so we're seeing that the need is um is pretty prevalent not only in tech companies but also in companies that are kind of like you know wanting to innovate so those guys come in under they can get started with you for under the 120k are you still that's really your floor is 120. the floor is typically 120. okay but what we've seen is we do bring in like mid-market companies for six-month uh projects as well okay so we've had like projects come in for about three to six months where they're typically like starting off with a pilot in some cases and then once the pilot works out then they kind of scale up to an annual contract so i want to talk more about that in terms of churn because typically projects like they're not sometimes they're not always going i'll talk about that in a second but first i'm curious about the breakdown so if you look at your past 12 months would you what percent of your revenue would you say is from placing the talent versus the annual fees that are pure sas that's a great question so about 60 of our revenue comes from placing the talent and about 40 is coming from the software we do think that you know those metrics may change over time but i think what's more exciting is now when we first started with enterprise we were kind of going on a one-off project basis and what was happening was so for example with like the likes of cisco um we were doing multiple projects um within like one enterprise but it was with different teams so what we decided to do now is um get into multiple teams from the get-go um and the way we're doing that is we're essentially like doing these pilots where we're able to prove out the the value um with like an initial five-figure deal and then from that move into a six-figure deal when you know they have the confidence to really like scale it up and so that's what we're pursuing like specifically this quarter and next quarter that's exciting i want to talk more about that especially your team makeup but first just to round out the customer account so i mean eva can i take the 65 folks at 120 grand ish per year and assume you guys are doing north of 650 grand per month just on the sas side of things is that is are those numbers accurate no because a lot of the customers that are um so we do have the 65 customers a lot of them are on small like uh basically on a smaller contracts so what happened was when we first started as a company we were like okay can we actually you know market ourselves as a product that's you know about 120k and above yeah um as uh you know and so we really only started doing this this year so we started pitching our 120k annual contract model last month um before that a lot of our customers were on 40k to 50k i see and so now we're really starting to because the thing is we didn't have the enterprise features um back then and so as we're scaling up and understanding because enterprise um issues that they're facing and challenges that they face are very different um from you know the mid-market companies that we were typically targeting so just to add just to edit that that math thing for a second so people you're onboarding now are 120 makes perfect sense why you're going up market but historically at a 40 000 acv that comes out to about three grand per month times your 65 that would put you more around the 200 grand per month range on just the sas platform and about 270 or 280 on the placement fees on top of that is that generally accurate or is it different it's a little different just because of the fact that um about uh 60 percent of that is coming from talent placement so 60 of the 200 yes exactly okay so about uh 60 of the 200 is coming from the talent placement model and this the software component again is also something we released recently so the software component yeah exactly so software component as well as annual contract in terms of going higher acd that really only happened because um i mean if you look at our product in general we released the first version of our platform last year okay so it was like about about six months ago that we released a version that was you know fully functioning that could actually scope out at least about 40 percent of your build and could you know um really take you from scoping all the way to performance analytics so it's but it's fair to say you just recently or you're getting ready to pass that 200 grand per month mark when you combine both revenue streams is that accurate yes exactly so like where over the next six to seven months is really i think going to be critical for us because one of the things we're realizing is that we're able to get to a higher account value with a lot of the enterprise customers we're talking to um specifically even if they're just coming in on a about 120k for three to six month pilots so we're actually seeing we're actually seeing an opportunity to grow beyond the 120k acv as well but we essentially need to get our enterprise security features up and running which are exciting it's an exciting time to be in um before we talk more about again that plan over the next six seven months growth wise what are you going out year over year is it 50 100 percent but right now it's about 140 percent so if i go back 12 months from today you were doing about 80 grand a month uh no we were doing way less actually so how little we were doing about uh 20k holy mackerel so back at the end of december 2016 you're doing 20 grand a month then so i mean you've more than 10x since then that's that's the plan basically because a lot of the a lot of the revenues coming in on a per project basis and so what the goal is is to essentially add the software component on top of the contractors i understand that but i mean like last month if you look at just last month's revenue you you're at around 200 grand right in revenue for the month not yet can you share where you're at today just like in a general sense of growth yeah so as of this month we're looking at about 80k mrr okay and so from but we're actually going to go from 80k to about 140k in the next two months right because of the enterprise deals that we're almost about to close and but the key component here is we're essentially trying to understand um how much of the security components and security features we need to have up front before we start engaging in those enterprise discussions and i think that's something that like every founder has to kind of 100 yeah so just to just to go back and correct the rpoo for a second at 80 grand a month today at 65 customers each one's paying about 1200 bucks a month is that that's more right but moving forward you're going up which i completely get now let me ask you a question you said you just added the software component was that because you're having churn issues on just the placement model it was too lumpy it was actually because that was the plan from day one so for us what we wanted to do was we wanted to build scoping software that was essentially able to take on um the the specific customers that we were onboarding and what we found was we needed to close a lot of these per project like we need to close a lot of these accounts on a per project basis to actually get the software to learn that was that was essentially our goal so a lot of the scoping a lot of the um a lot of what the software does today was being done manually and we didn't want to charge for it we were like you know what this is something i think we can offer for free to our customers but then as as customers are now coming in they're onboarding more projects so we're seeing an average of about eight to nine projects being onboarded onto the system rather than the one to two um now the goal is to essentially like utilize more of the more of the machine learning capabilities of the software because it it takes time that's the thing like building um building ml software that actually works takes a lot of time because you really need to figure out your data structures from data what do you see yourself being though like i mean because look top towel is hitting you on just the marketplace side that's all they do and then there's other companies that just do some of the ai machine learning that kind of stuff you're fighting two wars i mean is there one you're generally trying to go more towards i think our goal is to essentially become an end-to-end platform because at the end of the day what what how we look at our company is that we look at the product being at the center and the core of just about everything that the company that our customers have to do so if you place the product at the core then hiring analytics api integrations and as well as the talent that's assigned a lot of that is is a part of the whole product equation so because we look at it um we look at it from that perspective we believe that more and more enterprise companies are now going to start organizing their teams based on based on products and as they become more agile their need for software development becomes more extensive how much have you given it and so i think what we've seen is that what really differentiates us in terms of within the competitive landscape is the ability to do not only go not only do the scoping but then also do scoping talent placement as well as the performance analytics in terms of how the developers are performing over time so we do think that we're going to be partnering with other existing marketplaces um that you know where we can pull talent from but the goal is to provide that end-to-end platform because that's really where we've seen the need so that the pm doesn't need to go why would why would top talk give you access to their talent though i don't see what's in it for them we're not looking at top down we're more like looking at upwork we're looking at platforms that have um you know a larger base of contractors specifically um as well as you know with with i don't think top tile's going to give us access to their talent what what we're what we're doing today is that we're offering talent you know more opportunities to um to make money because we have top top developers on our platform too it's not like every developer just joins one platform they usually join multiple well and then they go deep on the ones that make them the most money i i know top towels numbers they're significantly larger than what you guys are that's what i'm trying to understand is what you have that they don't have is the software component is the scheduling component the machine learning the ai but what you're telling me is no you want to keep doing both i think the time will really tell because the main thing is that with the with the machine learning component what we've seen is the reason why developers like using tara is because they don't have to do the scoping part on toptel and all these other platforms you as a developer have to sit down scope out your milestones and tasks and answer every question that the client has specifically on the specs side with tara the entire process is being managed through our platform so i think that's what really excites us where developers have can spend more time coding and less time managing scope have you given up equity or are you bootstrapped yes so we just raised a three million round for our seed okay so and is that is that total you've raised or what have you raised in total yes total three million okay got it and walk me back through you launched in 2015 what are you now today in terms of total team size uh so we are at about 12 people today okay and uh we didn't launch in 2015 we started developing the product in 2015 because it takes two to three years just to build out an ml platform i mean when did you have your first paying customer our first paying customer came in last year so what were you i mean how are you feeding yourself the first two years so the first two years what we were i mean so we had just graduated out of yc uh we had raised about 400k post yc so that was essentially what was keeping us going how many of your clients are wise are they went to the yc program or the they're part of the yc network i believe two oh two okay not a ton why aren't more using it i think that we we've just been so focused on mid-market and enterprise i think we we just haven't really i think given given much thought to like specifically focusing on startups just because enterprise and market are a completely different view i mean i would put weebly's not a startup they're doing well north of 24 million in arr but they were i'm talking i'm not talking about current people in the yc program i mean yc network yc alums i'm i think that'd be an easier sale for you because you guys share that common bond i'm curious why more of them aren't using you i think it's just because we we haven't specifically like sat down and looked at how many of the yc customers we can onboard and and you know what's our startup strategy because the thing is that with with our platform we're really trying to like specifically focus on enterprise i'm referencing enterprise companies that went through yc many years ago like weebly which are is very much an enterprise sized company so when we when i say enterprise i'm talking about typically like i publicly traded companies i see so we're looking at companies that have um thousands of engineers under their belt and they're essentially looking to figure out how to optimize the scoping process i see because what we've seen that when we talk to startups and smaller and i mean you know if you're if you're south of a thousand if you're south of a thousand employees then you're typically not dealing with the issues that large enterprise is facing and we think that we can optimize our platform by specifically focusing on larger publicly traded companies because the system will learn faster we can get hundreds of thousands of projects from one large enterprise and once we've actually like conquered that area we think that we'd be able to specifically focus on startups later down the line what's your turn what's your turn today we're at about uh eight percent sure okay and that's monthly logo churn yeah okay and then what are you spending to acquire these customers uh we're we're spending a lot like customer acquisition costs are pretty high and i think over time what we'll be able to do by the way you can afford to do that right i mean you can afford that so like are you talking 50 grand 100 grand what do you spend uh so well our our burn as of today is at about 110k okay um so we're still pretty low on the side yep okay but but just to be clear sorry i'm just talking about to on you have 65 customers yeah yeah specifically so for example if one customer um closes at about um 120 120k then we've probably spent roughly about 20 thousand dollars to acquire them okay well i mean that's not bad that's a payback period of like two months three months exactly but again you know i think because it's so new in terms of like us launching the software so recently really time will tell and i think like our costs will go up from a customer efficient standpoint you just raised this capital real quick because we're running out of time where is most of the money gonna go what are you gonna spend it on uh so primarily on the engineering side um specifically because as we're um as we're expanding with an enterprise we really feel that we need to bring on the right team um to build out to build out the software component all right eva let's wrap up here with the famous five number one what's your favorite business book uh i think anything anything by near eol he's a good one number two is there a ceo you're following or studying right now yes uh teresa tucker i think she's an incredible female ceo who's uh who's ipoded company just by bootstrapping what's your company uh treestucker blackline blackline oh yeah she's speaking at the recurring revenue conference coming up here soon in l.a should be good all right that's awesome yeah number two there's a whole story you know it's funny there she i don't know if you know the story there but like she lost her husband divorced her then husband came back as she was building the company was in serious serious debt she has an incredible story i'm glad you mentioned her and gave her some gave her some spotlight there uh number three what's your favorite online tool for building your business uh today uh it changes every day today it's slack slack okay number four how many hours i sleep to eat every night one seven and a half okay and what's your situation married single you have kiddos married actually married to my co-founder oh my gosh amazing how many kids no she says with a dissatisfied look on her face and and and do you mind me asking even how old you are i am 28. 28. okay last question what do you wish your 20 year old self knew that um you know graduating with a perfect gpa doesn't matter perfect gpas don't matter in startup land there you guys got it from eba she launched tara intelligence back in 2015 hustled for two years just to teach the system right how to get accurate scoping out using open source uh repositories really after training got the first paying customer in 2017 around that time frame now 65 customers paying average 200 bucks a month they're up to about 80 grand per month in revenue that's up from 20 grand a month in revenue just 16 months ago so healthy growth that's part of what enabled them to just close 3 million bucks in funding they have 8 monthly logo turns so kind of high but they're working on driving that down spending about 20 grand to acquire a 120 000 acv customer healthy economics with their team of 12. eva thank you for taking us to the top thank you appreciate it