How Tara AI CEO Iba Masood grew Tara AI 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...