SaaS Interviews with CEOs, Startups, Founders
1156 YC Alum Burning $110k/mo in Cash To Scope Projects and Place Dev Talent Using Machine Learning
23 Sep 2018
Chapter 1: What inspired Iba Masood to start TARA.ai?
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 repositories, really. After trained, it got the first paying customer in 2017 or around that timeframe. Now 65 customers paying an average 1200 bucks a month. They're up to about $80,000 per month in revenue.
That's up from $20,000 a month in revenue just 16 months ago, so healthy growth. That's part of what enabled them to just close $3 million in funding. They have 8% monthly logo turn, so kind of high, but they're working on driving that down, spending about $20,000 to acquire a $120,000 ACV customer. Healthy economics with their team of 12.
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Chapter 2: How does TARA.ai utilize artificial intelligence in product development?
We went from a couple of hundred thousand dollars to 2.7 million. I had no money when I started the company. It was $160 million, which is the size of many IPOs. We're a bit strapped. We have like 22,000 customers. With over 5 million downloads in a very short amount of time, major outlets like Inc. are calling us the fastest growing business show on iTunes.
I'm your host, Nathan Latka, and here's today's episode. 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's Winter 2015 class.
Chapter 3: What challenges did TARA.ai face in its early days?
She was recently awarded Forbes 30 Under 30 for the 2018 list under the field of enterprise technology. In August 2017, Iba became a permanent resident of the United States through the EB1A Award, which presents individuals of outstanding ability with the green card. Iba, are you ready to take us to the top?
Yes, I am. 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 my co-founder, Sayed, 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.
And one of the reasons why we started the company was because we personally saw a lot of issues with the product management process as it was. 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.
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Chapter 4: How has TARA.ai's revenue model evolved over time?
I'm a financial analyst. 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 weeks, Brent, and then seeing if 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, it's actually their 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 to just figure out our data structure. So Sayed, because he's a roboticist, he built a team of mechanical engineers, machine learning engineers. And as a team, what we did was we figured out that we could actually scrape the open web, essentially open source platforms that already have existing code and existing software projects.
So we used roughly about our early data set included about 5,000 software projects. And we use 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 get data that was clean and then 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 the overall neural network, we were doing a lot of things manually at the beginning. We actually brought product managers on board on our team.
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Chapter 5: What is the significance of customer acquisition costs for TARA.ai?
We had about 10 to 15 contractors early on that were actually running the scoping process. We were comparing results between what the AI could do versus what human product managers were mapping out. What we found was that there was about 70% of an actual product manager's time was really going into the scoping process and understanding and figuring out how the work should be divided week by week.
And we realized that a lot of this work could be automated by learning from what had been done in the past. 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 projects as a whole because...
Typically, when people are pursuing side projects, 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 to where you need to be. So these kind of hypotheses really formed our early product.
And, uh, and so what we were surprised by was that initially we thought real quick before you tell me surprises.
Chapter 6: How does TARA.ai ensure quality in talent placement?
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. So large enterprises, they typically come in and they have, um, annual contracts and that's licensing for the scoping software.
Can you give me general, I mean, are we talking like a hundred grand, a million, 10 grand annual, like average would you say is what per year?
So we're looking at about 120K annual. And then 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 more efficient.
Yep. Yeah, so like... When I think of like the closest thing to you that I have personally used, I think of TopTal, 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 manage to convince top tier development talent to work through your platform versus TopTal 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 Sayed, both of us. So what we did was we were freelancers on those platforms for about two years or so, 1.5 to two years.
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Chapter 7: What are the company's future growth plans and targets?
We found that the average developer was being paid about $200 on the leading freelance marketplaces like Upwork. Per hour? $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 build a meritocracy where folks that didn't have, you know, a 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.
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 when this was like in the U.S. We were initially called Gradberry and we were primarily like focusing on full time 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 primarily for freelance positions.
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. Total companies, we have about 65. Okay, 65.
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Chapter 8: What advice does Iba Masood have for aspiring entrepreneurs?
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That deal is available exclusively at nathanlaca.com forward slash SEMrush. Again, that's nathanlaca.com forward slash SEMrush. 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 SaaS side of things? Are those numbers accurate?
Nope.
No, because a lot of the customers that are, so we do have the 65 customers. A lot of them are on small, like 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, as a product that's, you know, about 120 K and above as a, you know, and so we really only started doing this. this year.
So we started pitching our 120K annual contract model last month. Before that, a lot of our customers were on 40K to 50K annual contracts. And so now we're really starting to, because the thing is, we didn't have the enterprise features back then.
And so as we're scaling up and understanding, because enterprise issues that they're facing and challenges that they face are very different from the mid-market companies that we were typically targeting.
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