Tara.ai, which uses machine learning to spec out and manage engineering projects, nabs $10M

Artificial intelligence has become an increasingly important component of how a lot of technology works; now it’s also being applied to how technologists themselves work. Today, one of the startups building such a tool has raised some capital, Tara.ai, a platform that uses machine learning to help an organization get engineering projects done — from identifying and predicting the work that will need to be tackled, to sourcing talent to execute that, and then monitoring the project of that project — has raised a Series A of $10 million to continue building out its platform.

The funding for the company cofounded by Iba Masood (she is now CEO) and Syed Ahmed comes from an interesting group of investors that point to Tara’s origins, as well as how it sees its product developing over time.

The round was led by Aspect Ventures (the female-led firm that puts a notable but not exclusive emphasis on female-founded startups) with participation also from Slack, by way of its Slack Fund. Previous investors Y Combinator and Moment Ventures also participated in the round. (Y Combinator provides an avenue to companies from its cohorts to help them source their Series A rounds, and Tara.ai went through this process.)

Tara.ai was originally founded as Gradberry out of Y Combinator, with its initial focus on using an AI platform for organizations to evaluate and help source engineering talent: Tara.ai was originally that name of its AI engine.

(The origin of how Masood and Ahmed identified this problem was through their own direct experience: both were engineering grads from the American University of Sharjah in the U.A.E. that had problems getting hired because no one had ever heard of their university. Even so, they had won an MIT-affiliated startup competition in Morocco and relocated to Boston. The idea with Gradberry was to cut through the big names and focus just on what people could do.)

Masood and Syed (who eventually got married) eventually realised that using that engine to evaluate the wider challenges of executing engineering projects came as a natural progression once the team started digging into the challenges and identifying what actually needed to be solved.

A study that Tara conducted across some 5,000 projects found that $66 billion dollars were identified as “lost” due to projects running past the expected completion time, lack of adequate talent and just overall poor planning.

“We realised that recruiting was actually the final decision you make, not the first, and we wanted to be involved earlier in the decision-making process,” Masood said in an interview. “We saw a much bigger opportunity looking not at the people, but the whole project.”

In action, that means that Tara.ai is used not just to scope out the nature of the problem that needed to be solved, or the goal that an organization wanted to achieve; it is also used to suggest which frameworks will need to be used to execute on that goal, and then suggest a timeline to follow.

Then, it starts to evaluate a company’s own staff expertise, along with that from other recruiting platforms, to figure out which people to source from within the company. Eventually, that will also be complemented with sourcing information from outside the organization — either contractors or new hires.

Masood noted that a large proportion of users in the tech world today use Jira and platforms like it to manage projects. While there are some tools in Jira to help plan out projects better, Tara is proposing its platform as a kind of virtual project manager, or an assistant to an existing project manager, to conceive of the whole project, not just help with the admin of getting it done.

Notably, right now she says that some 75% of Tara.ai’s users — customers include Cisco, Orange Silicon Valley and Mower Digital — are “not technical,” meaning they themselves do not ship or use code. “This helps them understand what could be considered and the dependencies that can be expected out of a project,” she notes.

Lauren Kolodny, the partner at Aspect who led the investment, said that one of the things that stood out for her, in fact, with Tara.ai, was precisely how it could be applied exactly in those kinds of scenarios.

Today, tech is such a fundamental part of how a lot of businesses operate, but that doesn’t mean that every business is natively a technology one (think here of food and beverage companies as an example, or government agencies). In those cases, these companies would have traditionally had to turn to outside consultants to identify opportunities, and then build and potentially long-term operate whatever the solutions become. Now there is an opportunity to rethink how technology is used in these kinds of organizations.

“Projects have been hacked together from multiple systems, not really built in combination,” Kolodny said of how much development happens at these traditional businesses. “We are really excited about the machine learning scoping and mapping of internal and external talent, which is looking to be particularly important as traditional enterprises are required to get level with newer businesses, and the amount of talent they need to execute on these projects becomes challenging.”

Tara.ai’s next steps will involve essentially taking the building blocks of what you can think of as a very power talent and engineering project search engine, and making it more powerful. That will include integrating databases of external consultants and figuring out how best to have these in tandem with internal teams while keeping them working well together. And soon to come also will be bug prediction: how to identify these before they arise in a project.

The Slack investment is also a notable nod to what direction Tara.ai will take. Masood said that Slack was one of three “big tech” companies interested in investing in this round, and she and Syed chose Slack because from what they could see of its existing and target customers, many were already using it and some have already started requesting closer collaboration so that events in one could come up as updates in the other.

“Our largest customers are heavy Slack users and they are already having conversations in Slack related to projects in Tara.ai,” she said. “W are tackling the scoping element and now seeing how to link up even command line interfaces between the two.”

She noted that this does not rule out closer integrations with communications and other platforms that people use on a daily basis to get their work done: the idea is to become a tool to work better overall.