When Yhat, the company that has developed solutions to help organize data scientist teams, graduated from the Y Combinator, winter 2015 class, the founders had a goal to raise a million dollars to keep growing the company when they returned to New York.
They may have aimed too low. The team actually was able to raise $1.5 million in their oversubscribed round, thanks to the interest in their technology.
Being part of Y Combinator enabled the founders to meet people in Silicon Valley — really one of the goals when they applied. The company already had east coast finance connections, but the founders hoped to build a network in Silicon Valley too. Being part of YC opened the door to meeting the right people, Yhat CEO and co-founder Austin Ogilvie explained.
“Y Combinator was an excellent way to build a west coast network of entrepreneurs and experienced software investment operators,” he said.
The Post-YC financing objective was a $1 million raise and was mostly focused on meeting these strategic west coast angel investors. In particular they hoped to attract enterprise SaaS executives.
And they found some good ones in Tikhon Bernstam, who co-founded Scribd and Parse; Ilya Sukhar, another Parse co-founder; and Justin Kan, who founded Twitch. These are experienced entrepreneurs and that’s precisely what Ogilvie and his co-founder Greg Lamp were hoping for.
In fact, they got a mix of over 20 investors, including several backers from the first seed round. Currently Yhat has 14 customers. Ogilvie joked that his company is “a mighty team of 9″ right now, but there are plans to expand.
The company doesn’t plan to sit still in the wake of Y Combinator, which he described as a whirlwind of activity.”The whole YC conclusion was overwhelming in a good way, but I’m glad to be back and focussing all of my attention on my business,” Ogilvie said.
With money in hand, co-founder Lamp flew to New York to get more office space. Over the next six months, they plan to hire at least 4 new employees.
The company has two products at the moment, ScienceOps and ScienceBox. The former, as they explained to TechCrunch last winter, “they developed as a solution designed to help teams of data scientists work and communicate more effectively with one another as they built projects on top of popular data science tools like R and Python.”
“Largely that road map [for ScienceOps] is centered around the idea of helping enterprises, understand the efficacy of the predictive models they are using in day-to-day decision making,” Ogilvie explained.
This will allow customers to measure the quality of the rules and predictive models they are using for their business decisions. He offers these examples: “How good are my product recommendations today vs. last week vs. last year?” or “How is our newly minted credit scoring model performing and are we seeing those lower credit loss rates we expect?”
The next version of ScienceOps is in early beta with wider rollout expected some time in the third quarter this year. Early customers are giving favorable reports, he said.
ScienceBox was the second product and designed to help smaller teams of data scientists
Ogilvie also gave a glimpse of a future product that’s not quite ready for release called ScienceCluster. It will enable data science teams to build clusters made up of multiple servers. This will unleash power that should create entirely new use cases.
It’s a crazy, fast-growing market and it takes a lot of education to explain the products to large organizations, many of which are just beginning to dabble in data science, he says.
Yhat has some funds now to keep it going, but Ogilvie made it clear he’s in it for the long haul. He wants to see his company grow and develop into a much larger organization.
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