A Cambridge company aiming to make an artificial intelligence platform for non-data scientists has raised $3 million in seed funding led by Bain Capital’s VC wing.
The two-year-old startup, Akkio, says its platform is designed to help businesses automate their decision making without having to use developers or data science resources, with training that can be as quick as one minute.
The company's co-founder and COO Jon Reilly, who spent eight years working on product development at the speaker giant Sonos, says that the concept for Akkio was born out of frustration with inefficient uses of data at companies he's worked at in the past.
"In almost every company I’ve worked at, the business itself generated data at a rate that far exceeded the ability of employees to take advantage of that data in efficient ways," Reilly said in an interview.
Trying to solve that problem created its own frustrations, with a lack of data scientists and expensive contractors, he said.
"There’s this big problem that exists, where the top very few tech companies have access to using machine learning all across their business stack, but the rest of everybody else doesn’t really have access," Reilly said. "The timelines to get projects done are long."
So in came Akkio, with the goal of making machine learning accessible.
"Today, the people able to use machine learning are basically data scientists, or in some cases data competent software engineers," Reilly said. "The next tier of people, and the people we’re targeting, are the sort of data-competent, non-data scientist, non-software engineer people. People who are heavy Excel users."
The company has hired engineers and grown to 11 employees, and even though it just announced the seed funding, is kicking off a Series A round, Reilly said.
Reilly and his co-founder Abe Parangi worked together at 3D printer developer Markforged before creating the AI firm.
Bain’s Agarwal Ajay said the company’s “no-code” solution was what attracted the investors.
Akkio says its platform has a wide range of potential uses, from helping a political fundraiser prioritize call lists to helping a medical device manufacturer predict clinical trial abandonment. It’s also been tested by betting on horse races and filling out March Madness brackets.
