Two ex-Humane, the AI hardware startup, executives now run Infactory, an AI fact-checking search engine that reportedly raised $4 million at a $25 million valuation. Brooke Hartley Moy and Ken Kocienda, who spent time as Humane’s strategic partnerships lead and head of product engineering, left in May when their AI Pin device, which looked like a smart Pin kind of thing, did little to impress.
Infactory is building an honest and explainable tool that searches enterprise databases and the open web. Targeting finance, insurance, SaaS, healthcare, and media industries, this is a startup that’s solving increasingly important needs for AI-driven solutions. “Building this kind of product was never going to be a fit at a consumer hardware company,” says Hartley Moy, now Infactory’s CEO.
In fact, the exit was due to more promising business opportunities rather than because of the negative critiques from the reviews of the AI Pin. As Hartley Moy explained, “The reality was this had been brewing for some time, not to do with the reviews.”
Humane is currently in talks with potential suitors, having held preliminary discussions with companies such as HP and several telecom corporations. The startup has previously raised $100 million from investors who include Microsoft and Tiger Global, pushing its total funding past $200 million.
Both Hartley Moy and Kocienda come with incredibly strong experience for Infactory. Hartley Moy has had experience with Salesforce, Slack, and Google on software partnerships. Kocienda, in his role as the CTO of Infactory, previously spent over 15 years at Apple, driving the development of the autocorrect feature for the first iPhone.
Infactory raised its seed funding led by Bee Partners, with additional funding from Andreessen Horowitz among others. The company also made use of the relatively common means of funding among AI startups-a special-purpose vehicle.
At its current alpha stage, Infactory is working with design partners to lock in its product before its larger rollout later this year. The architecture has been designed to be extremely aggressive on requirements of accuracy and trustworthiness requirements natively for businesses outside of the sector-natively AI-native businesses. “A lot of businesses want to enter into this ecosystem, but their requirements are regimented around accuracy and high-quality answers,” said Hartley Moy
The Infactory approach focuses on how to prepare data to enhance the analysis and response abilities of an AI model. For example, on the clinical side, a doctor could check potential drug interactions using a patient’s medication history via Infactory, which would source answers for such questions from within internal data.
Kocienda added that the model behind Infactory does not suffer from what is common in other AI chatbots: incorrect information. At no point is there any black box where a question goes in and an answer comes out without understanding its origin,” he said, positioning Infactory as a solution to the challenges the ‘rapidly evolving AI landscape’ presents. As competition heats up between tech giants, revenue from AI solutions is expected to rise to more than $1 trillion over the next decade.
