Unsupervised, a provider of an automated analytics solution that streamlines processes including data collection, feature development, and insight discovery, said today that it has closed a $35 million Series B investment round headed by Cathay Innovation and SignalFire.
Unsupervised, a Startup That Uses AI to Automate Data Analytics Rakes VC Funding (unsupervised 35m serieswheatleysiliconangle)
The company’s platform automates tasks that require manual resources. This can reduce the time it takes to complete data analytics by months, freeing up people to act on insights uncovered by their data. The company’s platform is powered by AI that learns from data and actions. The company has raised over $55 million in funding.
Unsupervised is led by Noah Horton, a former Microsoft executive who filed more than 20 patents in the field of data analytics. He also has experience in data processing and social network content analysis. Co-founder Tyler Willis has a background in marketing and angel investing. He served as CMO at Hired and provided consulting for AngelList.
Unsupervised’s technology allows medical imaging devices to recognize anomalies in data. This can help raise awareness about faulty medical equipment, human error, and security breaches. It also makes it easier for businesses to develop buyer persona profiles. By creating these profiles, businesses can better understand the habits and traits of their business clients.
VCs are eager to invest in small startups. They are often willing to fund a preemptive round to give the founders full control over the fundraising process. A Series A round, which used to range from $8 to $12 million, now reaches $15 to $20 million. This means that the valuations of such startups are often in the $100M range or higher.
With its AI technology, Unsupervised is enabling the analysis of structured and unstructured data. This technology enables companies to identify statistically significant patterns and identify differences between subgroups. It is also able to identify differences in behavior that may exist among subgroups.
