Chooch AI receives $20M in funding to tackle edge biometrics and visual AI market
Chooch AI, a provider of technology and services for visual AI, announced it has secured $20M in a Series A round of funding to pursue the development of software that is helping companies quickly deploy computer vision systems—systems that have greater speed and accuracy for biometric identification systems.
Venture capital firm Vickers Venture Partners led the round, with additional institutional funding from 212, Streamlined Ventures, Alumni Ventures Group, Waterman Ventures and others. The funding follows a seed round of funding in 2019 from Vickers.
“AI startups and established players in artificial intelligence often focus on vertical applications. Chooch has a bigger vision, a horizontal AI platform that provides flexible solutions for the common demands of many companies, regardless of industry,” commented Vickers Venture Partners Chairman Dr. Finian Tan in a press release. The company notes that it already has clients in market verticals such as geospatial, healthcare, security, media, industrial and retail industries spanning both enterprise and government clients.
Part of the success in 2020 has rested on offering pre-trained models for workplace safety, fire detection, crowd analysis and more.
“The pandemic has accelerated public safety based on cameras, so we’ve gotten a lot of traction on the PPE front,” said Emrah Gultelkin, co-founder and CEO of Chooch AI in an interview earlier this year with Biometric Update. “Touchless boarding and touchless check-in have come in post-COVID. We’re seeing the general acceleration of interest in AI deployments across the field just because people are more remote, and you know either you’re looking for assistance to do, so it has been a good ride for us post-COVID,” he noted.
Chooch.AI says that the pre-trained models do not require customers to already have data for training models. Chooch.AI also said that its edge AI deployments can now achieve “extreme response-time performance” of twenty milliseconds on multiple video feeds with extremely high accuracy for a wide variety of use cases.