U.S. Artificial Intelligence Company Launches Loss Prevention System to Transform Multi-Location Security

Share Article

At the International Security Conference & Exposition (ISC West) DeepCam introduced Retail by DeepCam, a plug-and-play system that drastically improves retail loss prevention and slashes operational costs

After three years in stealth mode, artificial intelligence (AI) company, DeepCam, has announced the release of a system that will revolutionize retail loss prevention. At the International Security Conference & Exposition (ISC West) today, DeepCam introduced Retail by DeepCam, a plug-and-play system that drastically improves retail loss prevention and slashes operational costs with DeepCam Advice, a proprietary biometric-enhanced recommendation engine that identifies shoplifting and other suspicious behaviors.

To download videos and photos about DeepCam, visit DeepCamAI.com/press-center.

Developed by an AI team with more than 200 years of combined experience, DeepCam is a proven success in extensive trials internationally and with select U.S. retailers. Based in Longmont, Colo., DeepCam’s comprehensive system achieves the large-population performance required in public safety and multi-location retail and banking applications.

“When you have a small population, such as an office building, facial recognition is easy,” said Don Knasel, DeepCam Founder and CEO. “Where biometrics fail is with large populations — tens of thousands or hundreds of thousands of people — and that’s what sets our AI technology and recommendation engine apart. Our systems have been proven to work accurately with large populations in the millions.”

Retail by DeepCam is designed specifically for multi-location stores, which may already have some kind of loss prevention system but likely miss the vast majority of shoplifters. DeepCam Advice looks for shoplifting and other suspicious behaviors that indicate further attention. Loss Prevention personnel review these incidents, tagging those they identify as shoplifters who should not be allowed back in the store. Then the system notifies store employees, allowing managers to stop the criminal and ban them from entering again.

“Some stores may catch as few as 10 percent of shoplifters,” said Knasel. “Our system not only catches that 10 percent but is designed to drastically close the gap on what other systems miss.”

The system uses three proprietary technologies: Match — biometric identification for large populations; Index — cross-camera event association; and Advise — behavior analytics. Multi-location businesses also benefit from the DeepCam Network, allowing every location to use loss prevention intelligence from its affiliated locations. When a criminal is identified at one store, and they decide to hit another location, the personnel at the new location are notified and can act.

A technology and big data analytics system that goes beyond loss prevention, DeepCam also delivers wide-ranging retail intelligence that can be utilized for customer appreciation, merchandising optimization and operations improvement. Retail by DeepCam can identify everything from the number of customers at a given time or day, queue wait times, and shopping habits by demographics and location.

After the completion of extensive trials with large retailers in the U.S. and in public safety settings abroad, DeepCam began shipping in the fourth quarter of 2017. In the U.S. market, Retail by DeepCam began to roll out in the first quarter of this year.

About DeepCam
DeepCam is revolutionizing multi-location retail and banking loss prevention through artificial intelligence (AI). Retail by DeepCam is a biometric-enhanced recommendation engine that drastically reduces loss while slashing loss prevention costs. With more than 200 years of combined AI experience, DeepCam delivers plug-and-play simplicity in scalable systems built for large-population environments where other technologies fail. To learn more about DeepCam, visit DeepCamAI.com.

Share article on social media or email:

View article via:

Pdf Print

Contact Author

Meghan Dougherty

Erin Wagner
(507) 237-6527
Email >
Visit website