Coram AI Secures $35 Million for AI-Powered Security Investigation Tools
Coram AI has raised $35 million in Series B funding to enhance its AI platform, which turns existing security cameras into autonomous investigators. The software, 'Deep Investigation', analyzes footage and access logs in plain language, reducing investigation time. The company aims to modernize physical security systems, processing data locally to address privacy concerns.
Key points
- Coram AI, a San Francisco-based company, raised $35 million in its Series B funding round, co-led by Ansa Capital and Battery Ventures.
- The new funding brings Coram's total raised capital to $66 million.
- Coram's 'Deep Investigation' software uses AI to analyze months of video footage, access logs, and visitor data across multiple sites in response to plain-language queries.
- The platform processes data locally on NVIDIA chips, aiming to keep sensitive video footage within the premises for enhanced privacy.
- Coram's technology, already deployed at over 1,500 locations, offers features like facial recognition and anomaly detection for physical security.
- The company was founded four years ago by Ashesh Jain and Peter Ondruska.
Coram AI, a technology firm focused on physical security, has successfully closed a $35 million Series B funding round. The investment was co-led by Ansa Capital and Battery Ventures, with participation from existing investors UP Partners, 8VC, and Mosaic Ventures. This latest funding brings Coram's total capital raised to $66 million since its inception.
The company's core product, dubbed 'Deep Investigation', is designed to transform existing security camera infrastructure into an AI-powered investigative tool. Coram states its software allows security personnel to query months of video footage, access records, and other data using simple, natural language commands. The system then compiles reports, significantly reducing the time typically spent manually reviewing hours of surveillance material.
Coram emphasizes its commitment to privacy by processing AI models locally on edge devices, specifically mentioning NVIDIA chips. This approach aims to prevent sensitive video data from being sent to the cloud. The platform is designed to work with existing IP cameras, avoiding the need for costly hardware replacements. The technology supports various functions including facial recognition, license plate reading, and detection of security breaches like tailgating and the presence of firearms, and is being deployed in diverse settings such as schools, factories, and religious institutions.
Sources
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