Conntour raises $7M from General Catalyst, YC to build an AI search engine for security video systems
As privacy debates intensify around surveillance technology, Conntour is making headlines by securing $7 million in seed funding led by General Catalyst, Y Combinator, SV Angel, and Liquid 2 Ventures. The two-year-old company is focused on building an advanced AI search engine for security video systems, empowering users to search camera feeds in real time using natural language queries—much like using Google for video surveillance.
AI-Driven Search for Security Video
Conntour’s platform stands out by allowing security teams to ask complex questions about surveillance footage, such as finding specific events or identifying objects and behaviors. The system leverages vision and language AI models for greater accuracy and flexibility compared to traditional methods, and can even auto-generate incident reports.
Scalable and Efficient Monitoring
Unlike legacy systems limited by preset rules, Conntour’s solution can efficiently handle thousands of video feeds. The technology dynamically selects the most efficient models for each search, enabling the system to monitor up to 50 camera streams on a single consumer GPU like Nvidia’s RTX 4090. The platform supports various deployment models, including fully on-premises, cloud, or hybrid setups, and easily integrates with most existing security systems.
Privacy-First Approach
Conntour’s leadership puts strong emphasis on ethical client selection, ensuring their AI tools are used responsibly and legally. The company has already attracted major customers, including Singapore’s Central Narcotics Bureau, allowing them to be deliberate about their partnerships.
Overcoming Surveillance Challenges
The company also addresses a key challenge in video surveillance—the quality of camera footage. Conntour provides confidence scores for search results, indicating reliability based on the video quality. The team’s ongoing mission is to enhance natural language processing power while maintaining efficient resource use, aiming to reconcile flexibility and scalability for large deployments.
For the original article, visit TechCrunch.
