Conntour Raises $7M to Build an AI Search Engine for Security Cameras

Tel Aviv: Conntour has raised $7 million in seed funding to build an AI-powered search engine for enterprise security camera systems. The round was led by General Catalyst and Y Combinator, with participation from SV Angel and Liquid 2 Ventures.

Founded less than two years ago, Conntour lets security teams query live and recorded camera feeds using natural language. Instead of scrubbing through hours of footage manually, a user can ask the system to find a specific person, object, or situation across an entire camera network in real time. The company describes it as a Google-like search engine built specifically for security video.

Unlike legacy surveillance systems that rely on preset rules and fixed parameters, Conntour uses vision-language models to give its platform flexibility. A security team can describe what they are looking for in plain language and the system surfaces relevant footage automatically. It can also monitor feeds autonomously and trigger alerts based on preset conditions.

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The startup already counts several large government and publicly listed enterprise customers. One of the most notable is Singapore’s Central Narcotics Bureau. That traction helped close the round quickly, the entire raise was completed within 72 hours of Conntour beginning investor meetings.

Conntour’s current client base spans use cases the team considers verifiable and defensible.

The $7M will fund product development, team expansion, and customer growth. Conntour targets enterprise security operations across government agencies and large corporates. The company is headquartered in Tel Aviv with operations extending across global enterprise markets.

The trend reflects a wider shift where AI is being applied beyond surveillance into areas like AI-powered privacy protection for enterprise, employee risk management and AI conversation intelligence.

Vision-language models have rapidly expanded what is possible in the surveillance space. Conntour is betting that natural language search is the interface that finally makes large-scale camera networks practically usable for security teams.