Conntour turns security cameras into searchable archives with natural-language AI
Conntour announced a $7 million raise from General Catalyst and Y Combinator to commercialize an AI-powered search engine for security video systems. The startup uses modern vision and language models so security teams can simply ask questions in plain English — for example, "show me the person in a red jacket who entered Gate B between 2–3pm" — and instantly surface relevant clips across camera feeds.
The core technology combines object and person detection with situational understanding, enabling queries for objects, behaviors, or specific people. That means security operators no longer need to scrub through hours of footage manually; instead they can retrieve targeted clips fast, freeing teams to focus on analysis and response rather than tedious review.
Real-world benefits include:
- Faster investigations and incident response by finding relevant footage in seconds.
- Reduced labor for manual video review, lowering operational costs for security teams.
- Scalability across many cameras and long retention windows through model-driven indexing.
With this new funding, Conntour can accelerate product development, integrations with existing VMS and cloud platforms, and customer deployments. For organizations managing large fleets of cameras, natural-language search is a practical win: it turns passive video archives into proactive, searchable intelligence that helps teams act faster and more effectively.