Video surveillance startup Conntour has raised a $7 million seed round from General Catalyst, Y Combinator, SV Angel, and Liquid 2 Ventures. The company is building what can best be described as a Google-like search engine specifically designed for security camera footage — allowing users to search thousands of live and recorded video feeds using simple natural language queries.
The funding round closed remarkably fast. Co-founder and CEO Matan Goldner said the round closed within just 72 hours. He scheduled around 90 investor meetings in eight days, and by Wednesday afternoon of the first week, the deal was done.
How It Works
Conntour's platform uses AI models to let security personnel query camera feeds using natural language to find any object, person, or situation in the footage in real time. Instead of relying on traditional surveillance tools that require preset parameters to detect specific objects or motion patterns, Conntour's system uses natural and vision language models for a much more flexible experience.
For example, a user could ask something like "find instances of someone in sneakers passing a bag in the lobby," and the system will quickly search all recorded footage or live feeds to return relevant results. Users can also ask questions about the footage and receive text-based answers accompanied by the relevant video clips, as well as generate incident reports automatically.
The platform can also monitor and detect threats on its own based on preset rules and surface alerts automatically, making it useful not just for investigation but for proactive security monitoring.
Scalability Is the Key Differentiator
Many AI video analysis tools exist, but Conntour says its core advantage lies in scalability. Goldner explained that the platform is designed to efficiently scale to systems with thousands of camera feeds. In fact, the system can monitor up to 50 camera feeds from a single consumer GPU like Nvidia's RTX 4090.
The company achieves this efficiency by using multiple models and logic systems, then intelligently identifying which models and systems the algorithm should apply to each query in order to minimize computing power while delivering the best results.
Conntour says its system can be deployed fully on premises, entirely in the cloud, or as a hybrid of both. It can plug into most existing security systems or serve as a complete surveillance platform on its own.
Dealing With Poor Footage Quality
One of the oldest challenges in surveillance is that camera quality varies wildly. A dimly lit parking lot recorded by a low-resolution camera with a dirty lens will naturally produce unreliable results from any AI system. Goldner says Conntour addresses this by providing a confidence score alongside its search results. If the source footage is not good enough quality, the system will return results with low confidence levels, letting security teams know to treat those findings with appropriate caution.
Ethics and Client Selection
The surveillance technology industry is currently facing intense scrutiny. Controversies around US Immigration and Customs Enforcement tapping into Flock's camera network to surveil people, and home camera maker Ring drawing criticism for building features that would let law enforcement request neighborhood footage from homeowners, have sparked a broad debate about safety, privacy, and who gets to watch whom.
Goldner says the ethics around this are important enough that his company is quite selective about which clients it works with. He can afford to be choosy because Conntour already has several large government and publicly listed customers, including Singapore's Central Narcotics Bureau.
He told TechCrunch that having such major clients allows the company to stay in control of who uses the technology, what the use case is, and whether it aligns with what they consider moral and legal.
The Technical Challenge Ahead
Looking forward, the biggest hurdle for Conntour is a fundamental contradiction at the heart of its product. Goldner explained that the company wants to provide full natural language flexibility — letting users ask anything — while simultaneously keeping the system extremely efficient, because processing thousands of feeds requires minimal resource usage. He called this contradiction the biggest technical barrier in their space and the problem they are working hardest to solve.
With $7 million in fresh funding and a growing list of high-profile clients, Conntour is well-positioned to tackle that challenge head-on — and to reshape how organizations think about security video intelligence.
