Conntour Raises $7M to Revolutionize AI Video Surveillance

Conntour Raises Funds to Innovate in Video Surveillance

The surveillance technology industry is currently in the spotlight, but not for the best reasons. With the controversy surrounding the U.S. Immigration and Customs Enforcement using the Flock camera network to monitor individuals, and the home camera manufacturer Ring criticized for its new features allowing law enforcement to request recordings from homeowners about their neighborhoods, a broad debate is currently taking place around security, privacy, and who is watching whom.

However, the controversy does not erase the markets, and the continuous improvement of vision-language models has given a new impetus to companies developing new ways to help organizations monitor what is happening on their premises.

A Selective and Ethical Strategy

According to Matan Goldner, co-founder and CEO of the video surveillance startup Conntour, the ethics surrounding this topic are significant enough for him to state that his company is very selective about its clients. This may not seem like good business sense for a startup barely two years old, but Goldner claims he can afford it because Conntour already has several large government and publicly traded clients, including the Central Narcotics Bureau of Singapore.

"The fact that we have such large clients allows us to select them and maintain control [...] We really control who uses our technology, what the use case is, and we can choose what we consider moral and, of course, legal. We use all our judgment and make decisions based on specific clients we agree [to work with] because we know how they will use it," Goldner told TechCrunch in an exclusive interview.

Rapid and Promising Funding

This traction has helped Conntour be more than just selective. Investors have taken note: the startup recently raised $7 million in a seed funding round from General Catalyst, Y Combinator, SV Angel, and Liquid 2 Ventures.

Goldner stated that the round closed in 72 hours. "I think I planned about 90 meetings in eight days, and just after three days — we started on Monday and by Wednesday afternoon, it was done," he added.

Cutting-Edge Technology for Security

In any case, Conntour may be right to be demanding, especially given the power of AI tools in this field. The company's video platform uses AI models to allow security personnel to query camera feeds using natural language to find any object, person, or situation in the recordings, in real-time — a search engine similar to Google, but specifically designed for security video feeds. It can also autonomously monitor and detect threats based on predefined rules and generate alerts automatically.

Unlike legacy systems that rely on definitions or predefined parameters to detect objects, movement patterns, or specific behaviors, Conntour claims that its system uses natural language and vision models, giving it a high degree of flexibility and usability. A user can ask, "Find instances of someone in sneakers passing a bag in the hallway," and Conntour's system will quickly search all recordings or live video feeds to return relevant results.

And because the platform integrates AI models, users can simply ask questions about the recordings and receive answers in text form, accompanied by relevant video feeds, as well as generate incident reports.

Scalability and Integration

The company's strong point, however, is its scalability. Goldner explained that the platform primarily distinguishes itself from other AI video search services because it is designed to scale efficiently to systems comprising thousands of camera feeds. In fact, he stated that Conntour's system can monitor up to 50 camera feeds from a single consumer GPU like the Nvidia RTX 4090.

The company achieves this by using multiple models and logical systems, then identifying which models and systems the algorithm should use for each query to require the least computational power possible while providing users with the best results.

Conntour claims that its system can be deployed entirely on-premises, completely in the cloud, or a mix of both. It can integrate with most existing security systems or serve as a complete standalone monitoring platform.

Challenges and Future Prospects

However, a long-standing issue in the video surveillance industry persists: the quality of surveillance is only as good as the recordings captured. It is difficult to distinguish details from recordings of a poorly lit parking lot captured by a low-resolution camera with a dirty lens, for example.

Goldner asserts that Conntour safeguards against this inevitability by providing a confidence score with its search results. If the source of a camera feed is of poor quality, the system will return results with low confidence levels.

Looking ahead, Goldner states that the biggest technical challenge to solve is bringing the full capability of large language models (LLMs) to its system while maintaining efficiency.

"We have two things we want to do at the same time, and they contradict each other. On one hand, we want to provide complete flexibility in natural language, LLM-style, to allow you to ask any question. On the other hand, there’s efficiency, so we want it to use very few resources, because again, processing [thousands] of feeds is just insane. This contradiction is the biggest technical barrier and the biggest problem in our field, and it’s what we are really, really working hard to solve."