The surveillance tech industry today is in the spotlight, but not for the best reasons. With controversy around the U.S. Immigration and Customs Enforcement tapping into Flock’s camera network to surveil people, and home camera maker Ring drawing criticism for building new features that would enable law enforcement to ask homeowners for footage of their neighborhoods, there’s currently a broad debate around safety, privacy, and who gets to watch whom.
But controversy doesn’t erase markets, and the continued improvement of vision-language models has only blown more wind in the sails of companies building new ways to help companies monitor what goes on in their premises.
According to Matan Goldner, co-founder and CEO of video surveillance startup Conntour, the ethics around this topic are important enough that he says his company is quite picky about which clients to sell to. That may not come off as sound business sense for a startup barely two years in, but Goldner says he can afford to do this because Conntour already has several large government and publicly-listed customers, one of which is Singapore’s Central Narcotics Bureau.
“The fact that we have such big customers allows us to select them and to stay in control […] We’re really in control of who is using it, what is the use case, and we can select what we think is moral and, of course, legal. We use all our judgment, and we make decisions based on specific customers that we’re okay [to work with] because we know how they will use it,” Goldner told TechCrunch in an exclusive interview.
That traction has helped Conntour with more than being selective. Investors have taken note: The startup recently raised a $7 million seed round from General Catalyst, Y Combinator, SV Angel, and Liquid 2 Ventures.
Goldner said the round closed within 72 hours. “I think I scheduled around 90 meetings in like eight days, and just after three days — we started on Monday and by Wednesday afternoon, we were done,” he said.
Regardless, Conntour may be right in being picky, especially given how powerful AI tools in this space have become. The company’s own video 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 — a Google-like search engine made specifically for security video feeds. It can also monitor and detect threats on its own based on preset rules, and surface alerts automatically.
Unlike legacy systems that depend on preset definitions or parameters to detect specific objects, motion patterns or behaviors, Conntour claims its system uses natural and vision language models, which lends it a high degree of flexibility and usability. A user may ask, “Find instances of someone in sneakers passing a bag in the lobby,” and Conntour’s system will quickly search all the recorded footage or live video feeds to return relevant results.
And because the platform bakes in AI models, users can simply ask questions about the footage and get answers in text, accompanied by the relevant video feeds, as well as generate incident reports.
The company’s selling point, however, is its scalability. Goldner explained that the platform mainly differs from other AI video search services because it is designed to efficiently scale to systems comprising thousands of camera feeds. In fact, he said, Conntour’s system can monitor up to 50 camera feeds off a single consumer GPU like Nvidia’s RTX 4090.
The company does this by using multiple models and logic systems, and then identifying which models and systems the algorithm should use for each query to require the lowest amount of computing power to give users the best results.
Conntour claims its system can be deployed fully on premises, completely on the cloud, or a mix of both. It can plug into most security systems already in use, or can serve as a full surveillance platform on its own.
But there’s been a long-running problem in the video surveillance industry: The quality of surveillance is only as good as the footage captured. It’s hard to make out details from the footage of a poorly-lit parking lot that was recorded by a low-resolution camera with a dirty lens, for example.
Goldner says Conntour hedges for this inevitability by providing a confidence score along with its search results. If the source of a camera feed isn’t good enough quality, the system will return results with low confidence levels.
Going forwards, Goldner says the biggest technical problem to solve is bringing the full level of LLM capability to its system while maintaining its efficiency.
“We have two things that we want to do at the same time, and they contradict each other. One one hand, we want to provide full natural language flexibility, LLM-style, to let you ask anything. And on the other hand there’s efficiency, so we want to make it use very few resources, because again, processing [thousands] of feeds is just insane. This contradiction is the biggest technical barrier and technical problem in our space, and what we’re working really, really hard to solve.”
