The surveillance technology sector is currently under scrutiny, and not for commendable reasons. Amid controversy regarding the U.S. Immigration and Customs Enforcement’s use of Flock’s camera network for monitoring individuals, as well as home security camera manufacturer Ring facing backlash for developing new functionalities that would allow law enforcement to request footage from homeowners about their surroundings, there is an extensive discussion surrounding safety, privacy, and the dynamics of surveillance.

However, controversy does not negate market opportunities, and the ongoing advancements in vision-language models have further propelled companies that create novel ways to assist organizations in overseeing activities within their facilities.

Matan Goldner, co-founder and CEO of the video surveillance startup Conntour, emphasizes the significance of ethics in this domain, claiming that his company carefully chooses its clientele. While this might not be perceived as typical startup logic for a company that is still in its early stages, Goldner asserts that Conntour is in a position to do so as it already boasts a number of substantial government and publicly traded clients, including Singapore’s Central Narcotics Bureau.

“Our relationship with such prominent clients enables us to be selective and maintain control […] We truly have autonomy over who utilizes our system, the intended applications, and we can determine what we deem to be ethical and lawful. We apply our discretion and assess specific clients that we’re comfortable collaborating with, based on our understanding of their intended use,” Goldner shared with TechCrunch in an exclusive discussion.

This momentum has assisted Conntour in more than just selecting clients. Investors have taken notice: The startup recently secured a $7 million seed funding round from General Catalyst, Y Combinator, SV Angel, and Liquid 2 Ventures.

Goldner noted that the funding round was concluded within a mere 72 hours. “I think I organized around 90 meetings in about eight days, and just after three days — we started on Monday and wrapped up by Wednesday afternoon,” he explained.

Nevertheless, Conntour’s cautious approach seems wise, particularly considering the immense power of AI tools within this sector. The company’s video platform employs AI models to allow security personnel to query camera feeds using natural language to detect any item, individual, or scenario in the footage, in real time — akin to a Google-like search engine tailored for security video feeds. It can autonomously monitor and identify threats based on predefined rules while generating alerts automatically.

In contrast to traditional systems that rely on predefined definitions or parameters to identify specific objects, movement patterns, or behaviors, Conntour asserts that its system leverages natural and vision language models, giving it a significant level of adaptability and user-friendliness. A user can simply request, “Identify instances of someone in sneakers handing over a bag in the lobby,” and Conntour’s system will swiftly comb through all recorded footage or live video feeds to provide pertinent results.

Furthermore, thanks to the integration of AI models, users can effortlessly pose questions regarding the footage and receive textual answers, along with the corresponding video feeds, and even create incident reports.

The key advantage of the company, however, lies in its scalability. Goldner clarified that the platform distinguishes itself from other AI video search solutions by being engineered to efficiently accommodate systems made up of thousands of camera feeds. Notably, he mentioned that Conntour’s system can oversee up to 50 camera feeds using a single consumer GPU, such as Nvidia’s RTX 4090.

The company achieves this by employing a variety of models and logical systems, and then determining which models and systems the algorithm should apply for each query to minimize computational demands while delivering optimal results.

Conntour asserts that its system can be entirely implemented on-site, completely in the cloud, or a combination of both. It can integrate with most existing security infrastructures or function independently as a comprehensive surveillance platform.

Nonetheless, a persistent issue in the video surveillance industry remains: The effectiveness of surveillance is only as good as the quality of the captured footage. For instance, distinguishing details from low-resolution footage taken in a dimly lit parking area with a dirty lens is quite challenging.

Goldner states that Conntour mitigates this challenge by providing a confidence score alongside its search results. If a camera feed’s source is of inadequate quality, the system presents results with low confidence levels.

Looking ahead, Goldner identifies the primary technical challenge as integrating the full extent of LLM capability within the system while retaining efficiency.

“We are focusing on two objectives simultaneously, which tend to contradict each other. On one side, we aspire to offer complete natural language flexibility, LLM-style, allowing for any inquiries. Conversely, we are concerned with efficiency, aiming to utilize minimal resources, as processing [thousands] of feeds is extremely demanding. This conflict represents the most significant technical obstacle we face in our field, and it’s what we are diligently striving to resolve.”