Conntour, a company developing an artificial intelligence platform for real-time video intelligence analysis, has raised $7 million in seed funding. The round was led by General Catalyst with participation from Y Combinator, SV Angel, Liquid 2 Ventures, and other investors.
Conntour allows security teams to query cameras using natural language to find any object, person, or situation without relying on preset categories or preprogrammed rules. For example, users can search for “a man with a tattoo on his left arm” or “a van with a fruit pattern on it.”
The company was founded in 2024 by Mattan Goldner and Tomer Colla, computer vision experts with experience in video analytics and technology companies. Conntour employs 14 people in its Tel Aviv office and participated in the first cycle of Palantir’s Startup Fellowship program. The company’s idea came from working with IDF field observers during their reserve duty after October 7th. At the time, both founders were in active combat reserve units.
“Traditional video surveillance requires operators to define exactly what they are looking for before they know what they need to find,” said Matan Goldner, CEO of Conntour. “Existing solutions can only detect a predefined set of parameters, such as a weapon or car make. But what if you need to identify someone passing a bag to another person or a guy wearing a Nike shirt? Real-world security doesn’t work in neat categories. Our platform provides search engine-level intelligence to any camera network, so security teams can respond to threats in minutes instead of days to develop and investigate incidents.”
Conntour’s platform is designed to address that constraint by applying computer vision algorithms that can interpret complex, context-driven queries. The system can operate both in real-time, alerting you to potential threats as they occur, and retrospectively, allowing investigators to search through large amounts of archived footage.
The company said the technology has already been deployed in homeland security operations in Singapore, with early indications for use in high-stakes environments such as border control, critical infrastructure, and large public facilities.
The company claims its approach can significantly reduce the time and effort required to monitor and review footage. Metrics include the ability for a single operator to simultaneously monitor thousands of cameras and analyze large amounts of recorded video in minutes, reducing both false alarms and missed events. If realized, these improvements could change the economics of mass surveillance, where manual reviews remain costly and time-consuming.
