Surveillance Tech at a Crossroads
The surveillance industry faces intense scrutiny. Recent controversies involving ICE accessing Flock’s camera network and Ring developing features for police requests have sparked heated debates about privacy, safety, and oversight. Yet market demand persists, driven by rapid advancements in vision-language AI models.
Companies continue developing tools to help organizations monitor their premises. The ethical dimension, however, has become impossible to ignore.
A Startup With Selective Ethics
Matan Goldner, Conntour’s co-founder and CEO, emphasizes that ethics guide his company’s client selection. For a startup barely two years old, turning away business might seem risky. Goldner argues their existing customer base provides that luxury.
“Having large customers allows us to stay in control,” Goldner told TechCrunch. “We select who uses it and for what purpose. We apply our judgment to ensure use is both moral and legal.” Current clients include Singapore’s Central Narcotics Bureau and other major government and publicly-listed entities.
This principled stance hasn’t scared off investors. Conntour recently closed a $7 million seed round led by General Catalyst and Y Combinator, with participation from SV Angel and Liquid 2 Ventures. The funding round wrapped up in just 72 hours.
“We scheduled about 90 meetings in eight days,” Goldner recalled. “We started on Monday and were done by Wednesday afternoon.”
How Conntour’s AI Search Engine Works
Conntour’s platform transforms security video monitoring. Instead of relying on preset motion detectors or object definitions, it uses natural language queries. Security personnel can ask questions like “Find instances of someone in sneakers passing a bag in the lobby.”
The system scans live or recorded footage across thousands of camera feeds, returning relevant video clips with timestamps. It functions like a Google search engine specifically for surveillance video.
Beyond search, the platform monitors feeds autonomously based on configured rules, surfacing alerts automatically. It can generate incident reports and answer questions about footage in text, accompanied by the relevant video evidence.
Technical Scalability and Efficiency
Goldner highlights scalability as Conntour’s key differentiator. The system is engineered to handle massive deployments efficiently. A single consumer-grade GPU, like an Nvidia RTX 4090, can process up to 50 camera feeds simultaneously.
“Other AI video search services exist,” Goldner explained, “but they aren’t built for systems with thousands of feeds.” Conntour achieves this by employing multiple AI models and logic systems. Its algorithm intelligently selects the most efficient model for each query, minimizing computational load while delivering accurate results.
The platform offers flexible deployment: fully on-premises, completely cloud-based, or a hybrid model. It integrates with most existing security systems or can operate as a standalone surveillance platform.
Overcoming Industry Challenges
Video surveillance has a persistent weak link: garbage in, garbage out. A blurry, poorly-lit feed from a dirty camera lens yields useless data, regardless of sophisticated AI.
Conntour addresses this by providing a confidence score with every search result. If camera quality is subpar, the system indicates low confidence in its findings, alerting users to potential inaccuracies.
Looking ahead, Goldner identifies a core technical challenge. “We face a contradiction,” he said. “We want full natural language flexibility—let users ask anything, LLM-style. Simultaneously, we need extreme efficiency to process thousands of feeds without insane resource demands. Solving this contradiction is our biggest technical barrier.”
The $7 million in new capital will fuel that effort, pushing the boundaries of what’s possible in AI-powered video security while navigating the complex ethical landscape that defines the industry.