Unlock Live Video Intelligence

In this episode of "Full Tech Ahead," host Amanda Razani interviews Ofir Mulla, CTO of Lumana. They discuss how artificial intelligence is transforming modern video surveillance from passive, reactive recording into proactive visual intelligence. Mulla points out that while businesses spend billions on video cameras, recorded data is typically ignored until a theft or security breach occurs, and human monitors experience visual fatigue after just ten minutes.  Lumana transforms existing camera infrastructure into operational intelligence engines without requiring new hardware or cloud streaming. By deploying compute directly within the client's local network, Lumana preserves absolute data privacy and keeps high-bandwidth video on-premise, transmitting only lightweight metadata.  Utilizing camera-specific **self-supervised learning**, Lumana's AI agents dynamically adapt to local environments (differentiating a sunny beach from a parking lot), automatically baseline normal versus anomalous activities, and deliver actionable operational insights—such as tracking lost children across smart cities, verifying workplace compliance, and optimizing peak-hour staffing. **Key Quotes** - "We are coming to businesses that invested billions of dollars on their cameras, and we are making the information from the cameras meaningful information that they never had." - "Even when cameras were connected to control centers where there were people watching the cameras, there were researchers that showed after ten minutes, they got blind." - "All we need is you to give us the permission to put our compute in your network... keeping the data on your site, meaning that we are keeping privacy. You don't need to upload a lot of video data to the cloud." - "Lumana is the only solution that is pushing a unique model, a unique agent per camera with self-supervised learning... teach the camera based on what it sees." **Takeaways** - **Shift from Reactive Forensics to Proactive Insights:** Traditional surveillance only reviews footage post-incident. Deploying dedicated AI agents across camera feeds enables real-time anomaly detection and operational optimization (e.g., floor-cleaning compliance, queue management, and service velocity) without relying on fatigued human monitors. - **Preserve Privacy via Local Hybrid Compute:** Enterprise video feeds contain highly sensitive visual data that companies hesitate to upload to public clouds. By running AI inference and computer vision pipelines locally within the customer's on-premise network or data center, organizations retain full data sovereignty and only push encrypted metadata to the cloud. - **Leverage Per-Camera Self-Supervised Learning:** Generic, one-size-fits-all computer vision models generate high false-alarm rates because conditions vary drastically by angle, lighting, season, and geography. Training an independent, self-supervised model per camera allows the system to continuously baseline "normal" behavior and automatically flag genuine operational drifts without human labeling. - **Evaluate Adaptive Scalability Over Basic Motion Alerts:** Simple motion alerts or person-detection features are commoditized. When selecting an intelligent video platform, business leaders should evaluate how easily the system adapts to custom operational workflows (such as store curbside pickups or bank queue latency) without requiring weeks of custom model retraining. Find Amanda Razani on LinkedIn.  https://www.linkedin.com/in/amanda-razani-990a7233/ Follow the FTA LinkedIn Page: https://www.linkedin.com/company/full-tech-ahead/ Visit the FTA website: https://fulltechahead.com/ Check out the Substack Channel: https://fulltechahead.substack.com/