Physical security teams often rely on manual review of access control logs and static alarm systems, which can miss subtle anomalies and require significant staff time to monitor and investigate. This leads to delayed responses to security threats and limited visibility into overall security posture.

Louhe offers an AI-driven platform that continuously analyzes real-time access control events to learn normal patterns and automatically detect anomalous behavior. Its Detection & Response module classifies security incidents and provides actionable alerts for rapid response, while the Insights component aggregates historical data to reveal movement trends, facility usage, and emerging threats. By turning existing security infrastructure into a data-driven system, Louhe enables 24/7 monitoring, reduces reliance on manual oversight, and supports more efficient security operations.

Real-time machine‑learning analysis of access control events to identify and classify anomalies

Automated alerting with context‑rich recommendations for appropriate response actions

Historical analytics dashboard that visualizes user behavior, perimeter usage, and trend patterns

Integration with existing physical security hardware, extending its capabilities without additional sensors

Continuous learning engine that adapts to evolving security environments and reduces false positives

Primary customers are security operations teams and facility managers in enterprises, campuses, and critical infrastructure sites that operate access control systems and need automated threat detection.