MokSa.AI

Monitors live CCTV feeds to identify suspicious activities, unusual behavior patterns, and potential security threats in real-time. Automatically flags anomalies without requiring manual surveillance review.

Automatically detects and flags potential loss prevention incidents such as shoplifting, employee theft, and organized retail crime. Creates incident records with video evidence for investigation and prosecution.

Provides data-driven recommendations for store layout improvements based on customer traffic patterns, dwell times, and product interaction data. Helps optimize merchandise placement and store design.

Automatically reconstructs detailed timelines of incidents by analyzing video footage and extracting key events, movements, and interactions. Creates comprehensive incident narratives for investigation and documentation.

Analyzes video footage to create visual heatmaps showing customer movement patterns, dwell times, and traffic flow throughout the store. Identifies high-traffic zones and areas where customers spend the most time.

Measures how long customers spend in specific areas of the store, at product displays, or in checkout zones. Provides insights into customer engagement levels and product interest.

Analyzes customer traffic patterns and queue lengths to recommend optimal staffing levels for different times and locations. Helps match staff availability to customer demand.

Centralizes video monitoring and incident detection across multiple retail locations into a single dashboard. Enables security teams to monitor and manage all stores simultaneously.

Automatically extracts, clips, and organizes relevant video segments from surveillance footage based on detected incidents. Creates searchable evidence libraries for investigations and legal proceedings.

Monitors checkout areas and customer queues to identify bottlenecks, measure wait times, and track checkout efficiency. Alerts staff when queues exceed thresholds.

Monitors employee activities on the sales floor to ensure compliance with company policies, proper handling of merchandise, and adherence to operational procedures.

Identifies patterns of organized retail crime by analyzing multiple incidents across locations and time periods. Detects repeat offenders, organized groups, and coordinated theft activities.