This project addresses the challenge of understanding customer movement and behaviour in physical retail spaces. Using ceiling-mounted cameras and edge computing devices, the system processes video feeds in real-time to track anonymous customer paths, measure dwell times, and identify high-traffic areas.

The solution employs YOLOv5 for person detection and DeepSORT for tracking across multiple camera views. Heat maps and flow diagrams are generated hourly, providing store managers with actionable data for product placement and staffing decisions.

Legal professionals spend considerable time reviewing lengthy contracts for specific clauses and potential issues. This tool automates initial document review by extracting key sections, identifying unusual terms, and flagging clauses that deviate from standard templates.

The system uses BERT models fine-tuned on 5,000 commercial contracts to understand legal language context. It generates structured summaries highlighting payment terms, liability clauses, termination conditions, and other critical elements, reducing initial review time by 65%.

Manufacturing equipment failures cause costly production delays. This platform analyses sensor data from motors, bearings, and hydraulic systems to predict component failures before they occur, allowing maintenance teams to schedule repairs during planned downtime.

The system collects vibration, temperature, and pressure readings every 30 seconds. Machine learning models identify patterns that precede failures, providing 48-72 hour advance warnings. Dashboard visualisations show equipment health scores and maintenance recommendations.

Early detection of crop diseases and nutrient deficiencies is crucial for yield optimisation. This drone-based system captures high-resolution images of fields and uses computer vision to identify problem areas before they become visible to the naked eye.

Convolutional neural networks trained on 50,000 plant images recognise 15 common crop diseases and 8 nutrient deficiency patterns. The system generates field maps showing affected areas, enabling targeted treatment rather than blanket pesticide application.

E-commerce platforms receive thousands of customer enquiries daily about orders, returns, and product specifications. This chatbot handles routine questions automatically, freeing human agents to address complex issues requiring personal attention.

The assistant uses transformer-based language models fine-tuned on 2 years of customer service transcripts. It understands context, handles multi-turn conversations, and seamlessly transfers to human agents when necessary. Integration with order management systems allows real-time status updates.

Commercial buildings often waste energy through inefficient HVAC operation. This system predicts hourly energy demand based on weather forecasts, occupancy schedules, and historical patterns, automatically adjusting heating and cooling to minimise consumption while maintaining comfort.

LSTM neural networks learn seasonal patterns and respond to real-time conditions. The system pre-cools or pre-heats spaces before occupancy, reduces output during low-occupancy periods, and optimises based on electricity pricing. Building managers receive weekly reports showing savings and system performance.