Optimized Segmentation Model for Garbage Detection
We built an optimized segmentation system to detect and classify different types of waste in real time. By enhancing model speed and accuracy through hybrid CPU-GPU inference, the solution enables faster sorting, smarter recycling, and sustainable automation across waste-management facilities.
No-Code Computer Vision Platform
We built a no-code computer vision platform that enables users to create, train, and deploy custom AI models without writing a single line of code. The system simplifies the entire computer vision workflow from data upload and model selection to training, testing, and deployment all through a user-friendly interface designed for non-technical teams.
Avatar Video Generation with Realistic Lip Synchronization
We developed a real-time avatar system that turns any user image into a lifelike, talking digital avatar capable of natural, audio-synced interaction. The system listens, processes speech, and responds with synchronized lip movements and voice output enabling fully automated, human-like communication for businesses, educators, and creators.
Smart Greenhouse Monitoring and Automation System
We developed a computer vision and AI-based greenhouse monitoring system that tracks plant health, environmental conditions, and resource usage in real time. The solution integrates sensor data, image analysis, and automation workflows to optimize temperature, humidity, and irrigation , ensuring better yield and resource efficiency in controlled agricultural environments.
Basketball Video Analytics and Player Tracking System
We developed a computer vision based basketball analytics platform that automatically tracks players, the ball, and in-game events directly from match footage with no need for sensors or manual tagging.The system gives coaches and analysts real-time insights into player positioning, movement, and performance, helping teams make faster, data-driven decisions and optimize their strategy effectively.
AI-Based Safety Monitoring System
We developed an AI-powered industrial safety monitoring system that automatically detects unsafe behavior, PPE violations, and potential hazards in real time. By combining computer vision, deep learning, and Edge AI, the system continuously analyzes live video feeds to deliver instant alerts, enhance safety compliance, and prevent workplace incidents across factories, warehouses, and construction sites.
Accelerating Civil Plan Approvals with AI-Based Drawing Interpretation
We solved one of the most time-consuming challenges faced by government planning authorities, manually reviewing and approving thousands of civil and architectural site plans using AI and computer vision. The system analyses complex drawings, extracts essential details, and automates approval checks, accelerating infrastructure development with accuracy and consistency.
Intelligent Surveillance with LiDAR-Based Motion Tracking
We developed an Edge AI surveillance system that uses LiDAR and depth sensors to record only meaningful activity, minimizing bandwidth and storage needs. By integrating computer vision and depth analysis, the system enables accurate, efficient, and privacy-conscious monitoring for smart infrastructure and industrial environments.
Personalized AI Meal Planning for Chronic Care Patients
We addressed one of healthcare’s most complex challenges , designing personalized, doctor-compliant meal plans for patients managing chronic conditions such as diabetes, hypertension, and kidney disorders. Our AI system simplifies nutrition management by turning medical guidelines and pantry ingredients into daily, condition-safe recipes that are practical, personalized, and doctor-approved.
Traffic Monitoring and Violation Detection Using Edge AI
We developed an AI-powered traffic monitoring system to help government agencies and smart-city departments detect violations, analyze flow, and ensure road safety in real time. By combining computer vision, Edge AI, and custom-trained models, the system delivers actionable traffic insights directly from on-site cameras.
