Service Overview
We build and deploy Vision AI systems using the same Apar Drishti engine that powers our own division. From fire detection with VLM scene reasoning (FireIQ) and safety compliance to real-time surveillance and drone-led monitoring, every solution starts from models and pipelines already proven in production — not academic experiments. Our defence-grade AI discipline means each system is built for reliability in real operating environments: factories, construction sites, smart cities, and critical infrastructure.
Key Benefits
12+ Vision AI prototypes already deployed
Apar Drishti engine — proven models, not lab experiments
FireIQ fire detection running in real production
Defence-grade AI discipline for critical environments
Edge-optimized models for real-time on-site processing
Automated compliance documentation with visual evidence
Works with existing IP camera infrastructure
Custom-trained on your specific environment and objects
Key Features & Capabilities
Our comprehensive solution includes these powerful features designed to maximize value and performance.
CSPNet-based detection models optimized for your specific objects and environments. Multi-class detection running at production frame rates with configurable confidence thresholds.
DeepSORT and ByteTracker implementations that maintain object identity across frames, handle occlusions, and track hundreds of objects simultaneously with re-identification.
Automated detection of safety violations — missing PPE, restricted zone intrusion, fire and smoke — with real-time alerts and visual evidence capture for compliance documentation.
Person and vehicle detection, perimeter monitoring, behavior analysis, and automated alert systems that turn passive camera feeds into actionable security intelligence.
Models optimized for edge devices (NVIDIA Jetson, Coral TPU) for low-latency on-site processing, with cloud infrastructure for centralized monitoring, analytics, and model updates.
Aerial imagery processing from drone feeds for infrastructure inspection, agricultural monitoring, large-area surveillance, and mapping applications.
Use Cases
Discover how organizations are leveraging this solution to address specific business challenges.
Fire & Smoke Detection
Real-time fire and smoke detection using our FireIQ system — already deployed and production-tested. Alerts in under 30 seconds with visual evidence capture and location pinning.
Manufacturing Quality Control
Automated defect detection, dimensional measurement, and assembly verification on production lines with rejection/acceptance automation and statistical process control.
Construction Site Safety
Detect missing helmets, restricted zone intrusion, unsafe behavior, and equipment proximity violations with real-time alerts sent directly to site supervisors.
Smart City Surveillance
Traffic monitoring, crowd analysis, vehicle classification, and anomaly detection across urban camera networks with centralized dashboards and automated reporting.
Agricultural Monitoring
Drone-based crop health assessment, pest detection, and yield estimation using aerial imagery processed by custom-trained detection models.
Infrastructure Inspection
Automated inspection of bridges, pipelines, power lines, and buildings using drone footage and edge-deployed detection models for predictive maintenance.
Implementation Process
Our structured approach ensures efficient delivery and exceptional results.
Use-Case Discovery & Feasibility
We assess your camera infrastructure, environmental conditions, and detection requirements to determine what's achievable and design the right architecture for your deployment.
Data Collection & Model Training
We gather training data from your actual environment, annotate it, and train or fine-tune detection models on your specific objects, lighting conditions, and accuracy targets.
Edge Deployment & Integration
Optimized models deploy on your edge hardware or cloud, connect to your camera feeds, and integrate with existing alert systems, dashboards, and operational workflows.
Monitoring & Continuous Improvement
Go live with performance tracking. We monitor accuracy, handle edge cases through active learning, and retrain models as your environment evolves or new detection needs arise.
Frequently Asked Questions
Get answers to common questions about this service.
Related Services
Explore additional services that complement this solution.