At the forefront of Industry 4.0 Enables Manufacturers to Revolutionize Operations by Integrating
Cutting-Edge Artificial Intelligence and Advanced Simulations
We leverage industry leading tools like NVIDIA Omniverse to deliver physics-accurate virtual testing, sensor simulation, and synthetic data generation. Our expertise helps manufacturers overcome complex challenges in product inspection, sustainable recycling practices, intelligent material classification, and robotic automation. By utilizing multi-stage intelligent frameworks for automated defect detection and autonomous AI-powered robots for precise tasks like RFID scanning, we drive significant improvements in efficiency, agility, and sustainability. Our solutions are designed to streamline manufacturing processes, accelerate deployment, reduce operational costs, and significantly shorten time-to-market, ensuring manufacturers stay competitive in an ever-evolving market.
10M+ Data Points Processed Daily
AI-driven analytics provide real-time production insights, enabling better capacity planning, supply chain visibility, and optimized equipment performance.
99% Defect Detection Accuracy
Advanced computer vision and machine learning identify quality issues at scale, drastically reducing rework, scrap, and customer returns.
35% Reduction in Unplanned Downtime
Predictive maintenance leverages sensor data and anomaly detection to preempt equipment failures, improving asset utilization and reliability.
2× Faster Time to Market
Simulation-based testing and agile prototyping via NVIDIA Omniverse and AI workflows accelerate product development, shrinking design-validation cycles
Multi-Modal Insights for Production & Supply Chain
Unified data models combine sensor, robotic, and ERP information to deliver actionable intelligence, fueling continuous improvement in manufacturing..
Enterprise-Ready AI Scaling Across Global Plants
A secure, distributed, and modular AI infrastructure ensures seamless deployment, standardizing best practices worldwide.
40% Reduction in Overhead
Automated inspections, robotic workflows, and data-driven process optimization cut labor costs and improve operational efficiency.
Multi-Stage Agentic Framework powered by VLM and Knowledge Graphs for Product’s Damage Assessment and Recycling
Automated Sorting with Vision Enabled Robotic Arm
Physics Accurate Synthetic Data Generation using NVIDIA Omniverse for Training Physical AI Models
Predictive Maintenance
Autonomous Intelligent Robots for RFID Scanning with SLAM-Based Navigation
Simulating Sensor-Based Systems with NVIDIA Omniverse for Material Classification Using AI
Case Studies
Case Study
Multi-Stage Agentic Framework powered by VLM and Knowledge Graphs for Product’s Damage Assessment and Recycling
Our Multi-Stage Agentic framework introduces product damage assessment and recycling by integrating Vision-Language Models (VLMs), Knowledge Graphs, 3D design simulations, and Large Language Models (LLMs) into an intelligent, decision-making pipeline. Our framework enables defect detection, strategic disassembly, part recognition via AI vision, and structured reasoning with knowledge graphs. Our AI solution enables rapid, high-accuracy assessment of repair feasibility and sustainability impact. This AI-driven solution optimizes inspection for OEMs, speeds up decision-making, and provides cost-effective recycling decisions—enhancing efficiency, accuracy, and sustainability for manufacturers and recyclers.
Case Study
Automated Sorting with Vision Enabled Robotic Arm
Our Intelligent Robotic Sorting System integrates Omniverse Isaac Sim, ROS2, and NVIDIA GPU-accelerated perception for real-time, high-precision automation in manufacturing. Using YOLO-based object detection, synthetic data, and physics-accurate simulation, it minimizes sorting errors while enhancing speed by 30% in comparison with 3D-Sim inference time. With depth-based 3D pose estimation and adaptive grasping, this solution reduces factory sorting waste, automates enhancements, simplifies defects, transforms manual sorting into an intelligent, scalable automation solution.
Case Study
Physics Accurate Synthetic Data Generation using NVIDIA Omniverse for Training Physical AI Models
Our Physics-Accurate Synthetic Data Generation system, powered by NVIDIA Omniverse, aids in training AI models for industrial and physical AI applications by creating high-fidelity synthetic data simulations. All data is physics-grounded, simulating interactions with sensors, RGB images, depth maps, LiDAR scans, and object segmentations. This makes AI-based systems more robust for real-world environments, reducing failure rates and optimizing AI decision-making to really accelerate faster, more accurate AI-driven automation for manufacturing and robotics.