Build Intelligent Vision Systems That See and Understand

We develop solutions for image classification, object detection and tracking, facial recognition, OCR, medical image analysis, quality inspection, scene understanding, and custom visual analytics. We work with both static images and real-time video streams, deploying models optimized for cloud, edge, or embedded devices based on your latency and cost requirements.

We follow rigorous ML practices including comprehensive data collection and labeling, data augmentation, transfer learning from pre-trained models, extensive validation and testing, and continuous monitoring. We also implement confidence thresholds, human-in-the-loop review for critical decisions, and fallback mechanisms to ensure reliability in production environments.

Yes. We optimize models for edge deployment using techniques like quantization, pruning, and model compression. We deploy to edge devices including NVIDIA Jetson, Intel OpenVINO, Raspberry Pi, and mobile devices. We balance accuracy with latency and resource constraints to meet your real-time processing requirements.

We implement privacy-preserving techniques including on-device processing, data anonymization, secure model deployment, and compliance with regulations like GDPR and HIPAA. For sensitive applications like facial recognition, we use encrypted storage, access controls, and audit logging. We can also design federated learning approaches to train models without centralizing sensitive visual data.

Timelines vary based on complexity: simple image classification (4-8 weeks), object detection systems (8-12 weeks), custom medical imaging or complex multi-model systems (12-20 weeks). Factors include data availability, labeling requirements, model complexity, deployment targets, and integration needs. We provide detailed timelines during discovery and break projects into phases with regular deliverables.