At Yveloxy, we build Computer Vision solutions that give machines the ability to interpret and act on visual data — images, video feeds, documents, and live camera streams. From real-time object detection and facial recognition to medical image analysis and quality control automation, our CV systems deliver superhuman accuracy at scale.
We combine state-of-the-art deep learning architectures (YOLO, ResNet, Vision Transformers) with robust engineering to deploy vision AI models that work reliably in production environments — on cloud, edge devices, or embedded systems.
Deep visual AI expertise across detection, recognition, segmentation, generation, and real-time video intelligence.
Real-time detection, localization, and tracking of objects across image and video streams using YOLO, Faster R-CNN, and custom architectures — from single objects to hundreds in a single frame.
High-accuracy face detection, identity verification, liveness detection, emotion analysis, and age/gender estimation — built with privacy-first design and anti-spoofing safeguards for secure deployments.
Semantic and instance segmentation that labels every pixel in an image — enabling precise background removal, medical organ segmentation, autonomous driving scene understanding, and satellite image analysis.
AI-powered document processing — extracting text, tables, signatures, stamps, and structured data from invoices, IDs, forms, and handwritten documents with high accuracy across languages and layouts.
Real-time video intelligence for crowd counting, anomaly detection, intrusion alerts, behavior analysis, and activity recognition — transforming passive camera systems into intelligent monitoring solutions.
A proven, data-driven process that takes your visual AI project from raw data to production-ready model — with measurable accuracy at every stage.
We define the exact visual task, success metrics (accuracy, precision, recall, FPS), data requirements, deployment environment (cloud/edge/embedded), and integration points before any data collection or model work begins.
We source, collect, and annotate training data — bounding boxes, segmentation masks, keypoints, or classification labels — using our in-house annotation pipeline with rigorous quality checks to ensure label accuracy.
We select or design the optimal model architecture for your task, train on your data using transfer learning and custom fine-tuning, and apply augmentation, regularization, and hyperparameter optimization to maximize accuracy.
Rigorous benchmarking against test sets, edge cases, and adversarial examples. We optimize for inference speed using quantization, pruning, and ONNX/TensorRT conversion to hit your latency and accuracy targets simultaneously.
Wrapping the model in production-ready REST APIs or gRPC services, integrating with your existing systems (cameras, ERPs, mobile apps, web platforms), and building intuitive dashboards for results visualization and monitoring.
Cloud deployment (AWS, GCP, Azure) or edge deployment (Jetson, Raspberry Pi, embedded cameras), continuous performance monitoring, drift detection, and model retraining pipelines to keep accuracy high as real-world conditions evolve.
Transforming industries with visual AI — from factory floors and hospital diagnostics to retail shelves and smart city infrastructure.
Automated visual inspection systems that detect product defects, measure dimensions, verify assembly correctness, and flag anomalies on production lines — 24/7, with sub-millimeter precision that outperforms human inspectors.
AI-powered analysis of X-rays, MRIs, CT scans, pathology slides, and retinal images — detecting tumors, fractures, lesions, and abnormalities with radiologist-level accuracy to assist clinicians and speed up diagnosis.
Intelligent retail solutions — automated checkout with product recognition, shelf stock monitoring, customer traffic heatmaps, planogram compliance verification, and age-gated purchase systems powered by visual AI.
Intelligent video surveillance with real-time intrusion detection, unauthorized access alerts, crowd density monitoring, license plate recognition, and behavior anomaly detection — far beyond what traditional CCTV systems offer.
Drone and satellite imagery analysis for crop health monitoring, disease and pest detection, yield estimation, irrigation optimization, and field mapping — enabling data-driven decisions that maximize farm productivity.
Advanced driver assistance systems (ADAS), parking management, traffic flow analysis, vehicle damage assessment for insurance, and autonomous navigation components — visual AI that powers the future of mobility.
Deep computer vision expertise, battle-tested in production environments across industries — delivering accuracy, speed, and reliability that others can't match.
We use the latest deep learning architectures — Vision Transformers, YOLO v8/v9, SAM, and custom models — achieving benchmark-level accuracy on your specific dataset and deployment conditions.
Our models are optimized for speed — TensorRT, ONNX, quantization, and pruning techniques deliver real-time inference at 30-120 FPS on GPU hardware and 15-30 FPS on edge devices without sacrificing accuracy.
We deploy CV models wherever you need them — AWS/GCP/Azure cloud, NVIDIA Jetson edge devices, Raspberry Pi, industrial cameras, and embedded systems — with the same pipeline working seamlessly across environments.
We handle everything from data collection and annotation to model training and deployment. Our in-house annotation team produces high-quality labeled datasets with rigorous QA, so your model trains on clean, accurate data.
Whether processing 100 images per day or 10 million frames per hour, our architecture scales horizontally with auto-scaling inference clusters — handling peak loads without performance degradation or cost overruns.
GDPR-compliant facial recognition, on-device processing for sensitive data, anonymization pipelines, bias testing, and explainability tools — we build visual AI systems that are responsible, fair, and legally compliant.
REST APIs, WebSocket streams, SDKs for Python/JavaScript/Java, and pre-built connectors for common platforms — our CV solutions integrate cleanly with your existing software stack, cameras, and infrastructure.
Production models degrade as real-world conditions shift. Our MLOps pipelines monitor model performance, detect data drift, automatically trigger retraining on new data, and deploy updates — keeping accuracy high over time.
Everything you need to know about our Computer Vision services — answered clearly.
It depends on the task complexity and how visually varied your objects are. A simple binary classification task might need as few as 200–500 images per class. Object detection typically requires 1,000–5,000 annotated images to start. Complex segmentation or multi-class detection in challenging conditions may require 10,000+ images. We can often dramatically reduce data requirements through transfer learning from pre-trained models, synthetic data generation, and data augmentation strategies — we'll give you a realistic estimate during the discovery session.
Yes. We specialize in optimizing models for real-time inference on edge hardware. Using TensorRT, ONNX Runtime, and model quantization techniques, we can achieve 30+ FPS on NVIDIA Jetson devices, and 10–20 FPS on lower-power hardware like Raspberry Pi 4 with hardware acceleration. For GPU server deployments, we routinely achieve 100–500 FPS. We'll profile the target hardware during the project scoping phase and commit to specific performance targets before development begins.
No problem. We offer end-to-end data services including data collection, annotation, and quality assurance. Our annotation team can label bounding boxes, segmentation masks, keypoints, and classification labels at scale using industry-standard tools. We also use semi-supervised learning, active learning, and synthetic data generation (using 3D rendering or GANs) to minimize annotation effort while maximizing model performance. Starting a project without existing labeled data is completely normal for us.
Accuracy depends on task complexity, data quality, and deployment conditions. For well-defined tasks with good-quality data, we typically achieve 95–99%+ accuracy on test sets. For harder tasks (small objects, cluttered scenes, variable lighting), we set realistic targets during scoping and continuously improve through iterative training. We always provide honest accuracy benchmarks on a held-out test set that reflects your real production environment — not cherry-picked results. We also measure precision, recall, F1-score, and latency, not just overall accuracy.
Yes, with the right approach. We handle challenging conditions through specialized training data that includes the specific variations you'll encounter (night, rain, fog, motion blur, partial occlusion), data augmentation during training, and sometimes hardware solutions like infrared cameras or structured light. We always test models against the actual environmental conditions of your deployment — not just ideal lab conditions — before signing off on performance commitments.
Facial recognition is legal in many contexts but subject to strict regulations in others (GDPR in Europe, BIPA in Illinois, etc.). We build facial recognition systems with privacy-first design: explicit consent mechanisms, data minimization, secure encrypted storage, audit trails, and the ability to delete individual records on request. We conduct compliance reviews for every facial recognition project and can implement privacy-preserving techniques like on-device processing (data never leaves the camera) for the most sensitive deployments. We'll advise on the legal framework applicable to your use case and geography.
We provide multiple integration options depending on your needs. REST APIs let any system send images or video frames and receive structured JSON results. WebSocket connections enable real-time streaming for live video analysis. SDKs are available for Python, JavaScript, Java, and C++. For camera systems, we support ONVIF, RTSP, and direct SDK integrations with major camera manufacturers. We also build custom dashboards, alert systems, and ERP/MES connectors so the CV results flow seamlessly into your existing business workflows.
Click "Schedule a Demo" to book a free 30-minute discovery call. Bring any sample images or video clips of what you want the system to detect or analyze — even a handful of examples helps us give you a much more accurate scoping estimate. We'll discuss your use case, data availability, accuracy requirements, deployment environment, and timeline, then provide a detailed proposal within 48 hours. Many of our best CV projects started with a client who just had a problem and a few sample images.
Let's build a Computer Vision solution that transforms how your business sees, analyzes, and acts on visual data.