Enterprise-grade platform to build, train, deploy, and monitor production-ready vision AI applications. From annotation to inference, everything you need in one unified ecosystem.
Our platform powers vision AI applications across industries—from autonomous vehicles and manufacturing quality control to retail analytics and healthcare imaging. Every frame analyzed, every object detected, every insight delivered in milliseconds.
Industry-leading annotation tools for bounding boxes, polygons, keypoints, and semantic masks. Collaborative workflows enable teams to label thousands of images per hour with built-in quality assurance, version control, and automated consistency checking across annotators.
Leverage foundation models and active learning to auto-label up to 90% of your dataset. Our intelligent labeling pipeline identifies edge cases, surfaces uncertain predictions for human review, and continuously improves accuracy through iterative feedback loops.
Train state-of-the-art models with zero infrastructure setup. Choose from YOLOv8, RT-DETR, SAM, CLIP, and dozens more architectures. Hyperparameter tuning, distributed training across GPU clusters, and automatic experiment tracking ensure reproducible, optimized results every time.
Deploy models anywhere with one click. Export to ONNX, TensorRT, CoreML, or TFLite for edge devices. Scale to millions of inferences with our managed cloud API. Run models directly in browsers with WebGL/WebGPU acceleration. Complete flexibility for any production environment.
Import images and videos from any source. Upload directly, connect cloud storage, or stream from cameras. Our intelligent ingestion pipeline handles format conversion, deduplication, and automatic metadata extraction for seamless data organization.
Label your data with precision tools designed for speed. Smart assistants pre-label using foundation models, reviewers validate quality, and version control tracks every change. Built for teams scaling from hundreds to millions of annotations.
Select architectures, configure training parameters, and launch experiments with a single click. Monitor metrics in real-time, compare model versions, and automatically select the best checkpoint. No GPU management, no infrastructure headaches.
Push trained models to production with confidence. Auto-scaling cloud endpoints handle traffic spikes. Edge exports optimize for specific hardware. Continuous monitoring alerts on drift, tracks performance, and enables instant rollbacks when needed.
Access a growing library of pre-trained foundation models optimized for common computer vision tasks. Start with proven architectures, fine-tune on your data, or train from scratch. Every model is production-ready with documented performance benchmarks and deployment guides.
Fully managed inference endpoints that auto-scale from zero to millions of requests. Global edge network ensures sub-100ms latency worldwide. Pay only for what you use with transparent per-inference pricing.
Export optimized models for NVIDIA Jetson, Raspberry Pi, Intel NCS, and custom hardware. TensorRT, ONNX Runtime, and OpenVINO acceleration ensures maximum throughput on resource-constrained devices.
Run inference directly in web browsers using WebGL, WebGPU, and WASM backends. Zero server costs, complete privacy, and instant user experience. Perfect for real-time demos, client-side processing, and offline applications.
Access hundreds of thousands of curated, labeled datasets covering every domain imaginable. From autonomous driving to medical imaging, wildlife monitoring to industrial inspection—find the training data you need or contribute your own.
Integrate computer vision into any application with our comprehensive APIs and native SDKs. From simple REST calls to streaming video analysis, our developer tools are designed for production workloads at any scale.
# Initialize ESSINBEE client from essinbee import Client, Model client = Client(api_key="your_api_key") # Load pre-trained detection model model = client.models.load("yolov8-x") # Run inference on image results = model.predict( source="image.jpg", confidence=0.5, iou_threshold=0.45 ) # Process detections for detection in results.detections: print(f"Class: {detection.class_name}") print(f"Confidence: {detection.confidence:.2f}") print(f"BBox: {detection.bbox}") # Export for edge deployment model.export( format="tensorrt", device="jetson-orin", precision="fp16" )
Ready to deploy production-grade computer vision? Our team of ML engineers and solution architects will help you design, build, and scale vision AI applications tailored to your specific requirements.