Computer vision development company
Computer Vision That Converts Images and Video Into Operational Events.
CoreRiqo Labs develops object detection, multi-object tracking, OCR, visual inspection, pose estimation, and edge video analytics systems for real operating environments.
ORIGINAL CORERIQO LABS DEMONSTRATIONThe operating problem
A detection model is not yet a video analytics system.
Real deployments require stable identities across frames, zone and trajectory logic, camera calibration, event rules, evidence clips, privacy controls, monitoring, and performance that matches the available edge or cloud hardware.
How CoreRiqo Labs engineers the solution
We design the complete perception pipeline: data preparation, detection, tracking, calibration, event logic, edge optimization, API integration, annotated review, and diagnostics that make model behavior understandable.
Engineering scope
Capabilities built into the system.
ByteTrack, DeepSORT, and BoT-SORT tracking
Zone, line-crossing, dwell, and proximity events
OCR and visual quality inspection
ONNX, TensorRT, TFLite, and CoreML optimization
Edge and cloud deployment
Reference architecture
Designed beyond the model layer.
A production AI system needs data, interfaces, controls, evaluation, deployment, and ownership—not only a model API.
- 01Camera, image, or video ingestion
- 02Preprocessing and model inference
- 03Identity tracking and scene calibration
- 04Zone, trajectory, and business-event logic
- 05Alerts, evidence clips, and event API
- 06Model, stream, latency, and device monitoring
Where it creates value
Practical applications.
Warehouse safety analytics
Track people and vehicles, monitor restricted zones, and create reviewable safety events.
Retail intelligence
Measure occupancy, movement, queues, product interaction, and configured shelf conditions.
Manufacturing inspection
Detect defects, verify assembly, read labels, and route uncertain items for review.
Logistics and asset tracking
Track vehicles, pallets, containers, or packages and connect visual events to operations.
Typical technology stack
Python · OpenCV · PyTorch / TensorFlow · YOLO · ByteTrack / DeepSORT / BoT-SORT · ONNX · TensorRT · FastAPI · AWS
Service FAQ
Questions before a build begins.
Can computer vision run on edge devices?
Yes. We optimize models for the target device using ONNX, TensorRT, TFLite, CoreML, quantization, and pipeline-level performance work.
Can it work with existing cameras?
Often yes. We first assess stream format, resolution, placement, lighting, field of view, retention, and network constraints.
How do you protect privacy?
We can minimize retained media, process locally, blur configured regions, store events instead of full video, and apply access controls.
Start with the workflow
Bring us the business problem.
In a focused 30-minute consultation, we will discuss the users, workflow, data, integrations, risk, and the smallest useful next step.
Book your consultation