Object Detection and Counting
Custom detectors, such as YOLO models trained on your data, that locate, classify, and count objects in images or video streams.
Object detection using YOLOComputer Vision Development
Computer Vision Systems Built for Real-World Conditions
We build custom computer vision, from data preparation and model training to deployment and monitoring, engineered for the lighting, cameras, and edge cases your operation actually has.
- Classical and deep learning
- Evaluated on your footage
- Cloud or edge deployment
Beyond the benchmark
A model that works on sample images is not a vision system
Vision models are sensitive to everything a demo hides: camera angle, lighting, motion blur, occlusion, and objects that look slightly different from the training set. A system that performs well on curated images can fail quietly the first week it sees real footage.
We engineer computer vision as a complete system: data collection and labeling, preprocessing, the right model for the job, evaluation on your own images or video, and deployment where the decision needs to happen.
Vision capabilities engineered around your workflow
We start with the decision the system must support and the conditions it will run in, then choose the simplest approach that can meet the accuracy and latency required.
Visual Inspection and Defect Detection
Systems that flag defects, missing parts, or anomalies against clear thresholds, with uncertain cases routed for human review.
Segmentation and Measurement
Separating objects from background to measure size, shape, area, or coverage, using classical techniques or trained segmentation models.
OpenCV filtering and segmentation guideOCR and Document Vision
Reading text, labels, and forms from scans and photos, with preprocessing for skew, glare, and uneven lighting.
Video Analytics and Monitoring
Frame-level detection and event logic that turns camera feeds into alerts and records, with privacy controls where people are on camera.
Edge and Cloud Deployment
Optimized inference on cloud GPUs or edge devices, sized for the latency, connectivity, and cost constraints of each site.
Production experience
Computer vision for safety monitoring in sensitive environments
We helped a correctional technology company bring an AI-powered safety monitoring platform to production, including AI and computer vision workflows, HIPAA-focused security and architecture work, and production engineering, testing, and deployment support for real-world correctional environments.
See the work we have delivered- Vision
- AI and computer vision workflows for safety monitoring
- HIPAA
- focused security, architecture, and remediation
- Prod
- engineering, testing, and deployment support
Choosing the approach
Classical OpenCV, deep learning, or both
Not every vision problem needs a neural network. With a fixed camera, controlled lighting, and consistent objects, classical techniques such as thresholding, contours, and template matching can be fast, explainable, and cheap to run.
When appearance varies widely, a trained model is usually more robust. Most production systems combine both: classical preprocessing and validation around a learned detector.
Classical techniques
Best for controlled conditions, tight latency budgets, and problems where every decision must be explainable.
Trained models
Best when objects, lighting, or backgrounds vary and enough labeled examples can be collected.
From sample images to a monitored vision system
- 01
Assess
Review the decision to support, the cameras and conditions, existing images or video, and what accuracy and latency are required.
- 02
Prove
Build a focused proof of concept on your own footage to confirm the approach before investing in full data collection and labeling.
How our AI proof of concept service works - 03
Build
Prepare and label data, train and evaluate models against representative holdout sets, and integrate results into your workflow.
- 04
Deploy and Monitor
Deploy to cloud or edge, then track accuracy, latency, and drift as cameras, lighting, and objects change over time.
How we approach computer vision
Evaluate on your images
Accuracy is measured on footage from your environment, not on public benchmarks.
Design for the hard cases
Lighting changes, occlusion, and rare events are collected and tested deliberately.
Keep people in the loop
Uncertain detections are routed for review where an error carries real cost.
Respect privacy
Where people are on camera, data handling and retention are designed in from the start.
What teams ask before we start
Clear answers on scope, architecture, data, and delivery.
What kinds of computer vision systems do you build?
We build object detection and counting, visual inspection and defect detection, segmentation and measurement, OCR and document vision, and video analytics systems, deployed in the cloud or at the edge.
Do you use OpenCV or deep learning?
Both. Classical OpenCV techniques work well in controlled conditions and are fast and explainable. Deep learning models such as YOLO are more robust when appearance varies. Many systems combine the two.
How much training data do we need?
It depends on how much the objects and conditions vary. We assess existing images or video first, and a focused proof of concept shows how much additional data and labeling the production system will need.
Can computer vision run on edge devices?
Yes. We optimize models for the latency, connectivity, and hardware constraints of each site and deploy to edge devices when sending video to the cloud is impractical.
Can you improve an existing vision model?
Yes. We review the data, labels, evaluation, and deployment to find the limiting factor, whether that is training data, model choice, preprocessing, or how the results are used.
How do you handle privacy when people are on camera?
We design data handling, access, and retention around the use case from the start, and keep sensitive processing within infrastructure you control.
Related expertise
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Book a Discovery CallTurn a vision problem into a dependable system
Share what the system needs to see, the conditions it runs in, and the decision it supports. We will help you choose the right approach.