Production AISystems
Forecasting and Vision

Computer Vision Solutions

Computer vision systems that detect, classify, track and count objects in images and video. From manufacturing inspection to retail analytics, security monitoring to autonomous systems. Edge and server inference, integrated with your cameras and business systems. Built for production deployment, not just lab demos.

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What are computer vision solutions?

Computer vision solutions use deep learning models to interpret visual information from images and video. The system can detect objects, classify them, segment them pixel-by-pixel, track them across frames, count them and read text (OCR). Unlike off-the-shelf vision APIs, a custom computer vision solution is trained on your specific images, integrated with your cameras or imaging systems and deployed at the edge or on servers depending on latency and bandwidth requirements. It is measured on precision, recall, false positive rate and inference latency.

Manual inspection is slow and inconsistent

People inspect products by hand. Computer vision inspects consistently and at production speed.

Off-the-shelf models do not know your objects

Generic models detect people and cars. Custom models detect your specific defects, products and objects.

Latency is too high for real-time use

Cloud inference adds latency. Edge inference runs on-site with sub-100ms response.

Vision systems are not integrated with business systems

Detection results need to trigger actions. Computer vision connects to your PLC, ERP and alerting systems.

How This Solution Can Be Built

Different situations call for different implementations. These are the most common variants.

Object detection

Detect and locate objects in images and video with bounding boxes. Counting, presence detection, anomaly flagging.

Image classification

Classify images into categories. Quality grading, defect classification, scene understanding.

Semantic segmentation

Pixel-level classification. Identify exactly which pixels belong to which object or region.

Object tracking

Track objects across video frames. Counting, flow analysis, behaviour monitoring.

How the System Works

From input to output, each step is engineered for a specific purpose.

1

Image capture

Images or video captured from cameras, scanners or existing systems

input
2

Preprocessing

Images are normalised, resized and enhanced for the model

data
3

Model inference

Vision model runs detection, classification, segmentation or tracking

ai
4

Post-processing

Results are filtered, aggregated and formatted for the business system

ai
5

Decision logic

Business rules determine the action—pass, reject, alert, log

control
6

System action

Result triggers an action in your PLC, ERP, alerting or logging system

output
7

Human review

Low-confidence results are routed to a human reviewer

human
8

Model improvement

Reviewed and corrected images feed back into model training

feedback

Production and Enterprise Readiness

What makes this solution work in a real operating environment—not just in a demo.

Edge and server

Models can run at the edge for low latency or on servers for higher accuracy. The right choice depends on the use case.

Camera integration

Works with industrial cameras, IP cameras, webcams, drones and existing imaging systems.

Model versioning

Models are versioned and deployed through a controlled pipeline. Rollback is available.

Confidence-based routing

Low-confidence detections are routed to human review. The threshold is configurable.

Throughput

The system is engineered for the required frames per second and processing throughput.

How Success Is Measured

The right metrics depend on the solution. These are the measures that matter for this system.

Illustrative system view — metrics shown in the dashboard above are labelled examples, not client results.

Precision

Percentage of detections that are correct—no false positives

Recall

Percentage of actual objects that are detected—no false negatives

False positives

How often the system detects something that is not there

FPS

Frames per second the system can process

Inference latency

Time from image capture to result, measured at P50 and P95

Processing throughput

Total images or video frames processed per unit time

View related case studies

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Tell us the workflow, problem or system you want to improve. We respond with how we would approach it.

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Frequently Asked Questions

Can the system work with our existing cameras?
Yes. The system works with industrial cameras, IP cameras, webcams, drones and existing imaging systems. If your cameras provide a video stream or image feed, the system can process it. For specialised use cases, camera selection and positioning recommendations are provided.
Should inference run at the edge or on a server?
Edge inference is better for low-latency applications—real-time inspection, safety monitoring—where the result must be available in milliseconds. Server inference is better for high-accuracy applications where latency is less critical and the server can run larger models. Many systems use both—edge for immediate decisions, server for detailed analysis.
How do you train the model on our specific objects?
The model is trained on your images. You provide labelled examples—photos of your products, defects, scenes or objects. The training pipeline handles augmentation, transfer learning and fine-tuning. For rare defects, synthetic data generation can supplement real examples. The model is evaluated on a held-out test set before deployment.
What happens when the model is not confident?
Detections below a confidence threshold are routed to human review. The reviewer sees the image, the detection and the confidence score. The reviewer confirms or corrects the result. Corrections are logged and used to improve the model through periodic retraining. The threshold is configurable per use case.
Can it track objects across video frames?
Yes. Object tracking follows objects across consecutive frames. This enables counting (how many objects passed a point), flow analysis (which direction objects moved) and behaviour monitoring (how long an object stayed in an area). Tracking uses motion prediction and appearance matching.
How do you handle model updates in production?
Models are versioned and deployed through a controlled pipeline. A new model is tested on a held-out set, validated against business rules and deployed first to a canary configuration. If performance degrades, the system rolls back to the previous version. Model updates do not require system downtime.