VISION / 03

Computer Vision & Document AI

We build quality inspection, detection, segmentation, OCR, and document-processing solutions together with cameras, edge devices, and operations interfaces.

Discuss your project

Runtime flowVISION / 03

01Camera feed
02Preprocessing
03Model
04Decision threshold
05Operational action

The problem

We define the problem before the solution.

Most computer-vision projects fail on the image, not the model. Camera angles drift in the field, lighting changes with the shift, and the real defect distribution differs from the one seen in the lab. A model with high test accuracy can behave differently in production because what it now measures is no longer the same problem.

So we start with the signal rather than the model: camera placement, resolution, lighting, sample diversity, and class balance are measured first. Then come the labeling guide, the evaluation set, and a field test. Only once that ground is solid do we optimise the model and serve it to run on edge hardware within its latency, thermal, and resource limits.

Use cases

Where this system usually shows up.

Production-line quality inspection

A system that catches surface defects, missing parts, or assembly errors at line speed, diverts suspect units, and records the decision.

Plate recognition and access control

A recognition layer that reads vehicle plates and drives parking, campus, or facility access flows, able to run offline.

Document and form processing

An OCR pipeline that extracts fields from invoices, waybills, forms, and contracts, and routes low-confidence results to human review.

Counting, tracking, and zone monitoring

A layer that turns camera streams into operational alerts for object counting, flow tracking, and rule violations inside defined zones.

What does the system deliver?

Real-time defect and anomaly detection

Reduced repetitive manual document processing

Low-latency inference at the edge

Delivery scope

We deliver an operable product, not just a model.

01
Camera and image-quality analysis
02
Labeling strategy and dataset
03
Model, edge inference, and API
04
Operations dashboard and error feedback loop

How we build it

We design technical components together with the operating workflow.

Validate the signal

We measure camera angle, lighting, resolution, and sample diversity.

Prepare data

We establish labeling guidelines, quality control, and class balance.

Test in the field

We measure latency, false positives, and environmental shifts in real conditions.

Connect operations

We connect alerts, sorting, logging, and human review workflows.

Technical approach

The layers and tools we work with.

Models: YOLO, Detectron2, MMDetection, OpenCV
Serving: ONNX, TensorRT, Triton Inference Server
Documents: OCR pipelines, layout analysis, field extraction and validation
Operations: Edge deployment, alerting flows, defect feedback loop

Frequently asked questions

What to know before making a decision.

01How much image data is required?

Volume alone is not enough. Defect diversity, lighting, camera angle, and class balance determine the real data requirement.

02Can the system work without internet?

Yes. Suitable models can run on edge devices or local servers with low latency.

03Who does the labeling?

We write the labeling guide and the quality-control rules; how edge cases are marked is the most critical part. Labeling itself can be run by your team, a vendor, or a mix. Whichever route you take, we measure consistency on a sample and revise the guide after the first round.

04Will the model degrade over time in the field?

Performance can drop when cameras are replaced, products or packaging change, or seasonal lighting shifts. Rather than predicting it, we measure it: a fixed evaluation set, monitoring of the confidence distribution, and collection of incorrect results from the field. When retraining is needed, the trigger is explicit.

05When is computer vision the wrong choice?

If the information is not reliably visible to a human eye either, if the defect is so rare that samples cannot be collected, or if the same data can be read directly from a sensor, vision is the wrong tool. We test this during discovery with a few hundred samples and report the result plainly.