Dataset
Client dataset annotation.
Useful when there is volume and consistency: invoices, labels, defects, counting, occupancy. YOLO-v9 fine-tuned + GPT-4V for open-ended cases.
Computer vision has a clear sweet spot: high-volume tasks with consistent input images and clear economic impact — production defect inspection, OCR on unstructured invoices, warehouse unit counting, occupancy monitoring.
We work with YOLO-v9 fine-tuned on the client's dataset for closed cases (specific defects, defined counts) and with GPT-4V when the input is more open-ended (ambiguous visual classification, text extraction from variable forms).
In the most recent industrial deployment — weld defect detection — we achieved 99.2% recall on critical defects with 2.1% false positives, and a 14% increase in line throughput. Human inspectors moved to qualitative review and training.
Client dataset annotation.
YOLO fine-tuning on the dataset.
Edge for latency; cloud when justified.
Conservative policy. Inspectors moved to qualitative review.
The operational detail: what we deliver as part of the engagement and what we keep active afterwards.
Defect inspection
Recall >99% in closed projects.
Document OCR
Invoices, labels, forms.
Automated counting
Warehouse, occupancy, production.
Edge pipeline when needed
Low latency on the shop floor.
Triple human validation
During calibration.
Human redeployment
Inspectors reassigned to qualitative tasks.
If computer vision is used for decisions about individuals, the AI Act classifies it as high risk.
Industrial vision is AI + systems + compliance sometimes.
We coordinate. Professional when justified; tools such as Labelbox.
Edge when latency matters.
We do not sell hardware. We advise and integrate.