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Data and labelling strategy
What to capture, what to label, and what is good enough to train, without labelling everything you own.
05 / Services
Computer Vision
Detection, inspection and recognition models trained on your imagery, wired into the workflow that needs the answer.
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In short
What can computer vision do in a real workflow?
The Cubeless Company builds computer vision systems that replace manual image review at volume: defect detection, counting, OCR, product recognition and safety events. We start with a few hundred well-chosen labelled examples per class, prove the signal on a held-out set, then deploy to edge or cloud with a human review loop on uncertain cases. Accuracy targets are a business decision, set during scoping against the cost of a miss versus a false alarm.
| Alternative | Choose it when |
|---|---|
| Manual review | Volume is low and the edge cases still need a person anyway. |
| A vision model in the loop | People cannot keep up, and a fallback path exists for uncertain cases. |
| Rules and thresholds only | The visual pattern is a simple measurement, not a judgement. |
- Starting data
- Hundreds of labelled examples per class, not millions
- Deploy
- Edge or cloud, driven by latency and residency
- Production
- Fallback path and live accuracy monitoring
- Related work
- RPM
Outcomes
What this engagement moves.
Clear results we design toward from the first scoped release.
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Models evaluated on your real edge cases
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Inference that fits the speed your process needs
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A path from pilot to production with clear ownership
What’s included
Built as a system, not a pile of tickets.
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Model development
Detection, classification or inspection tuned to your imagery and failure modes.
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Deployment and review
APIs, edge or cloud inference, human review loops and monitoring in production.
How we deliver
From scoped first release to production.
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Prove the signal
A narrow use case, a held-out set, and an honest baseline before we scale labelling.
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Train and stress
Iterate on the hard cases your operators already know about.
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Put it in the loop
Ship into the real workflow with fallbacks, then expand coverage that earns trust.
More services
Keep exploring.
Computer Vision / FAQs
Questions we get asked.
What teams usually want to know before starting computer vision work with us.
The common production uses are visual inspection and defect detection on a line, counting and measuring objects, reading text and codes from images or documents, recognising products or parts, and monitoring for safety or compliance events. In each case the model replaces manual image review at a volume where a person cannot keep up.
Far less than most teams expect to start. We usually begin with a few hundred well-chosen labelled examples per class, weighted towards the hard cases your operators already know about, and prove the signal on a held-out set before scaling labelling. That keeps the labelling cost tied to results rather than paid up front.
That is a business decision, not a technical one, and we set it during scoping. The target depends on the cost of a false positive versus a missed detection in your process. We then design the review loop around that target, so the model handles the confident cases and routes uncertain ones to a person.
Yes. We deploy to edge devices where latency, bandwidth or data-residency rules require it, and to cloud inference where they do not. The decision is driven by how fast your process needs an answer and where the imagery is allowed to travel.
We ship it into the real workflow with a fallback path, monitor accuracy against live data rather than only the test set, and keep a human review loop on the cases the model is unsure about. Coverage widens as the model earns trust, and ownership and retraining are agreed before launch.