Detection, classification and extraction from images and video, usually applied to a process currently done by eye.
Inspection, document capture and counting — where the work is repetitive, high-volume and visually consistent. The projects that succeed are the ones where somebody can say precisely what a correct answer looks like, and has enough labelled examples to prove it.
Domains where the failure mode is expensive and the images are inconsistent. The pilot demo and the production accuracy are very different numbers. Capture conditions defeat more of this work than model architecture does. If the lighting, angle and image quality are not controlled, that is the project, and we will say so before the modelling starts.
Labelled data budgeted as a real line item, and accuracy reported against a held-out set from the actual capture conditions. The first question we ask is what happens when the system is unsure. In anything clinical or safety-related the answer is that a person decides, and the model’s job is to order the queue rather than to close it.
Where this appears on the site
We work in Computer vision, and no case study or service page currently published on this site prints it in its stack. Rather than describe an engagement you cannot check, this space stays empty until one does. Ask us and we will talk you through it directly.
Also in ai and machine learning
Tell us what it is running, what it costs you today, and what you need it to do next. A senior engineer will tell you what we would keep and what we would change.
