Computer vision: the hard part isn't the model.
Training a detector on a ready dataset is within reach for many today. A working system appears where the other questions are solved: where images come from, how data is labelled, what to do with errors and where the result lands. That's what we do.
Detection and segmentation
We find objects, defects and anomalies on images, outline regions and compute parameters from them. That's how EyeMesearing works: segmenting eye structures and calculating MRD1, MRD2 from the geometry.
Data and labelling
If there's no labelled data, collecting it is the first stage of the work, not a precondition. Labelling quality sets the system's ceiling more than model architecture does.
A threshold set by the cost of an error
A miss and a false positive cost differently in different tasks. The threshold is chosen for that cost, not for a good-looking number in a report.
Inference in a real system
Image ingestion, processing, result storage and delivery to where people work with them. This is where most of the engineering lives, not in training.
What tasks does computer vision solve?
In practice, four: find an object in an image, assign the image to a class, outline a region and compute parameters from it, spot an anomaly. Applied tasks are almost always a combination of these four.
How much data is needed?
It depends on how similar the objects are and how varied the capture conditions. You can start with a small evaluation set to find the ceiling instead of blindly collecting a large corpus.
Can off-the-shelf models be enough?
Sometimes yes, and that's a fine answer. Off-the-shelf models work well on common objects and poorly on ones specific to your industry. It's quick and cheap to check.
What about model errors?
They can't be reduced to zero. The question is framed differently: what costs more in your case — a miss or a false positive. The answer sets both the threshold and the human review flow.
Healthcare software development
Medtech, AI and computer vision in a sensitive domain — under a safety protocol.
Document processing
Data extraction from invoices, acts and contracts without manual retyping.
AI development
LLM features, assistants and RAG in a real product — with safety and cost control.