Computer vision development
Salto Angel builds computer vision for operations that still depend on someone looking: documents, photos, scans, and screens that should become a structured result.
Seeing is a business task
Vision projects start from what a person is checking today and what a miss costs. That might be reading a form, spotting a defect, confirming that a document is complete, or pulling fields from a photo. The model is judged on those cases, not on a generic benchmark.
Neural networks where the image is the data
Most of this work is neural network development aimed at pixels: detection, classification, OCR-style reading, and checks that combine vision with rules. When a language model should read a clean digital file instead, we use that and keep vision for what is actually visual.
From a folder of examples to a check in the workflow
We need examples of the good, the bad, and the ambiguous. The delivery is not a weight file in isolation. It is a service your process can call, with a review step for low-confidence results and a way to add new examples when the camera, the paper, or the product changes.
Experience on the team
Computer vision and machine learning are part of how Salto Angel's founders have built products, not a slide in a services menu. Custom vision sits next to custom AI and systems integration so the result lands in the tool that needs it.
Frequently asked questions
What computer vision services do you offer?
Models that classify, detect, or read images and scans, integrated into a process your team already runs, with a human review for uncertain cases.
Do you need thousands of labeled photos?
You need enough examples to measure. We start with what you have, label the confusing cases, and expand only if the metric says the model is not ready.
Can vision read documents?
Yes, especially scans and photos of paper. Clean digital documents may be a language or parsing problem instead, and we will choose that if it is more reliable.
Is this related to your other AI services?
Yes. Vision is one input. Agents, automation, and integrations decide what happens after the model sees something.