Image Describer

Describing an image well is deceptively hard work. Accessibility teams need alt text that conveys what actually matters in a picture. Content teams need captions and metadata at a volume no one wants to write by hand. Researchers and archivists need consistent descriptions across thousands of files. Image Describer is built for exactly that gap: you upload an image and get back a written description that reads like a person looked at it carefully. What separates it from a generic labelling API is the breadth of what it reports. Instead of a list of detected objects, it identifies the subject, the surrounding objects, the composition, the visual context and the mood of the image, then expresses that as connected prose. That distinction matters in practice — 'dog, grass, ball' is a tag set, while a sentence describing a border collie mid-leap catching a ball on a sunlit lawn is something a screen reader user can actually picture. The accessibility use case is the most immediately valuable one. Writing meaningful alt text is a legal and ethical requirement for a lot of organisations and a permanent backlog for most of them. Running an image library through a describer produces a first draft for every file, which turns an impossible task into an editing task. Beyond that, the same output feeds content workflows: image SEO metadata, catalogue descriptions, social captions, and search indexes that let people find images by what is in them rather than by filename. It runs in the browser with no setup, which keeps it usable for a marketer with one image as well as a team working through a backlog. As with any vision model, the output is a strong starting point rather than a final answer — it can misread ambiguous scenes, and anything involving names, brands or specialised subject matter still needs a human pass. For teams that currently write nothing at all because writing it manually is too slow, that trade is an easy one.