AI image recognition and analysis
AI image recognition tags, describes and checks images automatically, making large image libraries searchable.
Background
Many firms hold thousands of product shots, reference images and customer uploads. Sorting and describing them by hand ties up staff. For shops and manufacturers with large ranges, image recognition is often the fastest route to better filters, fuller product data and accessible images.
Use cases
Tagging: Colour, material, product type or subject, stored as metadata.
Alt text: Draft image descriptions for your editors to review.
Similarity search: Customers search with a photo, and the shop suggests items that look alike.
Quality checks: Flags for pixel dimensions, cut-outs, duplicates or unwanted content in uploads.
Documents and receipts: Text recognition (OCR) reads data from scans, delivery notes or forms.
Technology
Depending on the task, we use cloud services, multimodal language models or specialised open-source models. Similarity search relies on embeddings in a vector index. Where standard models fall short, we fine-tune on your sample images. Results feed into the media library, PIM system or shop.
Data protection and the law
Images can contain personal data, such as faces or number plates. We map data flows in advance and, if needed, process data in the EU or on your own servers. The GDPR and the EU AI Act heavily restrict biometric identification. We review any such use with particular care or advise against it. Customer uploads get fixed retention and deletion rules.
From test to production
A test on a sample shows how often recognition gets it right. You then decide whether to expand. Uncertain results go to a review list.
Large libraries run in batches, new images on upload. The cost per image can be calculated in advance.