Alt text for every image, not just the first fifty:
written, tagged, checked
Writing alt text by hand works for the first fifty product images and quietly stops happening after that, which leaves most of a catalog invisible to screen readers and image search alike. An agent describes every image against your actual product data and writes the tag once, at scale.
The process today
Alt text is one of those tasks everyone agrees matters and almost nobody keeps up with past the first batch. A catalog launches with a few dozen products carefully described, and by the time it reaches a few hundred or a few thousand SKUs, alt text either stops being written entirely or gets filled with the product title copy-pasted into every field, which satisfies no screen reader and does nothing for image search.
Teams usually find this gap only when an accessibility audit flags it, or when a competitor’s product images start outranking theirs in image search for no reason anyone can immediately explain. By then the backlog is the whole catalog, not a handful of items, and writing a thousand accurate, non-repetitive alt-text entries by hand is not a task anyone volunteers for.
An internal bot built for a related image problem, generating product cards at 1080x1080 in Thai and English with a calibration system measuring the product on the finished card, shows what happens once an image pipeline gets properly engineered: cost per scene fell from $0.40 to $0.04 as the system matured, a tenfold drop that came from structure, not from cutting corners. A packaging design agent built for supplement labels, running against a knowledge base of Thai and Ukrainian labelling rules with a multilingual validator, shows the other side of the same problem: images in regulated categories need checks that go beyond a plain description.
What the agent does
The agent reads each image against the actual product data behind it, title, category, key attributes, rather than guessing from pixels alone, so a description stays accurate to what the product actually is and does. Descriptions are written to be genuinely useful to a screen reader, not keyword-stuffed for search, though the two goals usually align: a specific, accurate description tends to perform better in image search than a generic one anyway.
Tagging runs in bulk across an existing image library or catalog, writing directly into the alt-text and tag fields a platform like Shopify, WooCommerce or a DAM already exposes, so there is no separate spreadsheet to reconcile later. For regulated categories, the kind of supplement and packaging images the regulatory-aware packaging agent was built around, images route through a label-aware check before anything publishes, catching a claim or term that should not appear outside its proper regulatory context.
Images that are ambiguous, a product shot with no clear category match, or anything the agent is not confident describing accurately, get flagged for a human pass rather than published with a guess.
What stays with humans
Final sign-off on anything flagged as ambiguous or regulated stays with a human, and the product attribute data the agent grounds descriptions in is maintained by the team, not inferred from the image. Judgment calls on borderline regulatory language stay with whoever owns compliance for that catalog.
Guards
A new catalog runs a dry run against 100-200 images before go-live, so the team can check description accuracy before a full bulk pass runs. Regulated-category images route through a label-aware validator before publish, every batch is logged against its source catalog, and a kill switch pauses tagging without losing any already-processed entries.
Price and timeline
| Option | Price | What it covers | Timeline |
|---|---|---|---|
| Single automation | from $450 | One catalog or image library, bulk alt-text and tagging | 3 to 6 days |
| Department package | from $2,500 | Alt-text and tagging plus catalog translation plus review mining for one product team | 2 to 4 weeks |
Running cost is usually $20 to $70 a month in model usage depending on catalog size, with a budget cap set before launch.
Related
Alt-text and tagging pairs directly with translation and localisation of catalogs for teams running a product catalog in more than one language, and with review mining for insights once image and review data both need to stay current at the same scale. See automation everything and AI agents for the broader pipeline approach. The product-card pipeline behind the cost-per-scene drop is detailed in AI product card designer bot, and the label-aware validation approach is in packaging AI designer, regulatory.
If your catalog’s alt text stopped past the first few hundred products, get in touch and we will scope a bulk pass for the rest.
Tired of doing this by hand? We can take the whole routine off your team, not just this step: Routine takeover, from $400 →
FAQ
How much does image alt-text and tagging automation cost?
From $450 for one catalog or image library with a fixed description format, live in 3 to 6 days. Larger catalogs or ones needing regulatory label checks usually run $1,200 to $2,800.
How long before it is live?
3 to 6 days once we have access to the image library and the product data each image maps to. Most of the time goes into tuning description accuracy against your actual product attributes.
Which tools does it connect to?
Shopify, WooCommerce, a custom CMS, or a digital asset manager through its API, writing directly into the alt-text and tag fields those platforms already expose. A CSV or sheet export works for catalogs without a direct API.
What happens if the description gets a product detail wrong?
Descriptions are grounded in your actual product title, category and attribute data rather than generated from the image alone, and anything in a regulated category, like supplements with label claims, routes through a label-aware validator before publish.
Is our image and catalog data safe?
Images and product data stay in your own catalog, CMS or asset manager, nothing is published to a third-party image service, and every batch processed is logged against the catalog it came from.