Automated alt text has been around for years, and most of it shares the same flaw: it looks at the image and nothing else. Point one of those tools at a photo on your team page and you'll get something like "a person smiling." Technically true. Completely useless to the screen reader user trying to figure out whether that's your CEO, a customer testimonial, or a stock photo.
The image alone doesn't tell you what the image is for. The page does. That's why AllAccessible agents read the whole page before suggesting a single word.
The problem with looking at elements in isolation
Alt text and control labels only work when they carry the element's purpose, not just its appearance. Consider what an isolated view gets wrong:
- A photo of a woman at a laptop could be a founder bio portrait, a case-study customer, or decorative filler — three different descriptions, and "woman at laptop" serves none of them.
- A button that says "Submit" is meaningless read aloud out of context. Submit what? To whom?
- A link labeled "Learn more" is a dead end for someone navigating a page by its links, hearing "learn more, learn more, learn more."
A tool that examines each element on its own can only ever guess. And confident guesses that miss the mark are worse than nothing — they actively mislead the people relying on them.
What reading the whole page changes
AllAccessible AI drafts image descriptions and control labels using the surrounding page context: the headings above an element, the copy around it, what the page is actually about.
Here's the concrete difference. Say your events page has a registration form ending in a vague "Submit" button. An element-by-element tool might improve it to "Submit button" — no help at all. AllAccessible AI reads the page, sees it's a signup form for your upcoming webinar, and suggests: "Submit registration for the May 21 webinar."
A screen reader user landing on that button now knows exactly what pressing it does, without reading the rest of the form. That's the standard the suggestions aim for across the board: image descriptions that reflect the image's role on the page, link text that says where the link goes, labels that state what the control actually does.
Suggestions in your site's language
Context also includes language. Suggestions arrive in your site's primary language by default — a French site gets French alt text, not English descriptions bolted onto French pages. And if your site serves multiple audiences, you can override the locale page by page, so your German-language product pages and your English-language docs each get suggestions their visitors can actually use.
Your team still approves every word
Context-aware drafting makes suggestions dramatically better. It doesn't make them infallible — and it doesn't need to, because nothing ships automatically.
Every suggestion lands in a review queue. Your team opens it, reads it against the page, and decides: approve it as written, edit the wording to match your voice, or set it aside. Only approved text goes live. This is the human-in-the-loop part of human-in-the-loop agentic remediation, and it's deliberate. You know your products, your people, and your brand voice; the AI's job is to hand you a strong first draft instead of a blank field.
And if you ever change your mind, approved changes are reversible, with a record of what changed and when. You're never locked into a suggestion, and you always know the current state of your site.
Why this matters to actual visitors
It's easy to treat alt text as a box to tick — an audit says 40 images are missing descriptions, a tool fills in 40 strings, the number goes to zero. But the people this work is for don't experience your site as an audit score. They experience it one element at a time, through a screen reader, trying to register for your webinar or find the right product.
For them, the difference between "image" and "founder Maria Chen speaking at the 2026 partner summit" is the difference between being locked out of your content and being included in it. Context-aware suggestions, reviewed by a human who knows the site, are how you get the second experience at scale — without your team writing hundreds of descriptions from scratch.
The pattern behind the feature
This is really one instance of how AllAccessible approaches everything: agentic AI does the heavy drafting with full context, your team keeps the final say, and every change stays visible and reversible. Alt text and labels are simply where the difference is easiest to hear — literally — the first time you listen to a page before and after.
If you want to see it on your own pages, the setup is quick: run an audit, open the review queue, and read what AllAccessible AI suggests for the images and buttons you already have. You'll know within five minutes whether the suggestions understand your site. We think you'll find they do.
Get started with AllAccessible and put context-aware suggestions — with your team in the loop — to work on your site.
Frequently Asked Questions
- What is context-aware alt text?
- It's alt text drafted using the whole page for context — the headings above an element, the copy around it, and what the page is for — rather than analyzing the image in isolation. The same photo needs a different description depending on whether it's a founder portrait, a case-study customer, or decorative filler, and only page context can tell those apart.
- Why is generic automated alt text a problem?
- Tools that examine each element on its own can only guess at its purpose, and confident guesses that miss the mark actively mislead screen reader users. 'Woman at laptop' or 'Submit button' is technically accurate but tells the visitor nothing about who the person is or what pressing the button does.
- Does AllAccessible support non-English websites?
- Yes. Suggestions arrive in your site's primary language by default — a French site gets French alt text — and you can override the locale page by page, so multilingual sites get suggestions each audience can actually use.
- Are AI-drafted descriptions published automatically?
- No. Every suggestion lands in a review queue where your team approves it as written, edits it to match your brand voice, or sets it aside — only approved text goes live. Approved changes are reversible and recorded, so you always know the current state of your site.