Images for AI: Being Visible in Multimodal Search

For years we thought of artificial intelligence as a system that only reads: it scans words, parses sentences, and produces an answer. But today's generative engines no longer just read — they also see. They can look at a product photo, an infographic, or a screenshot and interpret what is inside: the object, the scene, the colors, even the text printed on the image. This is the doorway to a new world called "multimodal search", and it matters far more to your brand's visibility than you might expect.

The problem is that most small-business websites still prepare their images for human eyes only: photos that look beautiful but tell the machine nothing, meaningless file names, empty alt text. When an AI cannot "read" an image, it ignores it — and leaves you out of the answer. In this article we walk through how multimodal search works, how an AI understands an image, and how to prepare your brand's visuals for both people and machines, step by step.

What multimodal search is

Multimodal search means an AI system can process several "modalities" at once — text, images, and sometimes audio and video — together. In the past, an image was really just the text around it as far as a search engine was concerned; the engine did not read the photo itself, only its file name and alt text. Today engines like Gemini and ChatGPT can look directly at an image's pixels and describe what they contain.

The practical consequence is clear: users no longer only type words. Someone can upload a photo and ask, "what is this, where can I buy it, what similar thing would you recommend?" Or they ask a question in text and the engine answers by drawing on both written and visual sources. An image is no longer decoration on the page; it is part of the answer. When your visuals are understood correctly by the machine, your chances of appearing in this new answer layer rise. When they are not, even your best product photos become a blank frame in the machine's eyes.

This shift runs in two directions. On one side, people now search with images: they photograph a product and ask "what is one like this?", share a screenshot, or point a camera at an object to learn what it is. On the other, generative engines increasingly place images inside the answers they build, so being the image shown beside an answer becomes a new form of visibility in its own right. In both directions, the brands whose visuals a machine can understand are the ones that surface.

How AI "understands" an image

Modern vision models turn a photo into a numerical form that represents the objects inside it, the scene, and the relationships between them. They recognize a leather wallet as a leather wallet and a coffee machine as a coffee machine; they read text on the image with optical character recognition; they assess color, texture, and composition. In other words, the machine no longer sees "a photo of something" — it sees an object it can name.

But there is a crucial limit here: the model can understand what is in the image, yet it cannot know on its own that the image belongs to your brand. What ties a photo to a specific business, product, or entity is the surrounding context and consistent signals. When the machine recognizes you as a clear entity, it is far easier for it to attribute your images to the right owner. That is why an image strategy cannot be separated from the work of turning your brand into an entity machines recognize; if who you are is unclear, so is who your images belong to. Recognizable, consistent visual marks — your logo, your packaging, a steady product style — strengthen the bond that ties an image to your brand.

Alt text and file names: the machine's first clues

However powerful vision models become, alt text and file names are still the clearest, most direct clues you give the machine. Alt text exists for accessibility — for visually impaired visitors using screen readers — but precisely for that reason it describes the image in plain, descriptive language, and it does the same job for machines. A well-written alt attribute becomes a reliable anchor the engine leans on while interpreting the image.

Good alt text genuinely describes the image: not "image" but "handmade brown leather card holder, shown open". File names work the same way: "IMG_export.jpg" tells the machine nothing, while "brown-leather-card-holder.jpg" carries context. The same logic extends to captions and the text immediately around the image. A few solid principles:

• Be descriptive, do not stuff keywords: alt text should be a natural phrase, not a pile of terms.
• Keep file names readable and meaningful; separate words with hyphens and avoid spaces.
• Write what the image actually shows, so the claim on the page and the visual confirm each other.
• For purely decorative images that carry no meaning, leaving alt text empty is a valid choice.

Context and originality: an image alone is not enough

An image is far stronger together with the text around it. When an AI interprets a photo, it looks not only at the pixels but also at which heading it sits under, which paragraph it sits beside, and which page it lives on. An image next to relevant, explanatory text is much easier to understand than one floating in a vacuum. In other words, the context you place an image in is often as important as the image itself.

Originality matters here too. Stock photos that everyone uses tell the machine nothing special about your brand; if the same image appears on dozens of sites, it does not set you apart. Original visuals that show your own product, your team, your process, and your real work are a trust signal for both people and machines. Which brand an AI decides to cite has a lot to do with authority and trust signals, and original, well-framed imagery is one of them — it says there is a real business behind this content.

Product imagery and structured data

On commercial pages, images are especially critical, because a buyer rarely decides without seeing the product. If an AI is going to include a product in an answer, it wants to understand what it is, who it is for, and what it looks like. This is where structured data comes in: Product and ImageObject markup ties an image explicitly to a specific product, its attributes, and its availability. The machine no longer has to guess; the relationship is laid out plainly in front of it.

Unfortunately, many product pages are full of images the machine cannot read, or template photos that carry no context at all. Why an AI skips your product page is often as much about the images as the text. Real product photos, different angles, in-use scenes, and clean markup to support them all noticeably improve the odds that your product page shows up in multimodal answers.

Where images sit in GEO

Image optimization is not a standalone tactic; it is part of a bigger picture. Generative engine optimization (GEO) is about whether your brand is named and cited in AI answers, and images are a growing component of that visibility. When text, entity signals, and visuals work together, it becomes easier for the machine to understand and recommend you.

So is your effort working? Visibility is managed by measurement, not assumption. You can start measuring today which questions you answer, whether your name appears in the answers, and where your competitor stands out. Rocketly GEO Suite today measures whether your brand is mentioned and cited in Google AI Overviews; multi-engine measurement for engines like ChatGPT and Perplexity is on the roadmap. When your images tell the machine a clear story, seeing your place in those measurements gets easier too.

Frequently Asked Questions

With image AI this advanced, is alt text still necessary?

Yes. Alt text is needed first for accessibility, and that alone is reason enough. Beyond that, not every engine processes an image's pixels to the same depth; alt text, the file name, and the surrounding text are the most reliable clues that tell the machine what the image is without leaving room for ambiguity. Even as vision models grow stronger, alt text remains an insurance policy that locks in meaning.

Do stock images hurt my AI visibility?

There is no direct penalty, but stock images do not set you apart from competitors. When the same photo appears on dozens of sites, it tells the machine nothing unique about your brand. Original visuals of your own product and work carry a stronger signal of trust and authenticity; whenever you can, investing in original imagery almost always pays off better.

Does Rocketly measure whether my images are seen by AI?

Rocketly GEO Suite does not scan your images one by one; what it actually measures is whether AI mentions and cites your brand for the buyer questions you track — today within Google AI Overviews. Images are one of the inputs that feed that visibility; well-prepared visuals help you appear in answers, and the measurement shows the result of that effort.