Multimodal search lets users query with more than text, such as images, video, voice or a live camera, and get answers that combine visual and text input.
What is Multimodal Search?
Multimodal search is search that accepts and combines more than one type of input, such as text, images, voice, video or a live camera view, and returns answers based on understanding all of them together. A user can photograph a device and ask “how do I reset this?”, and the system interprets both the image and the question to respond.
Visual search is not new: Google Lens launched in 2017. What changed is that multimodal AI models, such as Gemini and GPT-4-class models, can reason across images and text in one step. Google said in October 2024 that Lens handled nearly 20 billion visual searches a month. In April 2025 Google added image questions to AI Mode, and Search Live brought spoken conversations about what a phone camera sees.
How it works
Google’s description of multimodal AI Mode gives a useful model:
- Object recognition. Lens identifies objects in the image.
- Scene understanding. Gemini interprets the whole scene: relationships, materials, colors, arrangement.
- Query fan-out. The system issues multiple searches about the image as a whole and the objects in it.
- Answer. It composes a response with links, and the user can ask follow-up questions.
Other assistants follow similar patterns: ChatGPT, Gemini and Claude accept images and screenshots, and voice modes turn speech into text queries before retrieval.
From SEO to AEO and GEO
The SEO predecessor was image SEO: alt text, file names, image sitemaps and ranking in Google Images. That treated images as a separate vertical. Multimodal AI search folds images into the main answer flow and starts from the user’s picture, not keywords.
| Aspect | Image SEO | Multimodal AI search |
|---|---|---|
| Query | Text typed into Google Images | Photo, screenshot, video, voice |
| Matching | Alt text, surrounding text, image signals | Visual understanding plus web retrieval |
| Output | Image results grid | Generated answer with links |
What carries over: Google’s image best practices, descriptive alt text, relevant surrounding text and crawlable images. What changed: the user’s image may show your product, interface or error screen, and the AI needs text on your pages to connect that visual to an answer.
What to do about it
- Document UI with text. Pair product screenshots with captions that name the screen and feature.
- Write out error messages. Include exact error strings and codes in docs and troubleshooting pages, not just screenshots.
- Label diagrams. Architecture and workflow diagrams should have readable labels and an accompanying text explanation.
- Add transcripts to videos. Product demos and webinars become retrievable when transcribed.
- Keep product visuals consistent. Hardware vendors and device makers should use clear product imagery with model names in nearby text.
Common mistakes
- Facts only in images. Pricing tables or specs saved as images are harder for both people and AI systems to use.
- Generic stock photos. They add nothing a visual query can match to your product.
- Empty or stuffed alt text. Write a short, accurate description, not a list of keywords.
Related terms
Sources
- Bringing multimodal search to AI Mode, Google (Apr 7, 2025)
- Google updates: AI-organized search, Google Lens and more, Google (Oct 3, 2024)
- AI in Search: Going beyond information to intelligence, Google (May 20, 2025)
Frequently asked questions
What are examples of multimodal search?
Pointing Google Lens at a device and asking what it is, recording a short video in Lens and asking a question about it, circling something on an Android screen with Circle to Search, uploading a screenshot of an error message to AI Mode or ChatGPT, or talking to Search Live about what your camera sees.
Does multimodal search matter for B2B SaaS?
More than it first appears. Engineers screenshot error messages, dashboards and architecture diagrams and ask assistants to explain them. If your documentation includes clear screenshots with matching text, error codes written out and labeled diagrams, assistants have better material to connect a visual query to your content.
How do I optimize images for multimodal search?
Follow Google's image SEO basics: use relevant, high-quality images near related text, write descriptive alt text and file names, add captions, and make sure images are crawlable. For video, provide transcripts and key moments. Avoid putting important facts only inside images.
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