Multimodal Search

What Is Multimodal Search?

With Google Lens, Circle to Search, and AI-powered image recognition, more and more users are searching not just with text but also with photos, voice, or video. For your SEO, this means images need alt texts, videos need structured data, and your content must be findable across different formats. Multimodal optimization is becoming a competitive advantage.

Multimodal search refers to the capability of modern search systems to process different types of input simultaneously — text, images, voice, video, or a combination. A user could upload a photo and add a text question, such as: “Where can I get this piece of furniture cheaper?” Google introduced this functionality with Lens and the Multisearch feature, and AI models like GPT-4o and Gemini support multimodal inputs natively. The technology is based on embedding models that translate different media types into a shared vector space.

The practical significance shows in concrete scenarios: a tradesperson photographs a broken component and asks the AI for the part number. A user speaks a question into their phone and receives an AI-generated answer with images and video clips. Google is increasingly integrating multimodal results into its search result pages — AI Overviews can already combine images, lists, and structured data in a single summarizing answer.

For GEO, this means pure text optimization will no longer be sufficient in the future. Optimize your content for multiple media formats: use descriptive images with meaningful alt attributes, create explanatory videos, and use structured data for products and services. The more high-quality media formats you offer on a topic, the more entry points you create for multimodal AI systems. Consistency across media is especially important — when your text, images, and videos convey the same message, it strengthens the overall relevance of your content.