Multimodal Search
Multimodal search is search that accepts and reasons over multiple input types — text, images, video, audio — together, powered by multimodal AI models like Gemini and GPT-4o.
In context
Multimodal search shifts the optimisation surface beyond text: image alt attributes and captions become more important (because the model can describe what's in the image), video transcripts and chapter markers matter (because the model can extract from video), and structured data describing media (ImageObject, VideoObject) helps the model reason about non-text content. Google Lens and 'circle to search' are mainstream multimodal entry points; ChatGPT and Gemini accept images directly. Optimise images and video as carefully as text.
A Toronto interior-design portfolio adds detailed alt text, captions, and ImageObject schema to every project image — Gemini begins surfacing the portfolio when users upload a 'find me a designer who does this' style reference image.
Have a question this didn't answer?
Book a free 60-minute strategy call. We'll explain how this concept applies to your specific situation.