Definition
Multimodal search lets people search using more than one type of input and receive answers that draw on more than one type of content. A user might photograph a product, ask a spoken question or combine an image with text. AI models that can interpret images, audio and video make these searches possible.
For example, a shopper might photograph a Hydrakind moisturizer and ask an assistant for a similar fragrance-free option. The assistant identifies the product from the image, then looks for alternatives. Clear product images, descriptive alt text and accurate captions help Aarvi Skin's products be understood in searches like this.
Why it matters
As assistants get better at reading images and video, visual content becomes another way for your brand to be recognized, or missed. Product photos with clear labels, descriptive alt text, captions and transcripts give systems more information to connect with your brand. Behavior varies by assistant and is still developing, so the practical step is making sure your visual assets are accurately described in text as well.
What to do
- Add descriptive alt text and captions to product images and diagrams.
- Publish transcripts for videos and podcasts that explain your products.
- Make sure product packaging and images show your brand and product names clearly.
Common questions
What is an example of multimodal search?
Taking a photo of a product and asking an assistant where to buy it or what similar options exist is multimodal search. So is asking a spoken question and receiving an answer that includes images. Google Lens and the camera features in AI assistants are common examples.
How do I optimize for multimodal search?
Make your visual content understandable in text as well as images. Use clear product photography, descriptive file names, alt text, captions and structured data for products. Provide transcripts for video. These steps help both accessibility and machine understanding, though how each assistant uses them varies.