What is Multimodal Search?
Search spanning text, images, voice, and video — AI assistants increasingly answer from and with non-text content.
Definition
Multimodal search is querying and answering across media types: asking with a photo (Google Lens, ChatGPT vision), voice queries, and answers that include images, videos, and products. Modern assistants are multimodal by default — they can read a screenshot of your pricing page as easily as your HTML.
Why it matters
Answer surfaces increasingly include visual results — product carousels, video citations, image packs — and assistants parse visual content when text is absent. For visibility this means alt text, descriptive filenames, image schema, video transcripts, and captions are retrieval surfaces, not accessibility chores. A chart with no textual restatement of its numbers is data invisible to most retrieval pipelines.
Frequently asked
How do I optimize images and video for AI search?
Give every meaningful asset a text shadow: alt text stating the finding (not 'chart'), transcripts for video, captions with the key numbers, and schema (ImageObject, VideoObject) tying them to the page's entities.
Do AI assistants cite videos?
Yes — YouTube citations appear regularly in AI answers, especially for how-to prompts. Transcripts make the content retrievable; chapters make it quotable.
Related terms
- Meta DescriptionThe HTML summary of a page. Rarely a ranking factor, but a parsing aid for machines and the pitch that wins the remaining clicks.
- AI CitationsThe linked sources an AI assistant credits when generating an answer. Being cited is the AI-era equivalent of ranking #1.
- AI CrawlerBots operated by AI companies — GPTBot, ClaudeBot, PerplexityBot, Google-Extended — that fetch web content for model training or live answer retrieval.
- AI OverviewsGoogle's AI-generated summaries shown above traditional results, now appearing on roughly half of searches and dramatically reducing clicks to websites.