Multimodal Search
In one line
Multimodal search lets users query with images, audio or video alongside text — the new entry channel created by 'snap a photo and ask' user behaviour.
Going deeper
Multimodal search lets users query with photos, voice clips or video instead of (or alongside) text. Google Lens, Circle to Search, ChatGPT's image attachments and Perplexity's image upload are the canonical examples. Snapping a shelf, an ad screenshot or a receipt and asking 'what is this?' is now an everyday flow.
For brands, this means visual assets become an entry point in their own right. Packaging, logos and storefront photos have to be identifiable by AI, and the information served after identification — official page, pricing, where-to-buy — has to be clean and easy to surface.
Implementations vary widely by surface. Some read words off the image, others lean on object detection plus a knowledge graph. There is no single playbook; the practical move is to test the major surfaces and see how your brand gets identified and cited per platform.
Related terms
Multimodal Model
A multimodal model is an LLM that can take in and reason over more than just text — typically combining images, audio or video alongside written prompts.
LLMVLM
A VLM (Vision-Language Model) is trained to reason over images and text together — the technology behind AI looking at your product photos, logos and shelf shots, not just your copy.
LLMRAG
RAG (Retrieval-Augmented Generation) lets an LLM fetch external documents at answer time and ground its response in them — the technique behind ChatGPT Search, Perplexity and most AI search products.
LLMHybrid Search
Hybrid search combines keyword (BM25) and vector retrieval to get the best of both — the default retrieval shape behind Perplexity-style answer engines and most production RAG.
GEO·AEOAI Overview
Google AI Overviews is the AI-generated summary that appears above the standard results in Google Search — one of the most prominent zero-click surfaces today.
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