Multimodal Search

Search that accepts and combines multiple types of input, such as text, images, video or voice, rather than text queries alone.

Category:

AI Engines

Last update:

August 31, 2026

Definition

Multimodal search lets a user search using more than one type of input, combining text, images, voice or video in a single query, and lets an AI system reason across those formats together instead of treating text as the only signal.

A common example is uploading a photo and asking a question about it, such as identifying a plant, comparing a product to similar items, or asking for repair instructions, features now available in Google Lens integrated with AI Mode, and in ChatGPT and Gemini's image understanding.

For GEO, this expands what counts as citable content: well-described product images, diagrams and videos, with clear alt text, captions and surrounding context, can now be a source an AI system draws on, not just body text.

This connects closely to conversational search, since a multimodal query is often the opening turn of a longer conversation, and to strong schema markup on images and products, which helps AI systems interpret visual content correctly.

Frequently Asked Questions

What counts as a multimodal query?

Any search that combines more than one input type, such as a photo plus a typed question, or a voice question about something on screen.

Which AI products support multimodal search today?

Google's AI Mode with Lens integration, ChatGPT's image understanding, and Gemini's multimodal capabilities are the most widely used examples.

How can content be optimized for multimodal search?

Through clear, descriptive alt text, captions, surrounding context, and structured data on images, products and videos, so an AI system can correctly interpret and cite visual content.

Does multimodal search replace text-based search?

No, it adds new input and reasoning types alongside text, expanding the ways users can search rather than replacing the existing ones.

Knowledge Graph

A structured database of entities (people, places, organizations, products) and the relationships between them, used by search engines and AI systems to understand and connect facts.

AI Overview

AI Overview is Google's AI-generated summary shown above traditional results, pulling from multiple web sources to answer a query directly.

Retrieval-Augmented Generation (RAG)

A technique that lets an AI model pull real, up-to-date information from an external source before generating its answer, instead of relying only on what it learned during training.

AI Hallucination

When an AI model generates information that sounds plausible and confident but is factually wrong or entirely made up.

AI Grounding

The process of anchoring an AI model's answer in verifiable, external data instead of letting it rely only on what it memorized during training.

Large Language Model (LLM)

An AI model trained on massive amounts of text to understand and generate natural language, the technology behind ChatGPT, Gemini, Claude and most AI search engines.