Multimodal AI

Multimodal AI refers to artificial intelligence systems that can understand and work across multiple data types, or modalities, simultaneously. Unlike traditional AI models that specialize in a single modality (text-only or image-only), multimodal models can process text, images, audio, video, and other data types within a unified architecture, enabling richer and more human-like understanding of information.

Understanding Modalities

Common AI Modalities

Modality	Data Type	Example Input	Example Output
Text	Written language	Documents, queries, code	Articles, summaries, code
Image	Visual data	Photos, charts, screenshots	Generated images, diagrams
Audio	Sound data	Speech, music, recordings	Voice responses, transcriptions
Video	Moving visual data	Clips, presentations, streams	Video summaries, highlights
Structured Data	Tables, databases	Spreadsheets, CSV files	Charts, analysis

Cross-Modal Capabilities

The power of multimodal AI lies in its ability to work across modalities.

• Image to text - Describing what is in a photograph
• Text to image - Generating an image from a written description
• Audio to text - Transcribing speech to written text
• Text + image to text - Answering questions about a visual
• Video to text - Summarizing video content

Major Multimodal AI Models

GPT-4o

OpenAI’s GPT-4o (the “o” stands for “omni”) processes text, images, and audio natively within a single model. It can analyze charts, read handwritten text, describe photographs, and engage in voice conversations.

Gemini

Google’s Gemini models are built from the ground up as multimodal systems. Gemini can process text, images, audio, video, and code, making it one of the most broadly capable multimodal models available.

Claude

Anthropic’s Claude models can process text and images, enabling document analysis, chart interpretation, and visual question answering alongside standard text capabilities.

Open-Source Multimodal Models

• LLaVA - Open-source vision-language model
• Whisper - OpenAI’s open-source speech recognition model
• Stable Diffusion - Open-source image generation

Multimodal AI in Search

How Multimodal Search Works

Traditional search engines index and retrieve text-based content. Multimodal AI search extends this to visual and audio content.

Search Type	Input	Retrieved Content	Example Platform
Text-to-text	Text query	Text documents	Traditional search engines
Text-to-image	Text query	Relevant images	Google Images, CLIP-based search
Image-to-text	Uploaded image	Text descriptions and related pages	Google Lens
Image-to-image	Uploaded image	Visually similar images	Pinterest, reverse image search
Audio-to-text	Voice query	Text results	Voice assistants

Google’s Multimodal Search Features

Google has been steadily integrating multimodal capabilities into its search experience.

• Google Lens - Search by pointing your camera at objects
• Multisearch - Combine text and image queries
• AI Overviews - Synthesize information from text and visual sources
• Circle to Search - Highlight anything on screen to search for it

Content Types in a Multimodal World

Optimizing Beyond Text

As AI systems become multimodal, content optimization must extend beyond text to cover all the modalities through which your information might be discovered.

Image Optimization

• Use descriptive, keyword-rich alt text for all images
• Include relevant captions that add context
• Ensure images are high quality and properly compressed
• Use informative file names rather than generic ones
• Create original diagrams and charts that illustrate key concepts

Video Optimization

• Provide accurate transcripts and captions
• Write detailed video descriptions with relevant terms
• Use chapter markers and timestamps for longer videos
• Include key information in both visual and spoken form

Audio Optimization

• Offer full transcripts for all audio content
• Use clear, well-structured spoken language
• Include show notes and summaries for podcasts
• Tag audio content with relevant metadata

Structured Visual Content

Tables, charts, infographics, and diagrams are increasingly valuable in a multimodal AI landscape. Multimodal models can read and interpret these visual elements, extracting data and insights that complement textual content.

Visual Format	AI Readability	Optimization Tip
HTML Tables	High	Use clear headers and structured markup
Charts/Graphs	Medium-High	Include data labels and descriptive titles
Infographics	Medium	Supplement with text descriptions
Screenshots	Medium	Add alt text with full context
Handwritten notes	Low-Medium	Provide typed transcriptions

The Future of Multimodal AI

Emerging Capabilities

• Real-time video understanding - AI that can watch and respond to live video streams
• Spatial understanding - AI that comprehends 3D environments and physical spaces
• Multimodal generation - Models that generate text, images, audio, and video simultaneously
• Embodied AI - Multimodal models integrated into robots and physical devices

Implications for Content Strategy

As multimodal AI matures, the surface area for content discovery expands significantly. Content that exists only as text misses opportunities to be found through visual, audio, and video search. A comprehensive content strategy must consider all modalities.

Why It Matters for AEO

Multimodal AI is expanding the definition of what counts as searchable, citable content. AI answer engines are no longer limited to retrieving and synthesizing text; they can analyze images, interpret charts, process audio, and understand video. This means that every modality in which your content exists is a potential entry point for AI citation.

For AEO practitioners, multimodal optimization means ensuring that images have descriptive alt text, videos have accurate transcripts, charts have clear labels, and all non-text content is accompanied by rich textual context. As AI answer engines develop stronger multimodal capabilities, content that is optimized across modalities will have a significant visibility advantage over text-only pages.

Genrank helps you monitor your content’s visibility across AI answer engines, providing insights into how both textual and visual content is being discovered and cited in AI-generated responses.