Multimodal AI

Multimodal AI refers to artificial intelligence systems capable of processing, understanding, and generating multiple types of content—including text, images, audio, and video. This capability is increasingly important as AI search evolves beyond text-only interactions.

Major Multimodal AI Systems:

Google Gemini

• Native multimodal design
• Text, image, audio, video
• Powers Google products

GPT-4 Vision (OpenAI)

• Image understanding
• Text and image input
• Available in ChatGPT

Claude 3 (Anthropic)

• Image analysis
• Document understanding
• Code and diagrams

Multimodal Search Scenarios:

• Users upload images to ask questions
• AI analyzes screenshots for context
• Visual search for products
• Image-based troubleshooting

Why Multimodal Matters for GEO:

Image Optimization

• Descriptive alt text for AI understanding
• High-quality product images
• Diagrams and infographics
• Screenshots with context

Video Optimization

• Accurate transcripts
• Chapter markers
• Descriptive titles and descriptions
• Thumbnail optimization

Document Optimization

• Accessible PDFs
• Clean formatting
• Extractable text
• Logical structure

Multimodal Content Strategy:

Include Rich Media

## How to Set Up [Feature]

[Step-by-step text instructions]

![Screenshot showing the settings page with callouts](/images/setup-screenshot.png)
Alt: Settings page showing the GEO configuration panel with options for crawler access highlighted

Alt Text Best Practices

• Describe what the image shows
• Include relevant keywords naturally
• Provide context for understanding
• Be specific but concise

Future Considerations:

• Voice search optimization
• Video content creation
• Interactive content
• AR/VR experiences

As AI becomes increasingly multimodal, optimizing visual and audio content becomes essential for comprehensive AI visibility.