How to Optimize for Multimodal AI Search: Text, Image, and Video All-in-One

Search is no longer limited to text. With the rise of multimodal AI search, users are discovering information through a blend of text, image, video, and voice. Platforms like ChatGPT, Google SGE (Search Generative Experience), Perplexity, and Gemini are now capable of processing different media types at once, and delivering a single, unified answer.

This is a significant shift for marketers. In the past, optimizing for a single keyword or text-based query might have been enough. Today, ranking in AI search means ensuring your text, image, and video assets are AI-readable, citation-friendly, and structured in a way that aligns with how large language models interpret content.

Brands that get ahead of this curve can dramatically increase their AI search visibility, secure citations in high-value answers, and ultimately generate higher-quality traffic.

What is Multimodal Search?

Multimodal search is the process where AI combines text, images, video, and sometimes audio to understand user intent and provide more accurate answers. Instead of pulling from a single content format, the AI synthesizes different media types to deliver richer, more contextual results.

For example, a user might ask ChatGPT, “Show me how to repot a fiddle leaf fig.” The answer could include a text explanation, an illustrative image, and a linked video tutorial, all within one response.

This marks a dramatic departure from traditional keyword searches, where users typed a phrase and scanned a list of blue links.

Why It Matters for Modern SEO

In 2025, multimodal search is becoming the new default. Users increasingly expect a single, high-quality answer, not pages of search results. This means:

• Your content formats must align with how AI delivers information.

• Text alone isn’t enough to secure visibility.

• Rich media improves your chances of being cited in AI answers.

AI search visibility is also becoming measurable. Platforms like Passionfruit Labs track AI citations, giving marketers insights into when, where, and how their content is appearing inside ChatGPT, Perplexity, or SGE answers.

How Multimodal AI Search Works

How AI Processes Text, Images, and Video Together

Large language models and AI search engines work by breaking down inputs into semantic components. Text helps define context. Images provide visual clarity. Videos deliver instructional value. AI combines these layers to:

• Understand intent more deeply.

• Choose the most relevant content format to display.

• Generate answers that feel complete and trustworthy.

Text might provide the written “how,” but visuals make it more engaging, and video often seals the deal by showing “how to” in real time.

Key Platforms Leading the Shift

The key players driving multimodal search include:

• Google SGE – integrating image and video into AI-generated overviews.

• ChatGPT (Browse + Vision) – capable of reading text, interpreting images, and referencing media-rich pages.

• Perplexity – favoring concise answers enriched with citations and media context.

• Gemini and Copilot – leveraging multimodal comprehension for interactive queries.

These platforms reward structured, multimedia-rich content with higher citation rates and greater visibility in their answers.

Core Optimization Strategies for Multimodal AI Search

1. Optimizing Text Content for AI Search

Text remains the foundation of multimodal visibility. To make your content more AI-friendly:

• Use clear headings, structured subheadings, and concise intro summaries.

• Implement schema markup and structured data for articles, FAQs, and product pages.

• Use conversational language that mirrors how people speak their queries aloud.

• Summarize key points at the top of your article, AI models love snippet-ready text.

You can also learn how to track AI referrals using GA4 with this Passionfruit blog, which shows how to see where your content appears in AI engines like ChatGPT and Perplexity.

2. Optimizing Visual Content (Images)

Images play a big role in how AI understands content. An image with proper context can make your article more “citation worthy” for AI summaries. To optimize:

• Always use descriptive alt text that clearly explains what’s in the image.

• Add metadata and captions to give AI engines more context.

• Use file names that match the search intent (e.g., “repotting-fiddle-leaf-fig.jpg” rather than “IMG_3476.jpg”).

• Use structured image data when possible.

High-quality visuals are more likely to be pulled into AI answers, especially in queries where visual demonstration matters.

3. Optimizing Video Content for AI Search

Video optimization is often overlooked, but it can be a major differentiator in AI rankings:

• Add accurate transcripts and captions so that AI models can read and index the content.

• Ensure your video titles and descriptions use natural language and match conversational queries.

• Use timestamps and chapters for tutorials, AI engines often cite these directly.

• Embed videos alongside articles to create multimodal clusters on the same page.

By connecting your text and video content, you increase the chances of being cited in AI search over a competitor that only has one format.

Leveraging AI Tools to Track and Measure Visibility

Using GA4 and Looker Studio for AI Traffic Attribution

AI referrals often appear as “direct traffic” in analytics, which means many brands are underestimating their AI visibility. By setting up GA4 custom events and Looker Studio dashboards, you can:

• Identify traffic from AI engines like ChatGPT and Perplexity.

• Track engagement time and conversion behavior.

• Compare AI-driven traffic against traditional organic search.

This approach helps connect AI visibility to real business outcomes.

Advanced Measurement with Passionfruit Labs

Passionfruit Labs takes AI search tracking even further. It lets marketers:

• See which pages are cited inside AI answers.

• Identify which format, text, image, or video, drives the most visibility.

• Monitor changes in AI search ranking over time.

• Get clear data on how multimodal assets contribute to traffic and conversions.

This kind of AI visibility intelligence is becoming essential for SEO and content teams that want to win in the new search landscape.

Future-Proofing Your SEO Strategy for Multimodal Search

Aligning E-E-A-T With All Media Types

E-E-A-T : Experience, Expertise, Authoritativeness, and Trustworthiness - still matters, but now it applies across all formats:

• Add author bylines and credentials for articles.

• Use reputable external links in text content.

• Include original photos or videos where possible to demonstrate credibility.

• Keep content updated and factually accurate.

These signals make your content more appealing to both AI and human audiences.

Preparing for Voice + Image + Video Convergence

Voice search is merging with image and video search. A user might ask a question aloud, receive a spoken answer, see an image or video, and then act, all within seconds.
To stay ahead:

• Optimize for conversational search.

• Use structured data across formats.

• Make your content fast, clean, and crawlable.

• Track AI citations and voice referrals using the same strategy.

Conclusion: The Future Is Multimodal

Text SEO alone can’t win in the age of AI. Brands that optimize across text, image, and video will dominate AI-driven visibility. This is not just about ranking on Google, it’s about becoming the source AI trusts to answer user queries.

Multimodal AI search rewards:

• Structured, citation-ready content.

• Media-rich pages.

• Strong technical SEO and tracking infrastructure.

Platforms like Passionfruit Labs are giving marketers the ability to see their true AI footprint, not just where they rank in search engines, but where they’re mentioned and cited inside AI results.

The brands that adapt to this reality now will own Position Zero, voice search, and multimodal visibility for years to come.

Key Takeaways

1. AI search is evolving into a multimodal experience.

2. Text alone isn’t enough, images and videos matter too.

3. Structured metadata and schema boost AI comprehension.

4. Voice and visual search are merging into one experience.

5. Tracking AI visibility requires tools like Passionfruit Labs.

6. Brands that master multimodal SEO will dominate AI search results.

FAQs

1. What is multimodal AI search?

Multimodal AI search combines text, image, video, and voice to deliver richer, more contextual answers instead of relying on just one format.

2. Why should brands optimize for multiple content formats?

Because AI engines like ChatGPT and SGE prioritize diverse media sources. Optimizing across formats increases visibility, trust, and engagement.

3. How can I track performance in multimodal AI search?

You can use GA4 for basic attribution and advanced tools like Passionfruit Labs to monitor where your content appears in AI search engines.

4. How is image and video optimization different from text SEO?

While text relies on structured content and keywords, image and video optimization depends on alt text, metadata, captions, transcripts, and contextual relevance.

5. What’s the best way to prepare for future AI search trends?

Adopt a hybrid SEO strategy, optimize for text, voice, image, and video, and use visibility tools to measure performance in AI-driven search.