TL;DR: Multimodal search lets customers use text, voice, images, or video — often together — to find what they're looking for (instead of just typing keywords). This new approach to search changes how and where brands get discovered, since customers now jump between devices and search modes as they shop for goods and services.
Knowledge Center
Multimodal Search
Multimodal Search
What is multimodal search, and how is it transforming the way customers find brands? Plus, pick up strategies to get discovered when customers use multimodal search.
What is multimodal search?
Multimodal search refers to customers' ability to interact with search engines and AI tools using "multiple" types or "modes" of input, sometimes combining text, voice, images, and video modes in one search.
Multimodal searches represent a foundational shift away from traditional search, and they're changing how, where, and when discovery happens. Similarly, since search is no longer limited to a single device or just one mode ("Ask Alexa" or Google keyword searches), the shift to multimodal search is reshaping how brands must show up to be discovered in AI search.
Why is multimodal search so important?
Multimodal search blends input and intent. A customer can ask Siri, take a photo with Google Lens, or search Claude using a video — all before ever clicking a link. This new journey pushes marketers to rethink attribution, optimization, and content strategy: there's no going back to siloed, text-based queries or hunting and pecking through Google's blue links.
Example of a multimodal search
A man notices he has some sun damage on his face and arms, so he starts thinking about how to take better care of his skin. He conducts a multimodal search by combining a visual search using photos he uploads, text-based web searches, and voice queries while switching between his apps on his phone, laptop, and voice assistant.
Here's what a multimodal search looks like:
Device: Phone / Mode: Voice
He opens his Google app and does a voice search for "best men's sunscreen for sensitive skin and sun damage 2025" and finds a product he'll try in an article from Men's Health. He copies the link into his Notes app.
Device: Phone / Mode: Text
He also falls down a Reddit rabbit hole for starting with "best face serums for men 2025", watches more YouTube skincare videos than he'd like to admit, and discovers he can get a skincare analysis online, so…
Device: Phone / Mode: Image
He snaps clear, well-lit photos of his face and forearms, where he sees sunspots and texture concerns. Then, he uploads images to AI-driven skin analysis tools and converts as a customer for Thea Care and PerfectCorp, even though he didn't start his search with this intent. These apps analyze his photos, then recommend targeted skincare products based on the results, which drive him back toward his initial intent as he emails himself the results, and…
Device: Laptop / Mode: Text
He uses ChatGPT to compare the results and recommendations in the reports he got, then decides to look for a few products and also decides he wants to see a dermatologist in person, so….
Device: Amazon Alexa / Mode: Voice
He opens his notes app and tells Alexa to summarize the reviews from two products that caught his eye. Finally, he makes his first click and adds one of the sunscreens to his cart and orders it.
His journey continues when he asks Alexa to "ask Doctor Locator" to search for the top 3 dermatologists that take Optum insurance near me" and to text him their contact info. Then…
He wraps up this phase of his search with a voice note requesting a reminder to finish researching dermatologists tomorrow.
How do brands get discovered in multimodal search?
As multimodal search becomes more common, and as AI search continues to shift customer expectations and the entire search landscape, brands have to rethink content strategy to include data strategy.
There are a few key data strategy elements brands must understand so they can adapt to multimodal search — and adapt quickly:
#1 Retrieval-Augmented Generation (RAG) systems make multimodal search results relevant and conversational
RAG is like having an AI that can look things up in real time while answering your questions, instead of just guessing or pulling from memory (old indexed pages). RAG systems search through text, images, video, and audio ("What song is this, Siri?"). The systems are looking for facts, and they use those facts (or what they suppose are facts) to answer questions in a conversational way.
#2 Knowledge graphs help RAG surface what's true, so customers get good information
Tools like knowledge graphs provide a space to organize and connect facts about brands, products, providers, etc. They help AI "think" about the connections between all the entries in the knowledge graph and understand the knowledge graph's world of data.. When managed properly, a knowledge graph pushes factual, on-brand, up-to-date information to RAG systems and shows the RAG system how the brand information fits together.
#3 Structured data is the map within a knowledge graph, so multimodal search results stay relevant
Think of structured data as a very, very detailed map describing the world of data living in the knowledge graph. Structured data is how you keep your brand information "real" and up to date. If the individual data points on the map aren't accurate and if the connections between all the data points are out of date, the knowledge graph won't share the right information with external apps, publishers, traditional search, and AI search platforms.
#4 Schema Markup works like labels on the map, so multimodal search results are context-rich and engaging for customers
Schema Markup, or Schema, is a standard set of rulesand labels used to "markup" web pages and the data on them, so search engines and AI tools know exactly what each part means. Schema indicates things like "this is an image" and "this is what product is being shown." These labels help AI give context-rich, direct answers to customers because they help the AI tools understand what content is where, what it's about, how it's prioritized on a website or web page, etc.
Despite the standard and Schema's long-recognized value in SEO, it's often overlooked in brand, data, and content strategy. This makes Schema Markup an even more valuable opportunity for brands to optimize for AAIO, AEO, ASO, GEO, and LLMO.
How has multimodal search changed search and discovery?
Multimodal search, like AI search and AI agents, is a funnel disruptor. It's quickly becoming the norm as search shifts from a single-path, linear journey to a multi-path journey that blends awareness, consideration, and conversion into a layered, multi-platform search experience.
Similarly, multimodal search is also changing the way brands understand visibility — it's making visibility strategy harder:
Traditional SEO and reporting don't work here in multimodal search reporting.
Brand discovery is no longer trackable through one dashboard — it's fractured across formats and devices.
Voice inputs, image-based prompts, and AI agents sidestep the classic funnel entirely.
Marketers need a modern strategy for visibility that accounts for search inputs they can't see and a customer journey that's different for every single customer.