Multimodal search, which combines text, images, audio, and other data formats, is becoming the new standard for digital products. With the evolution of AI and neural networks, users can now search for information more intuitively.
For example, they can upload a photo instead of a text query or use voice commands. However, this leads to a conflict of approaches to UX design. On the one hand, there is a growing demand for universal interfaces that can handle different input types, and on the other, personalization is required for specific use cases.
How can designers adapt to these changes? Which principles remain key, and which ones should be abandoned? What should an AI development company pay attention to when building this feature?
In this blog post, I will explain how multimodal search is changing the user experience and what challenges it creates for UX specialists.
Let’s start!
What is Multimodal Search and How Does it Work?
Multimodal search is a technology that allows users to find necessary information with the help of different types of data (modality) simultaneously or in different queries. Unlike traditional text search, it supports:
- Text (queries, keywords)
- Images (photos, screenshots)
- Audio (voice queries, sound fragments)
- Video (frame or subtitle analysis)
- Geodata (search by location)
How does it work exactly?
Technically, just like a traditional search: A user sends a query, a search engine looks for the suitable answers among the content and then shows them to you, from the most relevant to the least relevant.
But there’s an AI tweak to it. The user can send a query in any format they need. For example, a photo of a product or a voice question. Then, neural networks analyze the content, extract the meaning, and translate it into a machine-readable format. The system searches for matches in databases, taking the context into account, and finally, the user receives a relevant answer.
There are plenty of real-life examples that are already popular. For instance, Google Lens can search for information by photo and Shazam recognizes the music based on audio fragments.
Multimodal search makes interaction with technology more natural, but requires UX designers to think through interfaces that are convenient for different data entry scenarios.
What’s Fueling the Shift?
But why exactly are people abandoning plain text search? Well, it’s a natural response to how people interact with the world. The hardware is evolving, the user expectations are changing, and the tech is getting more versatile. All these factors contribute to the shift. And if you are a machine learning development firm that wants to stay ahead of the competition, you need to take them into account.
Here’s what’s driving the change:
- The availability of cameras and microphones: Smartphones, smartwatches, laptops, and even refrigerators now come equipped with cameras and microphones. That means two things: 1) Users are already multimodal. They speak to their phones, snap photos of products, and record videos to share their thoughts. 2) Search is no longer confined to typing. Users want to show, tell, or even gesture what they’re looking for.
- AI models now understand more than words: A new wave of AI models can process and relate different types of data. For example, models like Gemini by Google and ImageBind by Meta can recognize the concept of “a red jacket with gold buttons” whether it’s typed, spoken, or shown in a photo.
- Consumer expectations are changing: People expect fast, intuitive, and intelligent responses. Thanks to multimodal experiences like Google Lens or Siri, users now believe they should be able to search with whatever’s easiest at the moment—voice, photo, or even context.
- Use cases demand it: Multimodal search is becoming not just “nice to have” in many industries and real-world contexts. For example, with the help of AR shopping, you snap a picture of a chair you have and get matching furniture suggestions. Or a more important side: Users with disabilities can use speech, touch, or camera-based input instead of typing.
UX Implications: Designing for Inputs you Don’t Control
Traditional UX design revolves around predictable interactions: The user types something into a search bar, you validate it, and then show results based on a clear query. Easy, right? Multimodal search flips that simplicity on its head. Now you’re designing for unpredictable inputs—photos taken in bad lighting, voice commands in noisy environments, or gestures captured from odd angles.
What does that mean for UX design?
1. You’re not designing the input. You’re designing the recovery.
You can’t force users to take the “right” photo or say the “perfect” phrase. So design for imprecision (users might upload an image of a messy room to find a specific chair and the system might get it wrong) and clear recovery paths (offer fallback suggestions, clear ways to refine results, and loop back into the flow). The focus shifts from perfect input design to resilient output UX.
2. Context switching is the new normal
A single query now might involve:
- Taking a photo that ends up messy.
- Then saying a product name with a ton of background noise
- Finally, typing a clarifier like “in green”
So the interface needs to handle fluid transitions between input modes without overwhelming the user. To do that, you should keep prior inputs visible and let users easily edit/remove past inputs. Also, use consistent design elements across modes so transitions feel natural.
3. Show what the AI thinks you meant
Because the system might misinterpret inputs, transparency is key. Show users what the model “saw” in an image (bounding boxes, tags, concepts) or how it interpreted voice input (transcribed text, identified keywords). This not only builds trust, but it also gives users control over the interaction.
4. Design for edge cases
With multimodal inputs, there’s a huge variety of “imperfect” scenarios like blurry images or slang in audio. Designing for multimodal UX means testing for these edge cases and ensuring users never hit a dead end.
5. Help users learn the system
Multimodal search is still new for many users. They don’t always know what the system can handle. UX should offer subtle cues and use example queries or demos. And remember: You’re no longer designing search inputs. You’re designing search conversations.
To sum up: Best Practices for Multimodal Search Design
So, how can you make sure your user experience is top-notch? You can implement the following best practices:
- Let users switch between voice, image, and text mid-query without friction.
- Preview and confirm inputs before processing.
- Display what the system understood, especially with ambiguous or noisy inputs.
- Provide smart suggestions and refinement options.
- Don’t let the experience collapse when inputs fail.
- Include onboarding and hints, especially on mobile.
- Ensure accessibility across input types.
With all these in place, the multimodal search experience in your software solution will bring satisfaction to users and help you turn them into your loyal champions.
