Multi-Modal Search

Written by Cobus van der Westhuizen, reviewed by Wynand van der Westhuizen, fact-checked by Lenata Oosthuizen. Last reviewed July 2026. Editorial policy.

What Is Multi-Modal Search?

Multi-modal search refers to search technology that accepts and processes multiple input types simultaneously. Rather than relying solely on a typed keyword, a user can combine a photograph with a spoken question, or upload an image alongside a text query, and the search engine interprets all inputs together to generate a single, contextualised result.

Google Lens is the most widely used example in South Africa.

A consumer can photograph a product in a shop window and ask "where can I buy this cheaper in Pretoria-" Google's AI then combines the visual data from the image with the location-aware text query to return relevant shopping results.

Google's AI Mode and Gemini-powered search extend this further, allowing users to ask follow-up questions mid-session without restarting the search.

For SEO professionals, multi-modal search changes the optimisation landscape significantly. Images, videos, and audio content now carry direct discovery value.

A product image that is properly labelled, sized, and described with structured data can appear in visual search results independently of any text on the page.

Alt text, image schema markup, and descriptive filenames are no longer optional niceties; they are direct ranking signals in a multi-modal world.

Multi-modal capabilities are driven by large vision-language models that can process text tokens and image embeddings within the same neural network.

Google's Gemini, OpenAI's GPT-4o, and Meta's Llama models all support multi-modal inputs, meaning AI assistants and AI-powered search engines are increasingly able to interpret visual context.

South African brands operating in visually-driven sectors, including fashion, automotive, real estate, and food and beverage, stand to benefit most from proactive multi-modal optimisation.

Multi-Modal Search In Practice

The scenario below is an illustrative example, not a Juicy Designs client result. The outcome described indicates the scale of effect that multi-modal search optimisation typically produces, so treat it as indicative rather than measured.

Imagine a Pretoria-based furniture retailer whose analytics review might show that a meaningful share of its traffic is arriving via Google Lens, driven by shoppers photographing items in competitor showrooms.

By auditing its product image library and adding structured data with Product schema, image sitemaps, and descriptive alt text, the retailer could plausibly start appearing in visual search results for category queries.

Its images might then surface not only in standard image search but within AI Overviews and the Google Shopping visual feed.

On the paid side, multi-modal awareness shapes campaign creative strategy. As AI search interfaces increasingly incorporate visual results alongside text answers, brands that invest in high-quality, well-labelled visual assets gain share of the emerging AI search result page.

This means photography standards, file naming conventions, and image structured data become marketing responsibilities, not just technical ones. For South African businesses, the shift also reinforces the importance of Google Business Profile photos and video content, both of which feed into local multi-modal queries.

What multi-modal search is

Multi-modal search is search that works across multiple modes or types of input and content, such as text, images, and voice, allowing users to search using more than one modality (for example, combining an image with a text query) and enabling search engines to understand and draw on different content types together. Rather than search being limited to typed text, multi-modal search embraces multiple ways of searching and multiple content types: searching with an image (visual search), searching by voice, and, increasingly, combining modalities, such as taking a photo and asking a question about it in the same search, so the search engine understands both the image and the text together to answer. This reflects advances in AI that let search engines understand images, voice and text together, and it is part of the evolution towards more natural, flexible, AI-powered search (Google's MUM model, for instance, was designed with multimodal understanding in mind, and features like Google Lens enable image-based and combined image-plus-text search). Multi-modal search matters because it expands how people can search, beyond typed keywords to images, voice and combinations, and how content is understood, drawing on visual and other content, not just text, which broadens the ways users find information and the ways businesses can be found. Understanding multi-modal search matters because search is increasingly multi-modal, supporting image, voice and combined searches through AI that understands multiple content types, so knowing what multi-modal search is, search across text, image, voice and combinations, helps a business appreciate the evolving ways people search and the growing role of visual and other content (alongside text) in being found, which has implications for how comprehensively a business should present its content across modalities.

Multi-modal search and content

The rise of multi-modal search has implications for how a business presents its content, chiefly reinforcing the value of quality across content types, images as well as text, and of the same clear, comprehensive, well-optimised content that serves search generally, since multi-modal search draws on multiple content types through AI understanding. The practical implications include: paying attention to visual content and its optimisation, since image and combined image-plus-text search means images matter for being found, so having quality, relevant images that are well-optimised (with descriptive file names, alt text, and context, which help image search and understanding) supports visibility in image-based and multi-modal search; continuing to provide clear, comprehensive, well-structured text content, since text remains central and multi-modal search still draws heavily on text understanding, and clear content that answers questions serves multi-modal and AI search as it serves search generally; and ensuring content is genuinely useful and well-optimised across the board, since multi-modal, AI-powered search rewards the same quality, relevance and clarity that underpin all modern SEO, now applied across content types. There is no separate multi-modal optimisation trick, rather, the implication is to ensure quality and optimisation across the content types multi-modal search uses (good, optimised images alongside clear, comprehensive text), and to recognise that as search understands more modalities, presenting quality content across them supports being found. On which search engines support multi-modal search, the major search engines and AI systems are increasingly building multi-modal capabilities, Google notably supports image and combined image-plus-text search (through features like Google Lens) and has developed multimodal AI (like MUM), and AI assistants and answer engines increasingly handle images and voice alongside text, so multi-modal search is a growing capability across major search and AI platforms rather than confined to one, reflecting the broad industry move towards AI-powered, multi-modal search. For a South African business, the implications of multi-modal search are to ensure it presents quality content across modalities, well-optimised, relevant images (with descriptive alt text and context) alongside clear, comprehensive text content, so it can be found through image, voice and combined searches as well as text, and to recognise that the same content quality, relevance and optimisation that serve search generally also serve multi-modal search, applied across content types. Because multi-modal search draws on multiple content types through AI understanding, and rewards quality across them, the sound approach is to ensure genuinely good, well-optimised content across text and images (and to consider voice-friendly, question-answering content), which positions a business to be found as search becomes increasingly multi-modal, so understanding multi-modal search reinforces the value of comprehensive, quality content across modalities rather than requiring a separate optimisation approach.

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