Most Indian brands are optimising for text. Meanwhile, their customers are searching with photos of sarees, screenshots of products, and voice queries in Hinglish. If your digital strategy doesn't account for multimodal search, you're already behind — and the gap is widening fast.

Multimodal AI search — where users query using images, audio, video, or a combination — is no longer a Western curiosity. It's hitting Indian markets hard, driven by three forces: the cheap-data economy, a mobile-first population that defaults to camera and voice over keyboard, and Google's aggressive rollout of AI Overviews and Lens integration across Search.

What's Actually Happening in Indian Search Right Now

Google Lens processes billions of queries monthly. A significant chunk of that activity is in India — people photographing price tags at kiranas, scanning menus at dhabas, pointing cameras at medicine packaging to find cheaper generics. This isn't fringe behaviour. This is mainstream Indian mobile usage.

Simultaneously, Gemini's multimodal capabilities are being baked deeper into Google Search. When someone asks Gemini to "find me something similar to this" and uploads an image, the AI doesn't just do a reverse image search — it reasons about the object, finds contextually similar products, and surfaces brands that have structured their content so the AI can understand and recommend it.

Most Indian brands have done none of that work.

The Three Layers of Multimodal Search Indian Brands Are Missing

Let's break this down practically:

  • Visual discovery: Google Lens, Pinterest Visual Search, and AI-powered product discovery on eCommerce platforms like Meesho and Flipkart. If your product images aren't optimised with alt text, structured data, and accurate filenames, you don't exist in visual search.
  • Audio and voice query processing: Users speaking in Hinglish, regional languages, or code-switched queries. "Ek achha budget phone under 15000" is a real query pattern. AI search engines are getting very good at parsing this. Your content needs to reflect how Indians actually speak, not how brands think Indians should write.
  • Video as search input: YouTube's multimodal search and Google's ability to index spoken content in videos means a well-structured video with accurate transcripts and chapter markers is now a search asset, not just a social one.

Why This Hits Indian Brands Harder Than Western Ones

Western brands have had structured content practices baked in for years — schema markup, image SEO, video transcripts. Indian brands, especially SMBs, largely skipped that infrastructure. When traditional text search was the game, you could get away with it. Multimodal AI search exposes every gap.

Consider an Indian D2C brand selling ethnic wear. Their Instagram is gorgeous. Their website? Images named "DSC_1047.jpg", no alt text, no product schema, no size guide structured in a way any AI can parse. When a user photographs a lehenga and asks Gemini "find me something like this under ₹8000," that brand is invisible — not because their product is wrong, but because their content infrastructure is broken.

This is a structural problem, not a creative one. And it's fixable.

What Multimodal Readiness Actually Looks Like

Here's a practical checklist for Indian brands to audit their multimodal search readiness:

  • Image SEO fundamentals: Every product or service image needs a descriptive filename ("blue-silk-banarasi-saree-500g.jpg" not "IMG_034.jpg"), an accurate alt tag, and where relevant, product schema (name, price, availability, image URL). This is table stakes.
  • Video transcript optimisation: If you're producing video content — and most Indian brands are, given Reels and YouTube Shorts — get accurate transcripts published. YouTube auto-captions are not enough. Timestamps and chapter markers help AI understand what each segment covers.
  • Voice-natural content structures: Write FAQ sections and how-to content the way people actually ask questions verbally. "What is the price of..." and "How do I..." are good. Dense paragraph prose with no question framing is invisible to voice-driven AI queries.
  • Structured data beyond basic schema: Product, HowTo, Recipe (if relevant), VideoObject, and ImageObject schema are all parseable by multimodal AI systems. Most Indian brand websites have none of these beyond a basic Organisation schema added once and forgotten.
  • Vernacular alt text: If you're targeting regional audiences — and if your customer base is outside metros, you should be — consider alt text and image titles that incorporate regional product naming conventions. A Kanjeevaram silk saree has different search vocabulary in Tamil Nadu versus Delhi.

The eCommerce Dimension: Where the Stakes Are Highest

For Indian eCommerce brands, multimodal search is not a future concern — it's a current revenue leak. Google Shopping now surfaces visually matched products in AI Overviews. Flipkart and Amazon India are both investing in visual search within their own apps. If your product feed, images, and metadata aren't structured correctly, you lose placement in these surfaces.

The fix for eCommerce specifically involves three things: a clean, complete product feed with accurate attributes, high-resolution images from multiple angles (AI models trained on visual similarity need variety), and product descriptions that use natural language, not keyword-stuffed gibberish.

Our eCommerce marketing services at Hriyan Digital specifically address product feed optimisation and structured data implementation — because we've seen how directly this affects AI-driven discovery for Indian brands.

The AI Search Infrastructure Argument

Here's the bigger picture argument: multimodal search is just the most visible symptom of a deeper shift. AI search engines — whether Google with Gemini, Microsoft with Copilot, or standalone tools like Perplexity — are increasingly reasoning engines, not matching engines. They don't just find pages with matching keywords. They understand context, modality, and intent together.

This means the old SEO playbook of targeting keywords on text pages is necessary but not sufficient. You need an AI-ready content infrastructure — one where your brand's information is structured, accurate, multimodal, and consistent across sources.

This is fundamentally what Generative Engine Optimisation (GEO) addresses — building the authority signals and structured presence that makes AI systems trust and cite your brand. Multimodal search readiness is one critical layer of that.

If you're not sure where your brand currently stands across these AI search surfaces, our free AI Visibility Checker is a useful starting point. It takes five minutes and usually surfaces at least one blind spot brands weren't aware of.

What Indian Brands Should Prioritise in the Next 90 Days

Don't try to fix everything at once. Here's a sequenced approach:

  • Week 1–2: Audit your top 20 product or service images. Fix filenames and alt text. Implement basic product or service schema where missing.
  • Week 3–4: Pull your top 5 YouTube or Reels videos. Add accurate transcripts or closed captions. Add chapter markers on YouTube.
  • Month 2: Rewrite your FAQ sections to reflect actual voice query patterns. Use Google Search Console's query data to find question-format queries you're already partially ranking for — and make your content answer them more directly.
  • Month 3: Implement VideoObject and ImageObject structured data. Test with Google's Rich Results Test. Fix errors before moving on.

Multimodal search in India isn't coming — it's here. The brands that treat their content infrastructure as a revenue-generating asset, not an afterthought, will be the ones AI systems recommend when someone points a camera at a product and asks where to buy it.

That could be your brand. But only if the infrastructure is there to make it possible.