What changed
For years, product discovery meant a search results page or a scroll through Instagram. Now a growing share of shoppers type a full question into Gemini, ChatGPT or Google’s AI answers: “best vitamin C serum for oily skin under ₹800” or “leather laptop bag that fits 15 inch”.
The answer is a short list of products with reasons. If your product isn’t on that list, the buyer never sees your ad, your store or your offer.
An AI assistant can only recommend a product it can describe with confidence.
What AI assistants read
They don’t look at your banners. They read text and structured data:
- Product titles and descriptions on your own store
- Structured data: price, availability, size, material, ratings
- Your Google Merchant Center feed
- Reviews and questions, on your store and on marketplaces
- What other sites say about your brand
Most D2C product pages lead with mood and keep the facts in images. Text like “fits a 15 inch laptop” or “for oily skin” is often only inside a JPG, where an assistant can’t read it.
Five fixes to make first
| Fix | Why it matters |
|---|---|
| Write the facts as text | Size, material, skin type and use case, in plain words on the page. |
| Complete your product structured data | Price, stock, ratings and variants in a form machines can read. |
| Clean up the Merchant Center feed | Titles and attributes that match how buyers ask. |
| Answer real questions on the page | A short FAQ per product, in your buyers’ own words. |
| Keep marketplace listings consistent | The same facts on Amazon, Flipkart and your store. |
None of these need a redesign. They’re content and data work, and they also help classic SEO and conversion, because human buyers want the same facts.
How to tell if it’s working
Ask the assistants the questions your buyers ask, and keep a simple log of which brands appear. Watch branded search and direct traffic in your analytics, and check that your structured data shows no errors.
It moves slower than ads, so we review it monthly, not daily.