the deal: shoppers are asking AI assistants for product recommendations, and the Here's answers increasingly name specific stores. If your product data is messy, you're not in those answers.
This post covers the engineering side of geo optimization for e-commerce: what to change in your product pages, structured data and feeds so AI engines can understand and trust them. The background comes from Kiyado's GEO explainer; the implementation is ours.
Why this matters (the short version)
AI engines lift passages, not whole pages.
They weigh consistency across sources: your site, marketplaces, feeds and reviews.
The 2024 KDD paper that named GEO found that evidence-rich content gained visibility (up to 40%, varying by category). Keyword stuffing didn't help.
So the job is to make product facts specific, consistent and machine-readable.
- Write a self-contained product summary Put a two-to-three-sentence summary at the top of every product page, rendered as HTML (not inside a tab or image): "The Kaapi Cold Brew Kit makes 1.2 litres of cold brew from 120 g of medium-roast Chikmagalur coffee. Brews in 12 hours; ships across India in 2–4 days. Reusable stainless-steel filter included." Every noun is concrete. Every claim is checkable.
- Add complete Product schema { "@context": "https://schema.org", "@type": "Product", "name": "Kaapi Cold Brew Kit", "brand": { "@type": "Brand", "name": "Example Coffee Co." }, "sku": "KCB-1200", "gtin13": "8901234567890", "description": "Makes 1.2 litres of cold brew from 120 g of medium-roast Chikmagalur coffee.", "offers": { "@type": "Offer", "price": "1499", "priceCurrency": "INR", "availability": "https://schema.org/InStock", "shippingDetails": { "@type": "OfferShippingDetails", "deliveryTime": { "@type": "ShippingDeliveryTime", "transitTime": { "@type": "QuantitativeValue", "minValue": 2, "maxValue": 4, "unitCode": "DAY" } } } } } Look, schema alone won't get you cited. But it removes ambiguity about what the product is, what it costs and whether it's in stock.
- Keep feeds and pages in sync Price, availability and product names in your Google Merchant feed, marketplace listings and site should match. Here's a quick diff you can run nightly: import csv
def load(path, key="sku"):
with open(path) as f:
return {r[key]: r for r in csv.DictReader(f)}
site = load("site_products.csv")
feed = load("merchant_feed.csv")
for sku, s in site.items():
f = feed.get(sku)
if not f:
print(f"{sku}: missing from feed")
continue
for field in ("title", "price", "availability"):
if s[field].strip().lower() != f[field].strip().lower():
print(f"{sku}: {field} mismatch -> site='{s[field]}' feed='{f[field]}'")
Mismatches are a trust problem for AI engines and for shoppers.
- Answer comparison questions on category pages Shoppers ask AI comparison questions: "Best cold brew kit under ₹2,000?" Add a short, honest comparison table on category pages with capacity, brew time, price and materials. AI engines love structured, factual comparisons.
- Make reviews readable Render review text in HTML, not only through a third-party widget that loads late. Reviews are corroboration. They're one of the strongest signals that a product is real and liked.
- Measure what matters Run the ten-minute test from the Kiyado post, adapted for products: Prompt Named? Cited? Accurate? Best [category] brands in India
Where can I buy [product type] online in Bangalore?
Is [your brand] good for [use case]?
[Your brand] vs [competitor]
Repeat monthly and track the trend, not single answers.
A worked example: one product page, before and after
Here's what the changes look like together on a single product page for a Bangalore D2C kitchenware brand.
Before:
"Elevate your cooking with our premium cookware. Crafted with love. Shop now!"
After:
"Example Kitchen's 26 cm tri-ply stainless steel kadai works on gas and induction, weighs 1.6 kg and comes with a 10-year warranty. Dishwasher-safe. Ships across India in 2–4 days; free delivery above ₹999."
The "after" version gives an AI engine everything it needs to answer "Which induction-compatible kadai has a long warranty?": the product type, size, material, compatibility, warranty and delivery terms. None of it is clever. All of it is quotable.
Then make the structured data say exactly the same thing:
{
"@context": "https://schema.org",
"@type": "Product",
"name": "Example Kitchen Tri-Ply Kadai 26 cm",
"material": "Tri-ply stainless steel",
"weight": { "@type": "QuantitativeValue", "value": 1.6, "unitCode": "KGM" },
"offers": { "@type": "Offer", "priceCurrency": "INR", "availability": "https://schema.org/InStock" }
}
Finally, check that your marketplace listings (Amazon, Flipkart and any quick-commerce apps) use the same product name, size and warranty. If the marketplace says "5-year warranty" and your site says ten, the engine has to pick one, and it may not pick yours.
Rollout tip: don't try this on 2,000 SKUs at once. Start with your 20 best-selling products and your five main category pages. Measure for a month. Then template the pattern for the rest of the catalogue so new products launch with complete summaries and schema from day one.
Who owns what
Product summaries usually fall between teams. Merchandising writes them, SEO edits them, and the marketplace team rewrites them for Flipkart. Give one team ownership of the master description for each product and make the others copy it rather than rewrite it. That one change stops most of the drift that confuses AI engines. Review the top 20 products every quarter, especially after a price, warranty or packaging change.
Common mistakes
Product facts only in images or carousels
Different product names on site, marketplace and feed
Out-of-stock items still marked InStock in schema
Thin, duplicated manufacturer descriptions across hundreds of SKUs
Results you can expect (honestly)
GEO is not a guarantee. The research reported meaningful gains, but they varied by category, and AI engines change often. What you can expect is cleaner data, fewer contradictions and product pages that work better for both Google and AI answers.
Searches for an ai seo company in bangalore and ecommerce technical seo show that D2C brands are starting to realise their product data is now part of their marketing.
If you'd like an outside audit of how AI engines see your store, Kiyado's Bangalore team offers one free: SEO and GEO for Bangalore brands →
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