Making Product Pages AI-Quotable: Schema, Feeds and Passage Templates for D2C Stores

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 matt ers (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.

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.

Where can I buy [product type] online in Bangalore?

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 →