CitationDesk

For e-commerce

AI-citation tracking for e-commerce — get recommended when shoppers ask AI.

A growing share of buying journeys now start with "ChatGPT, what's the best [product] for [need]?" — and the answer names a handful of brands before the shopper ever reaches Google or your category page. CitationDesk shows whether ChatGPT, Claude, Perplexity, and Gemini recommend your brand for buying-intent queries, which competitors get named instead, and the product-page gaps that keep you out of the answer.

What we hear from e-commerce brands

Three patterns showing up across your peers.

Shoppers ask AI for recommendations — and you're not in the answer

When someone asks an LLM for "the best [your category] under $X", it returns three or four brands with links. If you're not one of them, you lost the sale before the shopper ever saw your site — and your analytics will never show the visit that didn't happen.

You have no idea whether AI recommends you or a competitor

You track Google rankings and ad ROAS obsessively. AI product-recommendation share-of-voice is a complete blind spot — you can't see which competitors the models name in your category, or for which buying queries.

Your product pages are built for Google, not for AI extraction

Specs buried in tabs, no Product schema, marketing copy where a quote-ready spec should be — pages that rank fine on Google can be invisible to an LLM that needs to extract a clean, attributable fact to recommend you.

How CitationDesk fits

Built for the way e-commerce brands work.

Buying-intent share-of-voice

Track whether the four LLMs name your brand for the "best / recommended / vs" queries that precede a purchase — and exactly which competitors get recommended instead, query by query.

Product-page extraction audit

The Citation Readiness Score audits your product + category pages for the structure AI needs to recommend you — Product/Offer schema, quote-ready specs, extractable answers — and returns a 0-100 score plus the specific fixes.

Catch competitor displacement early

When a model swaps your brand out of its recommendation for a competitor, the alert lands the same week — so you fix the gap before a season's worth of AI-driven buyers route to someone else.

Worked example

Read Stacks.

Read Stacks made its catalog pages quote-ready and structured — the same product-page discipline that gets a store recommended when a shopper asks an LLM what to buy.

Read the case study →

The DTC brand that found out three competitors were being recommended by ChatGPT in its category — and closed the product-page gaps to get back in the answer.

— the kind of feedback we built CitationDesk to enable.

Get your free Citation Readiness Score.

Paste any URL. 90 seconds. 0-100 score across five dimensions plus the highest-leverage fix you can ship this week.