AI Shopping Assistants Are Recommending Products. Are Yours on the List?
When shoppers ask ChatGPT “best noise-canceling headphones under $300” or Perplexity “compare standing desks,” AI agents pull product data from the web. If your store is not optimized, your products are invisible to this fast-growing channel.
Product Discovery Has a New Channel
Consumers increasingly ask ChatGPT, Perplexity, and Google Gemini for product recommendations before visiting any store. These AI agents do not browse your site like humans -- they parse structured data, read product schema, and evaluate your content for authority and accuracy. See how AI agents evaluate every signal.
The e-commerce sites that win in this new channel are the ones with proper Product schema on every page, AI crawler access enabled, rich product descriptions with comparison data, and a machine-readable site summary via LLMs.txt. Learn how to implement Product schema for AI. Without these, AI agents literally cannot see your inventory.
What ConduitScore Checks for E-commerce
- ✓Product Schema — Price, availability, reviews, and offers in JSON-LD on every product page
- ✓Crawler Access — GPTBot, PerplexityBot, Google-Extended allowed to crawl product pages
- ✓Category Structure — BreadcrumbList schema for navigation hierarchy
- ✓Review Markup — AggregateRating schema for social proof in AI answers
- ✓Product Descriptions — Structured content that AI agents can extract and cite
- ✓LLMs.txt — Machine-readable catalog summary with top products and categories
Real data from our scans
Structured data gaps are widespread
Across 457 sites scanned between March 13 and March 17, 2026, the average AI visibility score was 35 out of 100 and the median was 29 out of 100. In nearly every low-scoring site, our structured-data analyzer flagged the absence of Product schema -- the markup that AI shopping assistants rely on to extract price, availability, and product details. This is consistent with what we observe across e-commerce: most stores have useful product information, but it is locked in HTML that AI agents cannot reliably parse.
457
Sites scanned
35 / 100
Average score
29 / 100
Median score
Addressing three gaps -- enabling AI crawler access in robots.txt, adding Product schema to product pages, and publishing an LLMs.txt file -- covers the most common deficiencies we see. These are the changes that consistently move scores from the 20–35 range into the 50–70 range in our scan data. They do not require a site redesign; they are configuration and markup changes.
Data from 457 anonymous ConduitScore scans, March 13–17, 2026.
Make Your Products Visible to AI Shoppers
Scan your e-commerce store now. See exactly what AI agents find -- and what they miss. Get copy-paste Product schema and crawler access fixes in 30 seconds. See what a fully optimized score report looks like.