Why I started counting citations
At Orbator we ask ChatGPT, Claude, Gemini, Perplexity, and Grok the same buyer-style questions — "best CRM for a 10-person startup," "best CI/CD tool for a monorepo" — across 271 software categories, every week. We don't just log the answer. We log every source each engine cites before it gives that answer. That's the AI Recommendation Index, and it's been running long enough now that the numbers stopped looking like noise and started looking like a pattern.
The number that made me stop and re-check the query
Over the trailing 28 days, we captured 143,424 citations. 46.2% of them resolve to vendor-owned domains — comparison pages, "top 10" listicles, and buyer's-guide content published by companies that sell a product in the exact category being asked about. Not third-party review sites, not Reddit threads, not analyst reports. The vendors themselves, showing up as the evidence behind what looks like neutral advice.
I want to be careful here: this isn't proof of manipulation. It's proof of a structural blind spot. Vendor content is often the most detailed, most frequently updated, most SEO-optimized material on a given category — so it's exactly the content a retrieval-augmented model is most likely to find and cite. The AI isn't lying. It's citing what's easiest to find, and what's easiest to find is marketing.
