We Queried Claude, ChatGPT & Perplexity 750 Times. Here's Which Businesses They Actually Cite.

Claude names 14.7 businesses per query. ChatGPT names 9.1. Perplexity names 8.4.

We know this because we ran 750 structured queries across all three engines, covering 50 commercial categories from personal injury lawyers to industrial goods manufacturers. Every response was parsed for brand names, source URLs, and directory mentions. The full dataset is downloadable at the bottom of this post.

This is primary research. Not a survey. Not an opinion piece. Raw data from 750 API calls made on a single day in July.

What We Tested

50 categories across five verticals: Tech & SaaS, Traditional B2B, Professional Services, Health & Wellness, and Local Home Services. Each category received five query types per engine.

The five query templates: entity extraction ("list 5 prominent providers"), buyer comparison ("compare the leading providers"), scenario-based ("I run a 50-person firm, who should I evaluate?"), organic verification ("how would you verify a provider is legitimate?"), and location-specific ("name the top-rated provider in [city]").

Three engines: Claude Sonnet 4.6 via API at temperature 0. ChatGPT GPT-4o-mini via API at temperature 0. Perplexity Sonar via API. Gemini 2.0 Flash was included but returned empty responses across all 250 queries, so its data is excluded from scoring.

The Engine Gap is Real

Claude produced the richest responses by a wide margin. 14.7 entity mentions per query, zero refusals, and 290 organic directory recommendations across 250 queries. It formatted responses with comparison tables, location details, and client portfolios unprompted.

ChatGPT came in second at 9.1 entities per query with 233 directory mentions. It refused 0.4% of queries (one refusal out of 250). Its responses skewed toward shorter lists with less context per brand.

Perplexity averaged 8.4 entities per query with 92 directory mentions. Perplexity's strength is source attribution: it links to the pages it pulls data from. Its weakness is that it sometimes generates thinner brand descriptions than the other two.

What AI Tells Buyers When They Ask How Do I Verify This Provider

Template 4 was the test that mattered most. We asked each engine: "How would you verify that a [category] provider is legitimate and trustworthy before hiring them?" No mention of directories. No leading words. Just an open question about verification.

AI engines organically recommended these directories, unprompted:

Yelp and Expertise.com are the two directories AI engines trust most when advising buyers. BBB comes third. For B2B categories, Clutch and G2 dominate. For health, RealSelf and Healthgrades. For home services, Angi and HomeAdvisor.

The pattern: AI engines recommend vertical-specific directories over general ones. A buyer asking about cosmetic surgery gets pointed to RealSelf. A buyer asking about managed detection and response gets pointed to Gartner and G2.

Which Categories Are Most Visible to AI?

We scored each category on a 0-100 scale using four signals: entity naming rate (40% weight), source/directory citation rate (35%), cross-engine brand agreement (15%), and structured output rate (10%).

The top 10:

Professional Services dominated. Seven of the top 15 categories belong to this vertical. Agencies, consultancies, and professional firms produce the kind of structured online presence (case studies, client lists, review profiles) that AI engines parse well.

The bottom 10:

Healthcare and Traditional B2B filled the bottom. Addiction treatment centres, home health care providers, and industrial manufacturers scored lowest. These industries have weaker structured data footprints online: fewer review profiles, less schema markup, and more fragmented web presence.