Do AI engines agree on the best tool?

Rarely. In our category measurements no single vendor leads on every engine; a name that dominates one engine is often absent from another. AI visibility is engine-specific.

Where do AI answers get their sources?

Roughly 95% of the citations behind these answers come from third-party pages — comparison posts, review sites, and community threads — not the vendor's own marketing.

Research · Report

State of AI Search: the engines disagree

By Logan Adams, Founder Reviewed & updated Jul 2026 Measurement-first: figures are median share-of-model with 95% confidence intervals. How we measure.

When buyers ask AI for the best tool in a category, the answer depends on which AI they ask — and the sources behind it are mostly not the vendor's own site.

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TL;DR. Across the categories we measure, no single vendor leads on every engine, and roughly 95% of the citations behind AI answers come from third-party pages (Otterly, State of AI Search). AI visibility is engine-specific and source-driven — it has to be measured, and worked, per engine.

The finding

We run the same buyer-prompt set against ChatGPT, Perplexity, Gemini, Claude and Grok — each prompt repeated 10+ times per engine, because a single AI answer is noise, not data. We record which vendors are named and how often, then compute each vendor's share of model: the percentage of qualifying answers that mention them, with a confidence interval.

No single vendor led on all engines. A name that dominated one engine was frequently absent from another. The "best tool" a buyer hears depends less on the product and more on which assistant they happened to ask — and on which third-party sources that assistant tends to cite.

What the leaderboards show

Measured category leaders (2026-07-01) from our AI Visibility Index — adaptive per-engine sampling across 5 engines (5-run floor; share-of-model Wilson 95% CI (pooled mention denominator), presence Wilson 95% CI). See each leaderboard for the full ranked table and per-engine spread. Every public Index category is now a live measurement — no illustrative previews.

Category leaders · 5 engines · measured 2026-07-01Updated monthly

Category	Leader	SoM	95% CI
Incident management platforms	PagerDuty	25.0%	23.1–27.1%
Product analytics platforms	Mixpanel	23.2%	21.4–25.1%
CRM software	HubSpot	22.3%	20.6–24.1%
Databases	PostgreSQL	22.3%	20.5–24.1%
CI/CD platforms	GitHub Actions	21.8%	20.1–23.7%
API platforms	Kong	20.3%	18.6–22.1%
Vector databases	Weaviate	19.1%	17.6–20.8%
Feature flag platforms	LaunchDarkly	17.2%	15.7–18.8%
AI observability tools	Datadog	16.4%	15.0–17.9%

Why it matters

If your visibility is strong on one engine and invisible on another, you lose shortlist spots you will never see in your analytics. The flip side: because the inputs are knowable — structured data, entity signals, and the specific third-party sources each engine pulls from — this is fixable. It just has to be measured per engine and worked per engine.

Method & honesty

Share of model = the % of qualifying answers that name a brand, measured as the adaptive per-engine sampling (at least 10 runs per buyer prompt across 5 engines: ChatGPT · Perplexity · Gemini · Claude · Grok, never below a 5-run floor), with a Wilson 95% confidence interval for share-of-model over the pooled mention denominator (presence Wilson 95% CI). Consumer apps and APIs differ (system prompts, tools, browsing) — we treat each edition as a point-in-time measurement and re-run on a cadence. We publish the date and method with every figure.

Last reviewed: July 6, 2026. We re-check figures on a monthly cadence because AI engines change continuously.

Get the full dataset

The per-category CSV/JSON datasets (CC BY 4.0) plus the methodology notes. Tell us where to send them.

Logan Adams · Founder, Clear Cited

Writes on how AI answer engines pick what to recommend, share-of-model methodology, and reproducible AI-visibility measurement. About Clear Cited →