AI Search Engines Disagree by 2× on Brand Recommendations: Inside Treyci's Empirical Study

Empirical research from AI visibility analytics firm Treyci reveals a 2x structural divergence in how leading generative search engines recommend commercial brands. In one representative enterprise B2B software evaluation, one primary AI engine cited tracked vendors in 81% of generated responses, while a competing engine recommended the identical vendor set in only 43% of queries.

The findings deliver a direct challenge to manual, screenshot-based AI search auditing. Because large language models rely on probabilistic sampling, identical buying queries executed across separate sessions routinely yield contradictory vendor shortlists. As Treyci founder Keith Schilling warned, relying on single-prompt manual checks produces an anecdotal illusion rather than actionable enterprise visibility intelligence.

Recommendation Metric	Perplexity / Gemini	ChatGPT / Copilot
Tracked B2B Vendor Mention Rate	81%	43%
Session Repeatability Rate	~52%	~48%
Primary Citation Source	Review Hubs (54.2%)	Editorial Press (31.6%)
Direct Brand Website Citations	14.2%	14.2%

Technical & Strategic Deep Dive

Generative Engine Optimization (GEO) has rapidly transitioned from an experimental digital marketing tactic into a multi-million dollar corporate budget line item. However, Treyci’s findings reveal that most organizations are applying deterministic SEO measurement mentalities to non-deterministic systems.

The Probabilistic Dilemma and Temperature Drift

Traditional search engines operate on deterministic indexing. While personalization and geolocation cause localized SERP differences, a company ranking #1 for “enterprise CRM software” generally retains that position across multiple browser sessions on the same day.

In contrast, generative search engines synthesize responses through temperature-adjusted probabilistic decoding. When an enterprise buyer asks, “What are the top three data observability platforms for Snowflake?”, the underlying model calculates token probabilities across its latent space and retrieved context documents.

Depending on the random seed, temperature parameter (typically 0.2 to 0.7 in commercial search interfaces), and real-time retrieval rankings, a vendor may appear in one answer and completely vanish in the next. Relying on a single manual query to evaluate brand visibility leads to wildly inaccurate executive reporting.

Third-Party Citation Hegemony Over Brand Websites

Perhaps the most disruptive finding for content marketers is where AI engines look when evaluating product superiority. While brands spend millions optimizing their corporate websites, product landing pages, and corporate blogs, AI engines exhibit strong algorithmic skepticism toward self-authored marketing copy.

When answering buying-intent queries, models heavily weight consensus data retrieved from:

• Aggregated user reviews (G2, Trustpilot, Capterra, Gartner Peer Insights).
• Independent editorial comparisons published on authoritative media domains.
• Technical discussion forums (Reddit, Stack Overflow, GitHub Issues).

Brand websites are primarily cited only for factual specifications (pricing tiers, supported protocols, documentation schemas), whereas qualitative recommendations (“Which tool is best for…”) are almost universally backed by third-party citations.

The llms.txt Measurement Disconnect

The study observed surging adoption of llms.txt—a proposed standard designed to provide AI web scrapers with clean, markdown-formatted summaries of site structure and product features. While 41% of top SaaS providers have deployed the file to their web root, Treyci discovered zero statistically significant correlation between having an llms.txt and achieving higher commercial recommendation rates.

Without continuous tracking across engines, marketing teams are executing technical optimizations in a vacuum, unable to differentiate between effective visibility engineering and useless overhead.

Real-World Utility & Limitations

Implications for Enterprise Growth Teams

• Budget Reallocation: Enterprise marketing teams must redirect budget away from isolated corporate blogs toward verified third-party review profiles and digital PR.
• Statistical Sampling: Marketing reporting must shift from “current rank” to “win rate probability” calculated across at least 50 automated query executions per week.

Limitations of Current GEO Metrics

• Black-Box Retrieval: Because OpenAI, Perplexity, and Google frequently alter their retrieval-augmented generation (RAG) pipelines without public notice, visibility metrics can fluctuate dramatically due to underlying infrastructure changes rather than competitor action.
• High API Monitoring Costs: Continuously polling multiple proprietary AI engines across hundreds of prompt permutations requires substantial API and proxy spend.

Updated on September 5, 2026