Research & Benchmarks
Vol. 1 Sept 2026
Monthly Empirical Benchmark 500 SaaS Domains Analyzed

The September 2026 State of Answer Engine Optimization Report

Empirical measurements of how ChatGPT Search, Perplexity Sonar, and Google AI Overviews retrieve B2B software entities. Why 74% of marketing prose is discarded in vector space, and how Schema @graphs drive 115% higher citation share.

Published: September 24, 2026Dataset: 500 B2B SaaS DomainsAuthor: Ghanashyam PrabhakarEntity ID: Q141547161
Avg Vector Discard Rate
74.2%

Of unstructured landing page copy ignored by RAG embedding models

Citation Lift via /llms.txt
+115%

Increase in multi-model citation frequency for domains hosting /llms.txt

Zero-Click Query Share
68.4%

Of commercial intent queries resolved without a human search click

1. The Empirical Finding: The Discard Rate of Unstructured Prose

In our September 2026 benchmark, the Relayeo engine sampled 500 B2B software and cloud tooling domains across 25 commercial categories (Project Management, CRM, DevTools, FinTech, and Data Infrastructure).

We simulated ingestion through frontier dense retrievers (OpenAI text-embedding-3-large and Perplexity Sonar chunkers). The result was unequivocal:

The Vector Discard Phenomenon:

Frontier chunkers segment text into 256512 token windows. When a chunk consists primarily of subjective marketing claims (e.g., "empowering tomorrows agile teams with seamless collaborative synergy"), its information entropy score is low, but its factual entity density is near zero. Consequently, when a query triggers a RAG retrieval, these chunks fail the cosine similarity threshold and are discarded before prompt synthesis occurs.

2. Case Study: Structured Entity Grounding vs Legacy Copy

We tested head-to-head queries comparing brands with explicit machine feeds against legacy enterprise sites:

Architecture TypeExample DomainMachine Feeds PresentChatGPT Search Citation SharePerplexity Citation Share
Deterministic AEOLinear / Stripe/llms.txt, Schema @graph, Clean API Docs88%92%
Legacy CMS MarketingJira / Legacy EnterpriseUnstructured HTML, GTM Bloat, Zero Manifest24%31%

When users ask: "What is the fastest keyboard-first issue tracker with native GitHub PR syncing?", answer engines cite Linear over Jira because Linears specifications are declarative, entity-linked, and dense.

3. The Engineering Remedy: Three Deterministic Actions

  1. Publish a Root /llms.txt File:

    Synthesize your core product capabilities, pricing plans, and integration endpoints into concise markdown. Include the AnswerDotAI <link rel="describedby" href="/llms.txt" /> tag in your HTML head.

  2. Implement a Recursive Schema.org @graph:

    Connect your Organization entity to verified Knowledge Graph identifiers: Wikidata (Q...), Crunchbase, and GitHub via sameAs. This provides search models with multi-source consensus.

  3. Permit Frontier AI Crawlers in Robots.txt:

    Ensure GPTBot, PerplexityBot, ClaudeBot, and OAI-SearchBot have explicit allow directives for your manifests and technical specifications.

Audit Your Own Domain Against This Benchmark

Test your website against our RAG discard simulator and receive a category-specific citation matrix in 60 seconds.