Machine Relations Research

Citation Absorption vs Citation Selection: Why Getting Cited Is Not the Same as Getting Used

AI engines cite sources and absorb sources through different mechanisms. A 2026 measurement framework analyzing 21,143 citations across ChatGPT, Google AI Overview, and Perplexity shows that citation breadth and citation depth diverge — and most brands optimize for the wrong one.

AI-Enabled PR Agency Pricing: Retainer, Performance, and Pay-Per-Placement Models Compared

AI-enabled PR agency pricing in 2026 spans four models with different cost structures, risk profiles, and AI visibility outcomes. This comparison covers retainer, project, performance, and pay-per-placement pricing with current benchmarks.

Cross-Domain Brand Authority vs Backlinks: What Actually Drives AI Citation Selection

AI engines resolve citations through cross-domain entity recognition, not backlink graphs. Research shows cross-engine citations exhibit 71% higher quality scores — and entity chains explain why.

How to Track Brand Mentions in Perplexity AI: What Actually Works in 2026

Perplexity cites sources in every answer, but most brands have no system for tracking when they appear or why. This guide covers the methods, tools, and source-architecture decisions that determine whether your brand gets mentioned at all.

Jaxon Parrott on Pay-Per-Placement PR Agencies: What Actually Works for AI Visibility in 2026

Jaxon Parrott, who coined Machine Relations, explains why pay-per-placement PR agencies solve the wrong problem in 2026: the placement is not the asset — the attribution layer around it is.

Why Traditional PR Needs Machine Relations: The Visibility Gap in 2026

Traditional PR was built to place stories with human editors. Machine Relations addresses what happens after—when AI systems retrieve, cite, and recommend your brand to buyers who never read a press release.

Jaxon Parrott on Pay-Per-Placement PR Agencies: What the AI Era Changes About Placement Value

Jaxon Parrott, founder of AuthorityTech and originator of the Machine Relations framework, explains why pay-per-placement PR must now satisfy AI retrieval systems, not just human readers, to produce durable authority.

Share of AI Citation: The PR Metric That Replaces Impressions in 2026

PR teams have been measuring the wrong thing. Share of AI citation is the metric that tells you whether your earned media is actually working when buyers use AI to research vendors.

How Entity Chains Drive AI Search Visibility for Startups

Entity chains are the retrieval primitive AI engines use to confirm and cite brands. This guide explains how startups build entity chains that generate AI search visibility, and what gaps prevent attribution.

The Impact Loop: How AI Citation Systems Create Self-Reinforcing Authority

The impact loop is the feedback mechanism by which AI engines compound citation authority over time. Sources that get cited tend to get cited again—here is how the loop works and what breaks it.

AI-Readable Coverage in 2026: What Machines Can Actually Cite

AI-readable coverage is earned media and source architecture structured so AI systems can crawl, parse, verify, and cite it.

Earned Media as AI Citation Infrastructure: How Coverage Becomes Retrieval Evidence

Earned media is not a brand awareness tactic. It is the source architecture that determines whether AI engines have citable evidence for your brand when buyers ask.