AI Relations vs. AEO/GEO

AI Relations vs. AEO/GEO

AI assistants are becoming the most influential intermediaries between buyers and brands. Treating them like a new “search engine” you can tune with AEO/GEO tactics misses the point. You don’t “rank” in a dialogue with a probabilistic, evolving system. You earn inclusion and preference by proving you’re the most reliable way for the model to help its user.

Below is a clearer frame for the work ahead.

Marketing to LLMs

Large language models are not indexes. They are reasoning systems that synthesize across sources, fill gaps with priors, and optimize for “helpfulness.” Three properties matter:

1. Prompt space is effectively infinite. Unlike keywords, natural-language requests vary without bound, and multi-turn context changes what “relevance” means in real time. You can’t chase phrases; you must cover domains.

2. Outputs are non-deterministic. The same query can yield different cites or selections across runs and versions. Your goal is not a fixed position; it’s to be consistently preferred when the model needs a trustworthy fact, example, vendor, or workflow.

3. Knowledge is federated and fluid. Models read you (your site), about you (credible third parties), and around you (comparables, reviews, specs, pricing feeds). They also re-evaluate when evidence changes. You need breadth, depth, and freshness—not tricks.

Marketing to LLMs therefore looks less like rank manipulation and more like evidence management: making it easy for the model to conclude “this brand is the safest way to satisfy the user’s intent.”

Why AEO/GEO Misses the Mark

AEO/GEO tries to retrofit SEO logic onto systems that don’t behave like search engines. It reduces a multi-agent, probabilistic ecosystem to a checklist. Here’s where it breaks.

Keywords ≠ Prompts

SEO assumes a finite, high-volume head you can target. In LLM land, the long tail is the distribution. You can’t chase phrases; you must cover domains.

Snippet ≠ Synthesis

Search surfaces snippets; LLMs compose answers. They weight multiple sources, interpolate, and may show no single “result.” Optimizing for a top slot misses how inclusion actually happens.

Signals ≠ Proof

Legacy ranking signals (anchor text, density, schema tricks) are weak in a system that can read and reason. Models reward consistency, coverage, and corroboration, not surface cues.

Static Rules ≠ Moving Target

AEO checklists assume stability. LLM behavior shifts with training refreshes, retrieval configs, and tools. What “worked” yesterday can quietly decay tomorrow.

Vendor Bias ≠ User Help

Prompt-shaping to “make the model say our name” isn’t durable. Models optimize for user value, not your brand; gaming tends to wash out across versions.

Attribution ≠ Last-Click

AEO/GEO assumes SEO’s tidy funnel (query → click → session). However, AI recommendations make attribution harder than ever. Most users don’t directly click the citations in ChatGPT. Instead, they open a new tab and visit your website directly. AI influence is partly hidden in organic traffic and sales notes (“Referred by ChatGPT”). Measuring impact, then looks like PR-style triangulation—tracking inclusion and framing in answers, crawl/freshness signals, third-party corroboration, and correlated demand—rather than rank and CTR.

Bottom line: AEO/GEO can be a subset tactic (clean FAQs, sensible schema), but it’s not a strategy. It optimizes appearances. AI Relations optimizes trust—earning default inclusion through evidence, access, and upkeep.

AI Relations

AI Relations is to LLMs what Public Relations is to people: a discipline for establishing authority and trust with the systems that shape public understanding.

Where PR builds credibility with consumers, critics, analysts, partners, and employees, AI Relations builds credibility with models, retrieval layers, and evaluation pipelines. The unit of progress isn’t a rank; it’s default inclusion and favorable framing across diverse prompts and contexts.

AI Relations has four pillars:

1. Reference Architecture (Your Open-Web Corpus)

   • Publish unambiguous, comprehensive, and current pages: product specs, pricing logic, implementation guides, comparisons, security & compliance, SLAs, roadmaps, FAQs, policy addenda, evaluation criteria.

   • Use stable URLs, clear headings, tables where appropriate, changelogs, and schema as scaffolding—not as a crutch.

   • Write to be citable, not catchy. Remove ambiguity and marketing fluff; add edge cases, definitions, and proofs.

2. Evidence Placement (Authoritative Third Parties)

   • Ensure the model can corroborate your claims: reputable media, standards bodies, docs sites, industry databases, open benchmarks, customers’ public references.

   • Prioritize sources models are known to trust (encyclopedic references, high-authority trade pubs, well-moderated forums).

   • Treat this like PR: embargoes, explainers, and third-party validations that reduce uncertainty in the model’s reasoning.

3. Data Availability (Feeds, Schemas, APIs, Toolability)

   • Make live facts machine-available: pricing calculators, inventory, regional availability, compatibility matrices, SDKs, OpenAPI descriptors.

   • Where possible, provide tools (APIs and typed contracts) a model or agent can invoke. If the assistant can call your function to confirm, it will recommend you more confidently.

4. Observation & Upkeep (Measurement and SLAs)

   • Monitor inclusion (presence in citations and tool choices), positioning (how you’re described), and freshness (crawler frequency, retrieval recency).

   • Track second-order impact: branded search, direct traffic lift, assisted conversions after model mentions.

   • Operate with SLAs: update windows for critical facts, deprecation policies, source-of-truth ownership.

What “looks like AEO” (FAQ blocks, schema) fits here—but only as presentation hygiene inside a larger posture: be the easiest source to trust.

Embracing the New Paradigm

Shift your mental model from “ranking for terms” to earning default status in an assistant’s working memory for your domain.

Practical operating model

• Two tracks, two audiences.

  • Human persuasion in walled gardens (video-first, creator-led, community).

  • Model persuasion on the open web (reference-first, machine-readable, corroborated).

• From campaigns to canon.

  • Campaigns spike interest; canon sustains inclusion. Maintain a living body of truth with clear ownership and update SLAs.

• From hacks to hygiene.

  • Replace one-off “prompt SEO” experiments with enduring assets: proofs, case data, customer workflows, failure modes, limitations, and exact claims.

• From vanity metrics to inference metrics.

  • De-emphasize “answer placement.” Emphasize coverage (topics you meaningfully own), corroboration (third-party alignment), and consistency (how often models prefer you across paraphrases and versions).

• From secrecy to legibility.

  • Obfuscation hurts you. The more legible your product, pricing, and guarantees, the easier it is for a model to choose you without hedging.

What good looks like

• Your site reads like a specification plus field guide.

• Third parties restate your claims accurately.

• Assistants name you unprompted for core use cases and select your tools when available.

• Net-new buyers report “the AI suggested you” with growing regularity—even when no referral click exists.

Bottom Line

AEO/GEO is a narrow attempt to retrofit SEO into an era that no longer rewards surface signals. AI Relations recognizes the real task: give intelligent systems enough evidence, access, and assurance to make you their safest recommendation.

Stop trying to win answers. Start earning trust.