SEO Geo Aeo Maxxing

SEO / GEO / AEO Maxxing

Run one auditable diagnostic system across classic search, generative search, answer engines, and answer extraction. Separate controllable readiness from observed visibility and business outcomes.

Non-negotiable rules

• Diagnose; do not mutate the live site, CMS, repository, DNS, CDN, robots, analytics, or webmaster tools.
• Never invent rankings, traffic, backlinks, CWV, citations, impressions, crawler access, or conversions.
• Never turn a correlation, practitioner heuristic, or old SEO convention into a platform requirement.
• Never treat training access as search/citation access.
• Never call missing telemetry 0.
• Never call a text-only fetch proof that JSON-LD is absent.
• Never score by feel when the bundled scoring scripts can run.
• Answer in the user's language while preserving technical identifiers.

Workflow

1. Frame scope, archetype, business goal, critical URLs, and target surfaces.
2. Pick mode and scoring profile.
3. Check volatile platform-source freshness.
4. Generate a complete audit skeleton.
5. Collect target-specific evidence using a stratified sample.
6. Run active pillar checklists and conditional overlays.
7. Score mechanically.
8. Measure observed search/AI visibility separately.
9. Prioritize by dependency, business exposure, evidence, and effort.
10. For VERSUS/DELTA, run deterministic comparison.
11. Deliver using the output contract.

Step 0 - Frame the audit without needless interrogation

Read references/data-collection.md and references/site-archetypes.md.

Capture or conservatively infer:

• site archetype(s);
• primary business goal;
• target market/language;
• target search/AI surfaces;
• critical URL classes and representative templates;
• priority query/task set when supplied or available from connected data;
• recent migrations/events relevant to a delta.

Label inferred context INFERRED. Ask only when missing context would materially change scope and cannot be resolved from available evidence. Otherwise proceed and use NOT_ASSESSED for real gaps.

When the user explicitly cares about browser/transaction agents, also read references/agent-readiness.md. Keep that overlay outside MAXX unless the registry is extended.

Step 1 - Pick mode and profile

Profiles:

• balanced - default broad audit;
• classic-search - brief is primarily organic Search;
• ai-first - explicit priority is AI discovery/citation readiness.

Do not silently invent custom weights.

A Foundation dependency may matter in PILLAR mode. Either expand the active scope and run the full Foundation checklist, or report the dependency as a non-scored diagnostic note. Never score one Foundation check as if it represented the whole Foundation pillar.

Step 2 - Freshness gate

Read references/live-source-registry.md and machine state in references/live-source-registry.json.

Before volatile GEO/AEO claims, run when code is available:

python scripts/check_freshness.py --strict

Volatile topics include crawler names/purposes, robots controls, AI-search eligibility, measurement products, structured-data feature support, llms.txt, agent guidance, and referral conventions.

If a required group is stale, refresh current first-party sources. Platform-specific scoring will refuse stale bundled facts. It also rejects bundled knowledge verified after a historical audit's as_of date, preventing temporal leakage. Supply an appropriate official-source override, for example:

"freshness_overrides": {
  "openai_search": {
    "verified_at": "2026-08-25",
    "sources": ["https://help.openai.com/en/articles/12627856-publishers-and-developers-faq"]
  }
}

If current first-party evidence cannot be obtained, keep the claim narrow and label it UNVERIFIED.

Step 3 - Generate the complete audit skeleton

Use references/check-registry.json as the closed registry. Do not omit inconvenient checks.

When code is available, create the skeleton first:

python scripts/build_audit_template.py --mode FULL --subject https://example.com/ > audit.json

Examples:

python scripts/build_audit_template.py --mode PILLAR --pillars geo --surfaces chatgpt-search,google-ai-search > audit.json
python scripts/build_audit_template.py --mode FULL --profile ai-first --archetypes saas,multilingual > audit.json

The generator starts applicable checks as NOT_ASSESSED and marks out-of-scope platform checks N/A. Fill the skeleton with evidence; do not rebuild a hand-selected checklist from memory.

Step 4 - Collect evidence

Read references/evidence-policy.md.

Every active check must end as one of:

• PASS
• WEAK
• FAIL
• N/A
• NOT_ASSESSED

Scored verdicts require target-specific evidence. Generic platform documentation alone cannot prove the target site passes. Use evidence objects:

{
  "id": "GEO-06",
  "verdict": "PASS",
  "evidence": [
    {
      "class": "E2_SITE_DIRECT",
      "artifact": "robots.txt does not disallow OAI-SearchBot on critical public paths",
      "source": "https://example.com/robots.txt"
    },
    {
      "class": "E1_FIRST_PARTY_LIVE",
      "artifact": "Current OpenAI publisher guidance identifies OAI-SearchBot as the Search control",
      "source": "https://help.openai.com/en/articles/12627856-publishers-and-developers-faq"
    }
  ]
}

For conditional N/A, supply both a reason and target-specific applicability_evidence. If applicability is merely unknown, use NOT_ASSESSED.

For sampled templates, use distribution when it preserves more truth than a coarse verdict:

"distribution": {"pass": 7, "weak": 0, "fail": 1}

Do not make sitewide claims from a page sample. State sample/template scope.

Step 5 - Run pillar checklists

Load only what the active scope needs:

Use references/schema-library.md and references/fix-library.md only after a concrete finding needs implementation guidance.

Important schema safeguard: static/text extraction may strip JSON-LD. Before saying schema absent, follow the rendered/validator protocol in references/data-collection.md; otherwise use NOT_ASSESSED.

Step 6 - Score mechanically

Read references/scoring.md, then run:

python scripts/score_maxx.py audit.json > score.json

Key semantics:

• MAXX exists only when all five pillars are active.
• Any subset outputs Focused readiness, never a whole-site MAXX tier.
• NOT_ASSESSED reduces coverage; it is not zero.
• A composite score is withheld when any active pillar has less than 50% weighted evidence coverage; 50-74.9% remains provisional.
• Conditional N/A is evidence-gated; core checks cannot be hidden as N/A.
• Scored verdicts require target-specific E2/E3/E4 evidence.
• Critical gates require direct/reproducible/connected evidence and must agree with related checks.
• Platform checks self-expire when bundled source facts exceed their TTL.
• Evidence grade describes audit support, not ranking/citation probability.

If code execution is unavailable, reproduce registry math exactly, show the arithmetic, enforce the same N/A/evidence/freshness/gate rules, and label the result manual registry calculation.

Step 7 - Keep observed visibility separate

Read references/measurement.md.

Use actual data when available, such as:

• Search Console Generative AI reporting when the property has access;
• Bing Webmaster Tools AI Performance and current AI visibility views;
• analytics referrals and conversions;
• connected third-party longitudinal panels;
• a fixed prompt sample clearly labeled directional.

Do not convert missing measurement into 0 citations or 0 impressions.

Step 8 - Prioritize

Read references/prioritization.md.

Resolve gates/prerequisites before downstream optimization. Score Impact x Confidence x Ease, but include affected template/business scope and cap Confidence by evidence. Label recommendations as REMEDIATION, MEASUREMENT, or EXPERIMENT.

Return at most 3 actions for this week and 5-7 for this month. Return fewer when evidence does not justify more. Never pad.

Step 9 - Compare VERSUS / DELTA deterministically

Score each side separately, preserving registry/profile/pillars/target surfaces, then run:

python scripts/compare_scores.py before.json after.json --kind delta
python scripts/compare_scores.py site-a.json site-b.json --kind versus

If scoring engine, registry, profile/weights, pillar set, or surface scope differs, mark totals non-comparable. If evidence coverage is materially asymmetric, warn before declaring a winner. Use output fingerprints for traceability and keep observed-visibility deltas separate from readiness score deltas.

Step 10 - Deliver

Read references/output-contract.md.

Lead with the decision, gate, or dominant uncertainty. Show scope, coverage, evidence grade, score, observed visibility, platform controls when relevant, material findings, prioritized actions, and not-assessed gaps. End with one concrete next step.

Anti-pattern guardrails

• Title/meta length ranges are heuristics, not ranking pass/fail thresholds.
• Multiple H1s or skipped heading levels are not automatic Google Search failures.
• E-E-A-T is a quality/trust framework, not one Google ranking factor.
• Structured data is not a generic ranking or AI-citation boost.
• Current Google generative Search does not require special AI schema or llms.txt.
• Do not confuse Google-Extended with Google Search eligibility.
• Do not confuse GPTBot/ClaudeBot training access with search access.
• FAQ/HowTo rich-result advice must reflect current platform support, not historical SEO playbooks.
• Query fan-out supports real task/topic coverage; it is not permission for scaled thin query pages.
• Google Preferred Sources is an optional user-selected publisher distribution feature, not a generic ranking hack or score bonus.
• AEO means extraction readiness, not guaranteed snippets/PAA/voice/AI citation.
• Agent interaction readiness is separate from search/citation readiness.

Bundled modules

references/
  data-collection.md
  evidence-policy.md
  site-archetypes.md
  live-source-registry.md
  live-source-registry.json
  check-registry.json
  pillar-foundation.md
  pillar-relevance.md
  pillar-authority.md
  pillar-geo.md
  pillar-aeo.md
  agent-readiness.md
  measurement.md
  scoring.md
  prioritization.md
  fix-library.md
  schema-library.md
  output-contract.md
scripts/
  build_audit_template.py
  check_freshness.py
  score_maxx.py
  compare_scores.py
evals/
  regression-cases.md