Monitoring Engine Playbooks
Methodology & engine playbooks

Playbooks

The orientation layer for AEO Owl. Start with the methodology to understand what your dashboard is telling you, then drop into the tactical engine playbooks to close the specific gaps your audit revealed.

Updated June 2026 · Methodology first, then 8 of 8 engines published.
Start here · The methodology

AEO Owl measures how often, where, and how AI answer engines cite your brand — and reports it as two 0–10 scores you track over time. AI Visibility is the outcome: are engines citing you right now? AEO Readiness is the diagnostic: is your site built to earn those citations? The two sit side by side and are never blended into one number.

AI Visibility — the outcome score

A deterministic formula over four signals — citation rate, share of voice, average position, and engine coverage. Same audit data, same score, every time. No black-box weighting. Above 6.5 = strong category presence; below 4 = you're functionally invisible to AI.

AEO Readiness — the diagnostic score

Readiness explains why your visibility lands where it does — and it usually moves first, one to three audits before visibility follows. It's built from three pillars:

01 · TECHNICAL
Technical

Can AI crawlers reach you? Open robots.txt rules, server-rendered HTML, Organization JSON-LD.

02 · CONTENT
Content

Is your content shaped for AI extraction? Question-form H2s, direct-answer paragraphs, FAQPage schema.

03 · AUTHORITY
Authority

Are third parties backing you? Source diversity, third-party ratio, category-leader citations (G2 / Capterra / Gartner).

How an audit works

Each audit fires 8 engines × 10 questions × 3 repetitions = 240 engine calls. The raw responses are the immutable evidence layer — every metric on the dashboard is computed deterministically from them, not summarized by an LLM. Monitor subscribers get auto-runs on a monthly cadence; credit buyers run on demand for $450 per audit.

Each audit is a frozen point-in-time snapshot. Trends come from comparing snapshots — if a number looks wrong, you can trace it back to math, not vibes.

How to read the dashboard

Source-driven engines
Perplexity
Reach · technical + content

The easiest engine to game in the right way. Source-rich pages with explicit citations, recency signals, and question-shaped H2s.

12 min read
Google AI Overviews
Reach · structure-heavy

The most mechanical engine. Inverted-pyramid openings, comparison tables, and FAQPage schema do most of the work.

8 min read
Training-data engines
ChatGPT
Know · long-horizon

The slowest engine to move and the most strategically important. Entity grounding in Wikipedia, Crunchbase, Reddit. 90-day arc.

10 min read
Claude
Reach · precision-first

The most citation-conservative engine. Specific, sourced, dated claims get extracted; marketing flourish doesn't.

10 min read
Ecosystem engines
Gemini
Know · Google ecosystem

Wins on the Google ecosystem: Business Profile, YouTube transcripts, Knowledge Graph entries, E-E-A-T signals.

9 min read
Grok
Know · real-time social

Indexed entirely from live X conversations. Founder-led posting + reply cadence + trend-jacking with substance.

7 min read
Specialized engines
Mistral
Reach · EU-focused

European-trained engine with the weakest US coverage. Strategic arbitrage for brands serving EU buyers — and competition is thin.

7 min read
DeepSeek
Reach · structured-data

Reasoning-strong, retrieval-light. Wins on schema-heavy pages, technical documentation, and structured product data.

7 min read
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