Aeo Data Guide

The Data Model (Conceptual)

AirOps tracks how AI search engines talk about a brand. The core idea:

1. A brand has prompts — questions people ask AI (e.g. "What are the best AEO tools?")
2. Each prompt gets passed into multiple AI providers (ChatGPT, Gemini, Perplexity, etc.)
3. Each provider returns an answer
4. Each answer is checked: did it mention the brand? Did it cite (link to) the brand?
5. From this, we get metrics: mention rate, citation rate, share of voice, etc.

There's also a page side — how each URL on the brand's site performs across AEO (AI citations), GSC (Google search), and GA4 (site analytics).

Everything hangs off a Brand Kit, which lives in a Workspace. You always need to find the right brand kit first.

Two Domains of Data

Delegate to the right agent. If a question spans both (e.g. "which pages are losing AI citations and also losing Google rankings?"), start with the page-analyst (it has the combined view) and escalate to ai-search-analyst for deeper prompt/answer drill-downs.

Branded vs Non-Branded (Critical Guardrail)

Prompts have a brand_mentioned field. When the brand is in the question ("What is AirOps?"), AI will almost always mention it — inflating metrics.

Default to non-branded (brand_mentioned: false) for all analysis.

Only include branded when:

• User explicitly asks for branded vs non-branded comparison
• Analyzing brand-name recognition specifically

If metrics look suspiciously high, check whether branded prompts are included.

Handling Ambiguous Requests

Users often ask vague things. Here's how to route them:

"How are we doing?"

• Start with overall health: mention_rate, citation_rate, share_of_voice
• Break down by provider and topic to find the story
• Compare to benchmarks (5-15% mention rate for non-branded is good)
• Delegate to ai-search-analyst

"What should we work on?"

• Check pages with smart filters: losing_ai_visibility, citation_rate_decline, almost_page_one
• Check prompts where brand is NOT mentioned but volume is high (opportunities)
• Delegate to page-analyst for page opportunities, ai-search-analyst for prompt gaps
• For each opportunity, check if a page exists (see Refresh vs Create) and suggest a specific skill the user can run (e.g. /airops:refresh-content "url" or /airops:create-blog "keyword")

"How do we compare to competitors?"

• share_of_voice and citation_share by competitor dimension
• Drill into which topics competitors dominate
• Delegate to ai-search-analyst

"Show me our best/worst performing content"

• Pages sorted by citation_rate or citations_count (best) or using smart filters (worst)
• Delegate to page-analyst

"What's happening with [specific provider]?"

• Filter analytics to that provider
• Compare mention_rate and citation_rate vs other providers
• Delegate to ai-search-analyst

When you don't have enough context — ASK:

• No time period specified → "What time range? Last 30 days, 90 days, or a specific period?"
• No brand kit clarity → List brand kits and ask which one
• "Report" is vague → "Do you want a summary in chat, a saved markdown file, or a dashboard in AirOps?"
• Topic unclear → List topics and ask which to focus on
• "Performance" is broad → "Are you asking about AI search visibility (mentions/citations) or page performance (traffic/rankings)?"

Don't guess. A quick clarifying question saves a wrong analysis.

Reading AI Search Numbers

Mentions first, citations second

Mention rate is the primary metric for AI search visibility. It tells you whether AI is actually talking about the brand. If you're not mentioned, nothing else matters — citations, sentiment, and position are all downstream of being mentioned in the first place.

Citation rate is important but secondary — it measures whether AI links back to your content. You can be mentioned without being cited (brand is known but content isn't linked), but you generally won't be cited without being mentioned.

When reporting or analyzing:

• Lead with mention rate and share of voice
• Follow with citation rate as supporting context
• Don't fixate on citations alone — a brand that's mentioned everywhere but cited rarely has a different problem than one that's not mentioned at all

Metric combinations that tell a story

Volume matters

relative_volume_score (0-1) tells you how popular a prompt is. A prompt with 0.9 volume where you're not mentioned is a much bigger opportunity than one with 0.1 volume. Always factor in volume when prioritizing.

Reading Page Numbers

Page data combines three sources: AEO (AI citations), GSC (Google Search), and GA4 (site analytics). The power is in reading them together — one source shows what's happening, the others show why.

Single-signal patterns

Multi-signal patterns (the real insights)

How to navigate page analysis

1. Start broad — use smart filters to find pages that need attention
2. Check the combination — don't act on one signal alone. A page losing clicks but gaining citations is a different situation than one losing both.
3. Drill into why — use get_page_prompts to see which AI prompts cite the page. Are those prompts losing volume? Are competitors getting cited instead?
4. Cross-reference — if a page is losing AI citations, delegate to ai-search-analyst to check if it's a page-specific issue or a broader trend (maybe all pages on that topic are declining).
5. Prioritize by impact — pages with high traffic + declining signals need attention first. A page with 10 monthly visits losing citations is less urgent than one with 10,000.

Refresh vs Create (Hard Gate — Not Optional)

You MUST check if a page already exists before suggesting ANY content action. This is a hard gate, not a suggestion. If you skip this step, you will recommend creating content that already exists — which is worse than recommending nothing.

The rule (follow this EVERY TIME)

For every keyword, topic, or content gap you identify:

1. Identify the keyword or topic from the opportunity
2. Run list_pages — search by primary_keyword AND by url containing relevant terms. Do both searches. Cast a wide net.
3. If you're suggesting pillar pages, category pages, or hub content — also search by folder_name to see if an entire section already exists
4. If a page exists → recommend refreshing/optimizing that page. Don't create a competing page. Ever.
5. If no page exists (you searched and confirmed) → recommend creating new content.
6. If you're not sure → search more broadly. Don't default to "create."

What counts as "checking"

A real check means you actually called list_pages with relevant filters and reviewed the results. It does NOT mean:

• Assuming no page exists because you haven't seen one
• Skipping the check because you're making multiple recommendations
• Checking for one keyword but not the others in a batch of suggestions

Every recommendation needs its own check. If you're suggesting 5 pieces of content, that's 5 checks. No exceptions.

Why this matters

Suggesting "create a pillar page about X" when the brand already has one is embarrassing and erodes trust. The existing page might just need a refresh — updated stats, better structure for AI citation, stronger authority signals. Duplicate content also competes with itself in search rankings.

How to check (multiple searches)

For each opportunity, run at least two of these:

• list_pages filtered by primary_keyword matching the prompt's keyword
• list_pages filtered by url containing relevant terms (e.g. URL contains "pricing" or "comparison")
• list_pages filtered by folder_name to scan an entire section (e.g. /blog/, /resources/, /guides/)
• If the suggestion is a pillar/hub page, check for any existing pages in that topic area — not just exact keyword matches

What to recommend

When suggesting actions, point the user to a specific skill they can run. The user can invoke these directly — you can't run them yourself, but you can tell the user exactly what to type with the right inputs pre-filled.

Format your suggestions as ready-to-run commands with the actual keyword, URL, or topic filled in. The user should be able to copy and paste directly.

In your output, show your work. When recommending content creation, explicitly state that you checked for existing pages and what you searched for. Example:

"Searched list_pages for keyword 'aeo tools' and URL containing 'aeo' — no existing page found. Recommend creating: /airops:create-blog "aeo tools""

Skip Root / Home Pages in Recommendations

Never recommend changes to root-level or home pages (e.g. example.com/, example.com, www.example.com/). Even if their citation rate or traffic is declining, suggesting edits to a homepage is not actionable advice — home pages serve a different purpose than content pages, and their AI citation behavior is driven by brand recognition, not content optimization.

When scanning list_pages results:

• Filter out any URL that is just the domain root (path is / or empty)
• Don't surface root pages in "pages that need attention" lists
• Don't suggest refreshing or rewriting the homepage
• If a root page shows up in a smart filter result (e.g. losing_ai_visibility), skip it silently — it's noise, not signal

This applies to all recommendations: page-analyst findings, action grids, "what should we work on" answers, and any suggested /airops:refresh-content commands.

What NOT to Do

• Don't include branded prompts by default — inflates everything
• Don't report raw numbers without context — "12% mention rate" means nothing without the benchmark and trend
• Don't analyze a single time snapshot — always check the trend
• Don't mix up mention and citation — mention = named in the response, citation = linked to. Very different signals.
• Don't compare across providers without noting the difference — each AI has different behavior patterns
• Don't act on one signal alone for pages — always cross-reference AEO + GSC + GA4 before recommending action
• Don't guess at metric definitions — check the AirOps concepts resource if unsure
• Don't suggest creating content without checking if it already exists — see "Refresh vs Create" above. This is a hard requirement, not a suggestion.