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Recommendation Share Movement
Positive checks are historical detector results and can repeat across runs. They are not unique citations, recommendations or visitor counts. The response records below provide context; a 0% Recommendation Share remains a valid reported outcome.
Citation Evidence
AI Engine Activity 0
Two views below. Page-linked observations have an associated deployed page. Brand-query responses have no associated page. These records do not by themselves establish a recommendation, a verified source link or causation.
Page-linked Observations 0
The associated page and recorded response are shown together for inspection. Association alone does not prove that the page caused the response.
Brand-query Responses 0
Responses to brand-related questions, including uncertainty or lack of recognition. These are retained for diagnosis and are not counted here as verified citations.
Evidence Trail
How AI Engines Found Us
Inspect the recorded response and associated page where available. Brand-query responses may mention the brand without recommending it or linking to a source.
Before → After Evidence
Queries Where Citations Changed
Citation Scan
Every Query. Every Engine.
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Checks Run
0
Citations Found
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Engines
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Queries
Live Verification
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Don't take our word for it. Pick a query and an AI engine, then watch it respond — live, unscripted.
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Progress Timeline
Recommendation Share Over Time
Remediation Pages
Pages AIsubtext Deployed 0
Each page is crafted to answer a specific query and get picked up by AI engines.
Recommended Actions
What You Can Do to Boost Visibility
These are channels where your brand is missing or underrepresented. Each card includes ready-to-use content you can publish with minimal effort.
How It Works
Understanding Traffic Signals
Referrals, crawlers and inferred activity are different signals. A crawler visit does not demonstrate that a person arrived from an AI recommendation.
Layer 1
Referrer Headers
When an AI engine links to our content, the HTTP referrer reveals the source. We match against known AI engine domains.
Direct referrer signal
Layer 2
User-Agent Bot Signatures
AI engines use crawlers with distinctive User-Agent strings. We identify GPTBot, PerplexityBot, ClaudeBot, and others — even when referrers are empty.
Crawler activity, not a human referral
Layer 3
Behavioral Fingerprinting
When both referrer and UA are inconclusive, we score 7 behavioral signals: missing Accept-Language, no cookies, no Sec-Fetch headers, and more.
Heuristic inference, source unverified
Why this matters: A source citation, a crawler request and a human visit answer different questions. Do not combine them into a single claim of traffic or conversions.
Self-Improving System
Closed-Loop Feedback — Content That Learns
AIsubtext doesn't just deploy content and hope. Each remediation cycle feeds performance data back into the next generation of content. The system gets smarter with every scan.
1
Scan
Measure visibility across engines
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2
Generate
Create counter-move pages
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3
Track
Monitor AI referrals & citations
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4
Learn
Feed results into next cycle
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What the feedback loop does:
• Replicates what works: Pages that got cited by AI engines inform the style/structure of future content
• Drops what doesn't: Pages deployed 14+ days ago with zero citations are flagged for revision
• Skips won queries: Queries already highly cited are deprioritized to focus effort on gaps
• Adapts per engine: Performance data per engine tunes content for each AI model's preferences
A repeatable workflow. Scan, review, make changes and measure again. Results remain subject to changes in AI systems and available sources.
This evidence is generated automatically by AIsubtext.