$ ~/ym8 --define detection-diagnosis-resolution
Detection, Diagnosis, Resolution (DDR)
definition
The DDR framework — Detection, Diagnosis, Resolution — provides a systematic approach to improving AI visibility. Rather than making broad, unfocused changes and hoping for improvement, DDR follows a structured process that identifies specific issues, determines their causes, and applies targeted solutions.
Detection is the monitoring phase: running query banks across AI engines, tracking Share of Model and Citation Rate, monitoring brand mention accuracy, and flagging anomalies. Detection answers "what is happening?"—your brand is missing from ChatGPT responses for category queries, your Citation Rate on Perplexity has dropped, or a competitor has displaced you in a key topic area.
Diagnosis is the analytical phase: investigating why a detected issue exists. If your brand is missing from ChatGPT responses, is it because your content isn't structured for AI extraction? Because a competitor published more authoritative content? Because your llms.txt is missing or inaccurate? Diagnosis requires both technical analysis and competitive intelligence.
Resolution is the action phase: implementing specific fixes based on the diagnosis. This might involve Technical AEO changes (adding structured data, fixing crawler access), content improvements (restructuring pages for answer-first formatting), or strategic actions (publishing comparison content, building citation sources). Each resolution is targeted at the specific root cause identified during diagnosis.
The DDR cycle is continuous—after resolution, you return to detection to measure whether the fix was effective, creating a feedback loop of continuous improvement.
why_it_matters
DDR provides the operational methodology that makes AEO actionable and measurable. Without a structured framework, AEO efforts tend to be scattered and unmeasurable. DDR ensures that every action is informed by data (detection), guided by analysis (diagnosis), and targeted at a specific outcome (resolution).
examples
- Detection: discovering that your brand is mentioned in only 5% of category queries on Claude. Diagnosis: your llms.txt is missing and Claude relies heavily on this file. Resolution: implementing llms.txt with comprehensive brand information.
- Detection: Citation Rate dropped 20% on Perplexity after a site migration. Diagnosis: new URL structure broke existing citations. Resolution: implementing redirects and updating content for the new URLs.
faq
How often should the DDR cycle run?
Detection should be continuous (or at least weekly). Diagnosis is triggered by detected issues. Resolution timelines vary by issue complexity—Technical AEO fixes may take days, content improvements may take weeks. The full DDR cycle should complete monthly at minimum.
What tools support the DDR framework?
The AEO Platform is designed around the DDR framework, providing automated detection (Share of Model monitoring, Citation Rate tracking), diagnostic analysis (competitive comparison, content gap identification), and resolution guidance (prioritised recommendations, impact scoring).
Related Terms
Share of Model
Share of Model (SoM) measures how frequently a brand is mentioned or recommended by AI engines in response to relevant queries. It is the AI-era equivalent of Share of Voice, quantifying your brand's presence across ChatGPT, Perplexity, Gemini, Claude, and other answer engines.
Citation Rate
Citation Rate measures the frequency at which an AI engine references a specific source domain when generating responses. Unlike Share of Model, which tracks brand mentions, Citation Rate specifically tracks when your website URL or domain is cited as a source.
Brand Mention Tracking
Brand Mention Tracking in AEO is the process of systematically monitoring when and how AI engines mention your brand in their responses. It goes beyond simple name detection to analyse context, sentiment, accuracy, and competitive positioning of each mention.
AEO (AI Engine Optimisation)
AEO — AI Engine Optimisation — is the practice of optimising a brand's digital presence to maximise visibility, accuracy, and favourability in AI-generated responses. It is the AI-era evolution of SEO, focused on how brands appear in ChatGPT, Perplexity, Gemini, Claude, and other answer engines.
Related Engines
Monitor Your AI Visibility
See how your brand appears with the default core pair. Start with ChatGPT and Claude by default. Expand monitoring only when the workflow needs it.