Detection, Diagnosis, Resolution (DDR)

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.