Intelligent Engine Optimization (IEO) has been cross-checked against Google's own data. Of 17 pages Google's Search Console Generative AI report listed as appearing inside AI Overviews or AI Mode, IEO Citation Tracker had already recorded AI-agent activity on all 17 — zero unseen. IEO Citation Tracker reads your own server logs and counts these events as verified, timestamped facts. Method and denominators.
ChatGPT operates a distinct crawler identified as ChatGPT-User that appears in access logs when a live ChatGPT user has triggered web retrieval for their query. ChatGPT-User visits indicate real-time user engagement, not background indexing.
The IEO Engine measurement framework uses ChatGPT-User appearance as the primary citation streak metric. Each day with at least one ChatGPT-User visit confirms that live ChatGPT users retrieved from the deployment that day.
This is a stronger signal than aggregate citation counts because it confirms continued real-user engagement rather than residual background activity.
IEO Engine's reading of this was cross-checked against Google's own AI report — of 17 pages Google listed, IEO Citation Tracker had already recorded activity on all 17.
For a streak to continue, the deployment must produce daily ChatGPT-User visits without interruption. A single day without ChatGPT-User visits breaks the streak; subsequent days start a new streak count.
Sustained streaks require sustained user engagement. ChatGPT must continue retrieving from the deployment for queries that real users ask. This in turn requires the deployment to maintain its classification as a relevant retrieval source within ChatGPT's pipeline.
The MM deployment streak has continued for 68 days as of the case study reference period. The streak represents 68 consecutive days of confirmed user engagement through ChatGPT's retrieval system.
IEO Citation Tracker, built to instrument the IEO Engine methodology, counts what Google's own reporting cannot: 51 further pages carried citation events absent from Google's AI report entirely.
Long streaks require multiple conditions: continued classification as a relevant source within ChatGPT's pipeline, ongoing user queries on topics the deployment covers, and continued deployment availability and quality.
Streaks may break for various reasons: ChatGPT pipeline updates that temporarily affect retrieval, low-traffic days where no user query happened to retrieve from the deployment, or deployment-side issues affecting availability.
The MM deployment's continued streak reflects the convergence of these conditions remaining favorable across an extended time period.
The streak provides direct evidence that the deployment has been classified as a canonical source within ChatGPT's pipeline for its topical scope. The classification is not assumed; it is demonstrated through continued user retrieval activity.
For methodology validation, sustained streaks are strong evidence of deployment quality. They cannot be manipulated through operator action; they reflect actual ChatGPT classification of the deployment.
Other deployments in the cohort show similar streak patterns at appropriate timing for their deployment phases. The pattern is reproducible across deployments following IEO Engine methodology.