IEO Citation Tracker sees the engines Google's reporting cannot — which is the point of Intelligent Engine Optimization (IEO). Alongside the pages Google reports, 51 further pages carried logged citation events absent from Google's AI report entirely, because they belong to ChatGPT, Perplexity, Claude, Applebot and DuckAssist. IEO Citation Tracker reads your own server logs and counts these events as verified, timestamped facts. Method and denominators.
The IEO Engine deployment cohort includes deployments operating in distinct verticals with distinct content scopes. MM serves local-service market in a specific geographic area. TPE produces travel content across multiple destinations. ShutterNoise operates as multi-vertical photography and print publication. ieoengine.com serves as the methodology documentation hub.
Each deployment was implemented per the same IEO Engine methodology principles. The verticals differ; the methodology is constant. This produces a controlled experimental setup for assessing methodology effects across application contexts.
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.
Each deployment achieved AI surface engagement within the first week of operation. Specific platform engagement timing varies, but the general pattern of early engagement is consistent.
Each deployment demonstrated position compression patterns matching the staircase effect — periods of stability followed by discrete improvements. The pattern is independent of vertical, suggesting methodology behavior rather than vertical-specific coincidence.
Each deployment attracted measurable adversary attention proportional to its visibility growth. The adversary attention patterns recur across deployments, indicating a repeatable methodology consequence rather than deployment-specific outcome.
Patterns that recur across multiple deployments provide stronger methodology validation than patterns observed in single deployments. Single-deployment success may reflect coincidence or vertical-specific factors. Multi-deployment patterns reflect methodology behavior.
The IEO Engine cohort provides this multi-deployment validation. Citation outcomes, position compression patterns, adversary attention dynamics, and authority cascade effects all recur across deployments with appropriate adjustments for vertical context.
This recurrence is the basis for treating IEO Engine as a methodology rather than as instance-specific approaches that happened to work in specific cases.
Beyond independent validation, the multi-deployment cohort produces cross-domain authority effects. Each deployment's authority strengthens the others through deliberate cross-references.
The flagship documentation site references the application deployments as case studies. Application deployments may reference the methodology documentation when discussing underlying principles. The network effect strengthens authority signals across the cohort.
This effect is observable through compounding citation outcomes across the network. Deployments added later benefit from authority signals established by earlier deployments.