Trust Score
88

Eagle Eye Automation
EEOS
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EEOS Trust Engine
Mock trust measurement only. No APIs, no customer data, no production recommendation scoring.
Trust Score
88
Posture
Watch
Reviewed
42
91
Most mock recommendations include why, evidence, risk, confidence, and Business Health impact.
Create one canonical recommendation schema before production AI.
Improving
84
Decision History and Memory Engine preserve outcomes, but accuracy is not calibrated against live data.
Require actual outcome capture before production learning.
Stable
87
Mock owner decisions show positive business impact, but success criteria need one shared definition.
Define success by expected outcome, actual outcome, ROI, and Business Health movement.
Improving
89
Confidence appears consistently across Executive Confidence, Trust, and Decision Intelligence surfaces.
Calibrate confidence bands and evidence quality thresholds.
Stable
78
Predictions remain mock calculations and should not be trusted as calibrated forecasts.
Keep prediction accuracy labeled mock until live outcome tracking exists.
Stable
82
Mock approvals show owner interest, but acceptance is not connected to real user behavior.
Track approve, delay, reject, ask AI, and follow-through outcomes.
Improving
80
Audit requirements are documented, but persistent audit logging is not implemented.
Require audit records for recommendations, approvals, events, and integration attempts.
Improving
93
The Constitution, Trust Engine, and Decision Intelligence require recommendations to explain why.
Block production recommendations that lack evidence, risk, unknowns, and confidence.
Improving
Trust Activity
08:20 AM
Recommendation packet reviewed for evidence quality
Raised explainability score to 93.
Explainability Score
09:15 AM
Prediction flagged as mock-only
Kept prediction accuracy in Needs Review.
Prediction Accuracy
10:40 AM
Founder Approval outcome added to trust history
Improved decision success measurement coverage.
Decision Success Rate