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mAI Evidence System
Evidence before claims
A transparent methodology for separating demonstrated capability, applied engagement evidence, operating targets and claims that require additional validation.
What this page is for
mAI is designed around measurable operating decisions. Evidence should therefore state what was measured, in what context and over what period, including where the limits of that evidence begin.
The core rule
A demonstration is not a client result.
An engagement result is not a universal benchmark.
An operating target is not a guarantee.
A compliance capability is not a blanket compliance claim.
Four evidence classes
01
Demonstration
Shows how an mAI workflow, decision pattern or architecture can operate. Capability evidence only, not proof of a client relationship or realized business outcome.
02
Applied Case Study
Describes an anonymized engagement and documented outcomes associated with that operating context. Results remain engagement-specific.
03
Operating Target
P² planning objective
Approximately 15–20% Productivity improvement and 10–25% Precision improvement over 90 days against an agreed baseline. Actual outcomes vary.
04
Validated Claim
A statement supported by an appropriate source, documented measurement or implementation evidence and reviewed for the context in which it is communicated.
The mAI Measurement Contract
Before interpreting performance, establish the measurement contract. This gives the AI Marketing Brain a reliable decision frame and gives the AI Marketing OS a consistent Plan → Execute → Measure → Optimize loop.
Baseline + KPI
Define the starting condition, comparison period, KPI definition and calculation method.
Window + Context
State the observation period and record material changes in budget, channel mix, market, offer, audience or data quality.
Confidence + Governance
Separate association, experiment evidence and causal conclusions. Keep Human Command over material strategy, budget, audience, brand and regulated decisions.
How results should be communicated
Public case studies may be anonymized to protect confidentiality. Reported outcomes should identify operating context and measurement period when available, avoid implying independent audit unless supported, and never generalize engagement-specific outcomes into promises of future performance.
Regulated environments
mAI can be configured with governance, access, approval and data-handling controls. Compliance depends on the specific deployment, data flows, contracts, infrastructure, organizational controls and applicable legal requirements; it is validated per implementation.
P²: Productivity
time_to_launch · ops_hours_saved · reporting_latency · automation_coverage
P²: Precision
CTR · CVR · CPA/CPL · ROAS/ROMI · LTV · retention · pipeline quality · agreed business KPIs
Targets only become meaningful when tied to a documented baseline and measurement design.
EVIDENCE CLASSIFICATION
Know what kind of evidence you are reading.
Real-world evidence reflects production work where publishable. Anonymized evidence protects identities or sensitive data. Controlled tests demonstrate system behavior without implying client outcomes. Methodology defines baselines, attribution limits and claims validation.
STRATEGIC DILIGENCE · Technology → Architecture → Evidence → Deployment → Leadership
