You selected {{platform}} ({{subtitle}}) and enabled {{streamsSentence}}. On RUN, the 8-step Loop A certification pipeline executed in sequence — Entities → KPI Set → Execute → Reconcile → Rules → MQ6 → Score → Seal — and returned a Trust Score of {{ts}}/100 · {{gradeLabel}}.{{uploadSentence}}
| STREAM | UNCERTIFIED | CERTIFIED | CONDITION OBSERVED |
|---|---|---|---|
| {{r.name}} | {{r.uncert}} | {{r.cert}} | {{r.note}} |
MGE resolved every record against its canonical entity ontology, loaded the versioned KPI formulas for {{platform}}, executed them in context, and reconciled the claimed values against independent sources. It then evaluated the applicable deterministic rules — each one named, each one binary. The findings below are not anomalies or confidence intervals; they are specific rules that fired against specific data conditions, each carrying its dollar exposure.
Every finding came with its exit path — the FIX IT work order, in priority order, with the Trust Score points each action recovers:
{{sealSentence}}
This run was executed as a controlled A/B experiment: identical input data down two paths — column A with MGE bypassed (what {{platform}} self-reports today), column B with MGE on. Every delta below is attributable to MGE alone.
| DIMENSION | A — MGE BYPASSED | B — MGE ON |
|---|---|---|
| {{ab.label}} | {{ab.a}} | {{ab.b}} |
The metrics that run every restaurant — prime cost, labor %, food cost % — are estimates computed from inputs that have never been formally verified. MGE calls the downstream cost of acting on them CAC™: for a 10-unit operator, $28,000–$84,000 annually, not from fraud but from measurement. What this test demonstrated is the alternative: inputs certified before decisions are made, failures priced and named, and certified outputs that stand on their own — auditable by a third party without FohBoh in the room.
The timing is the point. Enterprises are beginning to let AI agents make consequential operational decisions, and every copilot and forecast consumes exactly these metrics. Errors in uncertified inputs do not disappear inside AI systems — they scale. A deterministic, independent, legally defensible trust layer under that data is not a reporting improvement; it is the infrastructure the AI era requires.