FohBoh | MGE — Deterministic Use Cases

The Cases

Six Recovery Patterns. One Engine.

Each case below is governed by the MGE rule registry. The narrative trace is a real pattern caught in production. The impact target is the variance band typically certified.

Use Case 01

Merchant Fee Recovery

Narrative Trace

"Recovered $32k annually after exposing hidden interchange padding in processor statements."

Impact Target

0.2% – 0.8% of card volume

What It Is

A forensic audit layer that scans Shift4 and other processor statements for hidden interchange markups, downgrades, and non-compliant overrides.

Why It Matters

Most operators never validate processor math. A single "Interchange Padding" line item can quietly drain an average of 0.5% or up to $32k+ annually without triggering any alert in the POS.

Use Case 02

Delivery Fee Recovery

Narrative Trace

"Detected a 6% commission drift caused by unmapped aggregator marketing fee line items."

Impact Target

3% – 10% of delivery sales

What It Is

Commission logic validation across DoorDash, Uber Eats, and first-party delivery channels to detect fee drift and unmapped marketing line items.

Why It Matters

Aggregators frequently update fee structures without notifying merchants. An average of 6% or up to $100k metric drift is invisible in P&L summaries but erodes margin on every single order.

Use Case 03

Prime Cost Governance

Narrative Trace

"Flagged inventory reclassifications where premium proteins were moved into non-COGS categories."

Impact Target

4% Margin Gain

What It Is

A control mechanism that flags inventory reclassifications, recipe manipulation, and "meal transfers" used to artificially deflate reported cost of goods sold.

Why It Matters

When a store manager reclasses 40lbs. of ribeye as a "marketing meal," food cost improves on paper — but real cash left the building. This closes the fiction gap.

Use Case 04

Revenue Recovery

Narrative Trace

"Flagged a server-level reopen/delete pattern linked to abnormal void and comp behavior."

Impact Target

2% Gross Recovery

What It Is

POS transaction-logic auditing that detects void/delete patterns, server-driven comps, and unauthorized discount stacking at the employee ID level.

Why It Matters

A 15% void rate from a single server ID is not hospitality — it is either theft or process failure. Flagging the "re-open and delete" pattern stops the bleed at the source.

Use Case 05

Labor Productivity

Narrative Trace

"Identified shift overlap during low-traffic windows and reduced labor waste by 12%."

Impact Target

10% Efficiency Gain

What It Is

Intraday scheduling logic that compares actual employee clock-ins against interval-level sales forecasts and historical utilization curves.

Why It Matters

Overlapping shifts during 9–11 AM dead zones create labor waste that P&L summaries smooth over. 12% reduction is achieved not by cutting heads, but by aligning bodies to traffic.

Use Case 06

Shrinkage Detection

Narrative Trace

"Found 9% steak variance tied to prep-yield loss that never hit formal waste logs."

Impact Target

15% Waste Reduction

What It Is

Yield-driven waste tracking that reconciles raw ingredient purchases against finished good sales, flagging variance not logged as formal spoilage or trim.

Why It Matters

If 9% of steaks disappear before they hit the guest check, the problem is not theft — it is prep execution. Capturing yield loss at the table level converts waste into training ROI.

Operational Recovery Cases.

Six Recovery Patterns. One Engine.