Client Results

What Happens When the Numbers Get Fixed

All engagements are kept strictly confidential. The results below are real — drawn directly from client work. Segments and identifying details have been changed.

Case Study 01 · 12-week engagement

Prime Cost Reduced 7 Points in 90 Days

5-unit fast casual group, Washington DC Metro

7pts

Prime cost reduction

$68K

Annual vendor savings

34%

Overtime reduction

90 days

Engagement timeline

The Challenge

This operator's prime cost had crept to 71% — nearly 10 points above their target — over 18 months of growth. They had expanded quickly from 2 to 5 locations and the financial structure hadn't scaled with them. Food cost variance between theoretical and actual was running at 4 points, and nobody on the team could explain where the gap was coming from.

The Approach

We started with a full P&L diagnostic across all five units, identifying that the variance was concentrated in three high-volume categories with inconsistent portioning and unreconciled vendor invoices. We rebuilt their food cost tracking system, renegotiated contracts with their two largest vendors, and installed a weekly prime cost review process with the GM team.

Outcomes

Prime cost reduced from 71% to 64% over 12 weeks
Food cost variance brought down from 4.1 to 0.8 points
Vendor contract renegotiation produced $68,000 in annualized savings
Labor scheduling rebuild reduced overtime by 34%
Weekly P&L review process adopted across all 5 units

Case Study 02 · 6-month engagement

Google Rating From 3.6 to 4.5 in Six Months

Full-service restaurant, Northern Virginia

4.5

Google rating (from 3.6)

61%

Fewer service complaints

18%

Repeat visit increase

91%

Mystery shopper compliance

The Challenge

A well-established full-service restaurant in Northern Virginia was watching their Google rating slip despite strong food quality. Review analysis revealed a consistent pattern: service pacing complaints concentrated on Friday and Saturday dinner service, and a specific frustration with how long waits were communicated. The owner had tried to address it anecdotally but without data to drive specific changes.

The Approach

We ran a Guest Intelligence Audit analyzing 18 months of reviews across Google, Yelp, and TripAdvisor, benchmarked against three direct competitors, and identified the exact friction points. We followed with a mystery shopper program across four visits to validate the patterns in person. The findings drove specific service SOPs for the host stand and expo position.

Outcomes

Google rating improved from 3.6 to 4.5 over 6 months
Service-pacing complaints dropped 61% in post-engagement reviews
Mystery shopper scores improved from 72% to 91% compliance
Repeat visit rate increased 18% based on reservation data
Owner now has a quarterly review intelligence cadence in place

Case Study 03 · 8-week implementation

12% Labor Cost Reduction Through AI Scheduling

Growing multi-unit group, DC and Maryland

12%

Labor cost reduction

38%

Overtime hours cut

7hrs

Weekly scheduling time saved per location

Daily

Automated P&L reporting (vs. weekly)

The Challenge

A three-location operator in DC and Montgomery County was spending 7–9 hours per week per location on manual scheduling — and still missing labor targets. Their POS, scheduling tool, and accounting software didn't talk to each other. Every Monday morning started with the same question from ownership: where did labor go this week? Nobody had a clean answer until Thursday.

The Approach

We implemented an AI scheduling platform integrated directly with their POS sales data, configured automated labor threshold alerts, and built a daily reporting pipeline that put P&L-relevant numbers in the owner's inbox by 8am every morning. The scheduling tool was trained on their actual sales patterns, daypart volume, and role-by-role labor requirements.

Outcomes

12% reduction in total labor cost across all three locations
Scheduling time reduced from 7–9 hours/week to under 90 minutes
Overtime hours dropped 38% in the first 60 days
Labor cost visibility moved from weekly lag to real-time
Owner receives automated daily P&L summary at 8am every morning

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