Case studies

Real Restaurants. Real Numbers.

They had the reports. Marty connected the systems they already used and found the patterns those reports did not show on their own.

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Marty customers

  • Little Caesars
  • Buffalo Wild Wings
  • Scooter's Coffee
  • Hal's Steakhouse
  • Burger King
  • The Ivory Peacock
  • Fielding's

Featured customer

Hal’s Steakhouse

One location · Atlanta, Georgia

Reported overtime premium fell 52.6%.

Jan–Apr 2026 vs. May–Jul 2026

Reported overtime premium by month, 2026
Month, 2026Reported overtime premium
Jan$2,210
Feb$2,148
Mar$2,287
Apr$2,139
May$1,312
Jun$987
Jul$822
Monthly average, Jan–Apr
$2,196
Monthly average, May–Jul
$1,040
Sales YoY, Atlanta, same period
+4.1%

Reported comparison. No causation claimed. Results vary.

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Customer conversations

Real Owner Calls

Short clips from real conversations about labor, food cost, discounts, vendors and the problems that reports often miss.

20 clips · under a minute each

Customer conversation excerpts shared with permission. Details may be redacted. These are questions, reactions and operating lessons—not all verified results.

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More owner stories

Different Restaurants. The Same Blind Spots.

Every number is paired with its source, period and context.

National Restaurant Group · Four locations, one owner

Marty found a $96K gap between cash sales and bank deposits.

$96K in cash was rung but never tied out to a deposit.

Based on six months of POS cash and deposit records across four locations.

What Marty found

One store banked 93 cents of every cash dollar. Its sister store banked 17.

  • Six months of POS cash checked against the deposit logs at all four locations.
  • One location rang $71,147 in cash. The log accounted for about $11,964 of it.
  • For every $6 rung in cash there, the log showed about $1.
  • A sister store with the same owner and same setup banked 93 percent over the same months.

Each deposit looked normal on its own. Nobody tied six months of cash rung against cash banked.

“I am disappointed due to lack of control on my side.”

Multi-Concept Hospitality Group · Flossmoor, Illinois

Marty found $52K a month in profit problems.

$52K a month was slipping away.

Based on 30 days of POS, labor, and reservation data.

What Marty found

They were not missing reports. They were missing the connection.

  • Too many people were clocked in before customers showed up.
  • Prime dinner tables sat empty during the rush.
  • A few locations were quietly worse than the rest.

The reports showed labor and sales separately. Nobody connected them.

“We had the reports. We just never saw the full picture.”

Winery and Event Venue · Miami, Florida

Marty found $120K a year lost on busy events.

$120K a year was still being lost.

Based on 45 days of POS, event, and labor data.

What Marty found

Busy never meant profitable.

  • Staffing did not match how busy each event actually was.
  • Some sold-out events still lost money.
  • The schedule moved profit more than anyone realized.

Each event looked fine on its own. Nothing tied sales, labor, and timing together.

“Busy did not mean we were making the money we thought.”

Full-Service Restaurant and Bar · Houston, Texas

Marty found one shift costing $180K a year.

$180K a year.

Based on 60 days of POS and labor data.

What Marty found

This happened every single week.

  • The same shift was overstaffed week after week.
  • Overtime kept showing up in the same place.
  • Strong sales days were still losing margin.

Weekly reports averaged the bad shift away.

“This was not one bad week. It was the same problem over and over.”

Late-Night Bar Group · New Orleans, Louisiana

Marty found $104K a year lost after midnight.

$104K a year disappeared after midnight.

Based on 30 days of POS and labor data.

What Marty found

They had the least visibility when the risk was highest.

  • Staff stayed on the clock long after the rush ended.
  • The same late hours lost money every night.
  • No one was watching when it mattered most.

The nightly total looked fine. The hours inside it did not.

“The money was made late at night. So were the mistakes.”

Multi-Location Restaurant Group · Wilmington, Delaware

Marty found a $74K yearly labor gap between stores.

$74K a year was hiding in the gap between them.

Based on 30 days of POS and labor data across locations.

What Marty found

They would never have found this looking at one store.

  • Two similar stores spent very different amounts on labor.
  • Some managers paid more people for the same sales.
  • The biggest gap showed exactly where to start.

One store at a time, the gap was invisible. Side by side, it was obvious.

“I did not see it until the stores were side by side.”

Independent Restaurant and Bar · East Setauket, New York

Marty found $63K a year slipping past a hands-on owner.

$63K a year still slipped past him.

Based on 45 days of POS, labor, and cash data.

What Marty found

Even a hands-on owner cannot watch every shift.

  • The same labor mistakes repeated by day and shift.
  • The cash drawer did not always match the sales.
  • The fastest fixes were sitting in plain sight.

One person cannot watch every shift, every system, every day.

“I cannot watch every shift. This showed me where to look.”

Anonymous Restaurant · Location withheld

Delivery went offline. The prices were still wrong.

$18,956 in estimated one-time lost sales.

$13,584 annualized exposure. $6,800–$13,600 modeled opportunity, not verified recovery.

What Marty found

Downtime rose from under 1% to 57% while delivery orders fell from about 625 to 175.

  • Delivery downtime rose to 57% as order volume fell.
  • Ten items produced weaker economics after fees than their in-store prices.
  • The availability and pricing reports were showing two sides of one commercial problem.

Availability and pricing lived in separate reports, so nobody saw the combined effect on demand and unit economics.

“The problem was not only that delivery went offline. The orders that remained also had weaker economics.”
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Anonymous Restaurant · Single location

Kiosk adoption rose. Cashier hours rose too.

$10K–$20.5K in modeled annual opportunity.

Based on aggregate transaction and labor records. Modeled opportunity; results not yet verified.

What Marty found

Kiosks grew from 0% to 24% of transactions while monthly cashier hours increased from 643 to 808.

  • Nearly one in four transactions moved to kiosks.
  • Monthly cashier hours increased by 165 rather than declining.
  • Adoption and labor were reported separately, hiding the operating gap.

The standard reports did not connect kiosk transaction share to cashier staffing by daypart.

“The kiosk metric looked like progress. The cashier-hour trend showed that the operating model had not captured the expected benefit.”
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Modeled opportunities are labeled as modeled. Reported results include their comparison period. Marty does not claim every opportunity becomes recovered cash.

“Marty saves us hours every week. The platform is intuitive, the team is fast, and whenever something comes up they have it fixed before I can worry about it.”

Timothy TowOwner, Bin 35 Bistro

What Would Marty Find in Your Restaurants?

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