Customer Proof Point · The Hidden Revenue
M
Multi-entity Midwest US staffing group

Where the hidden revenue was hiding.

A $220M, five-EIN staffing group recovered $957,000 of annual hard-dollar cost and built the AI-ready operating foundation underneath it. Payback in four months. 30% EBITDA expansion - on the operation they already had.

Revenue
$220M+ annual
Workers / week
4,500+ W-2
Corporations
5 EINs
Footprint
Multi-state Midwest
Verticals
Light industrial · Clerical
The outcome, in four numbers

What seven months of disciplined sequencing produced.

$957K
Annual hard-dollar savings
Back-office labor + branch capacity recapture
4mo
Payback period
On $320K Year 1 platform investment
$4.26M
Annual EBITDA contribution
Hard dollars + GP margin expansion
~30%
EBITDA expansion
Against $14.3M baseline EBITDA
The Command Centre

From flying blind to real-time operating intelligence.

One dashboard. Role-based views. Anchored on a single North Star metric. This is what the operation looks like seven months after the first decision.

Jombone Command Centre · Operating Snapshot
Live · Updated 2 min ago
North Star · Revenue per Recruiter
Annualized · rolling 12-month
$1.42M
+18% vs prior quarter
Shift Fill Coverage
97.4%
target >95%
Time to Fill
2.8 hrs
high-volume orders
Payroll Exceptions
1.6%
target <2%
Weekly Placements
4,547
across 5 EINs
Operating KPIs · rolling 4 weeks
Timesheet error rate
2.1%
Back-fill rate
98.6%
Workers per back-office FTE
1,094
GP margin (rolling)
14.3%
Time captured at source
99.2%
DSO (invoiced)
Day 1
How the Group did it

Three chapters. One sequencing discipline.

01 The Group

Five EINs. One shared operating problem.

The Group operates five corporations under a shared services holding structure built to pool back-office cost across the operating companies. Each corporation runs its own brand, sales motion, and EIN. Corporate handles payroll, finance, IT, compliance, and accounting as a centralized capability. The model has worked for more than a decade. What changed was the cost of running it on the systems that got the Group to its current scale.

4,500+ weekly W-2 workers. Multiple weekly pay runs - some entities running back-to-back batches every week to meet client-specific timing. Branch staff absorbing 30-40% of their selling capacity into payroll prep. No real-time operational visibility anywhere in the system. By the time the executive team began the diagnostic, the chain had been running for over a decade.

"We didn't have a software problem. We had an operating model problem. The software just made it visible."
Group CEO · Multi-entity Midwest US staffing group
02 The first decision

Standardize where the data is captured. Everything downstream follows.

The transformation could have started in any number of places - back-office payroll engine, ATS replacement, ERP modernization, AI agents at the top. Each option had vendors lined up to sell it. The Group's leadership made a deliberately tactical choice: begin with the upstream - the one place a single change would propagate across the entire operation.

The first decision
Standardize time clocks and timesheets across every branch and every operating company - before touching anything else in the stack.

Before the decision, time was captured on paper at one branch, on tablets at another, on iPads in a third location, and in branch spreadsheets at the rest. None of it validated at source. All of it re-keyed downstream into the back-office stack. Every upstream gap multiplied into every downstream cost.

With standardized native time clocks deployed at the largest operating company first, then replicated across the remaining four EINs in sequence, the Group rebuilt the foundation in three months. One clean upstream data layer now feeds three downstream workflows simultaneously: branch operations get real-time visibility, the back office gets validated data ready to process, and client workflows get faster, more accurate billing and reporting.

  • Native time clocks at branch entry · validation at source · zero re-keying
  • Branch staff returned to revenue work · payroll prep under 10% of the week
  • Timesheet exception rate from above 8% to 2.1% in eight weeks
  • Workers per back-office FTE from ~280 to 1,094 in seven months
  • DSO from Day 5-7 invoicing to Day 1 invoicing on validated data
"We had been waiting for a perfect platform decision. What we needed was the right first decision. Standardizing how the data was captured was where everything else became possible."
Group COO · Multi-entity Midwest US staffing group
03 The business case

Why do anything? Because the cost of doing nothing was higher.

The discovery work produced a bottoms-up business case the executive team could defend, line by line. Three layers, each independently calculated, each cross-checked against the Group's own operational data.

Current state
Back-office labor + branch leakage 16 FTEs at $55K loaded cost + 4,500 workers × 3 min/week of branch payroll prep
$1.25M / yr
Target state
Same operational throughput, lower labor base ~4 FTEs at AI-native benchmark + near-zero branch leakage
$319K / yr
Transformation value
Hard-dollar savings + GP margin expansion $957K labor + $3.30M GP margin recapture · ~30% of $14.3M EBITDA baseline
$4.26M / yr

Beyond hard dollars, the diagnostic put a number on every period of continued deferral. $80K every month. $240K every quarter. $958K every year. At mid-market staffing industry multiples of 5-7x EBITDA, the recurring annual contribution translates to $15-28M of enterprise value the platform creates every year it operates. Each year of deferral had been a year of forgone enterprise-value compounding.

"Once we quantified what every month of waiting cost us, the conversation stopped. The investment paid back inside one fiscal quarter. The only question was sequencing."
Group CFO · Multi-entity Midwest US staffing group
The full case study

14 pages. The diagnostic, the math, and the sequencing.

This page is the summary. The full case study is the operating manual - the line-by-line diagnostic, the bottoms-up business case, the cost-of-inaction math, and the AI-readiness sequencing that turned a legacy operation into a 30% EBITDA expansion in seven months.

  • The eight cascading problems · how each one fed the next
  • The diagnostic table · the Group vs. traditional staffing vs. AI-native operations
  • North Star metric selection · why Revenue per Recruiter
  • The three-phase rollout · 7 months from first decision to AI-readiness
  • Cost of inaction by month, quarter, year, and 3-year cycle
  • The five-layer AI-readiness stack · why foundation precedes deployment
  • Eight before-and-after categories with committed targets
  • The four selection factors that decided the platform choice
14 pages · PDF
J
Jombone
The Hidden Revenue · Customer Proof Point

Where the hidden revenue was hiding.

A $220M, five-EIN staffing group recovered $957,000 of annual hard-dollar cost and built the AI-ready operating foundation underneath it.

For
CEO · CFO · COO
Read time
8 minutes
Gated download

Get the 14-page case study.

The diagnostic, the math, the sequencing. The same blueprint that produced $957K of annual savings and a 4-month payback at one of the most operationally complex staffing groups in the Midwest.

$957K
Annual hard-dollar savings
4 mo
Payback period
$4.26M
Annual EBITDA contribution
~30%
EBITDA expansion
Your information is used only to send the case study and follow up. We don't sell or share contact data. Read our privacy policy.

Send me the case study.

14-page PDF. Delivered to your inbox in seconds.

We respect your inbox. One follow-up email if relevant, never marketing spam.

The math is on your table too.

If you run a multi-entity staffing operation, this case study isn't a curiosity - it's a working blueprint. Book a 30-minute working session and we'll model the same business case against your operating numbers.