Illustrative Scenario
Field Service & Maintenance
How a $14M equipment maintenance company increased EBITDA by 52% and attracted three competing offers — closing at a 4.8x multiple.
Company Profile
Sector
Commercial equipment maintenance
Services
Preventive maintenance, reactive repair, compliance inspections
Revenue
$14M
Employees
72 (incl. 48 field technicians)
Ownership
Husband-and-wife founders, 11 years
Exit Timeline
Planning to sell within 18 months
A mid-sized equipment maintenance company servicing commercial kitchens, laundries, and refrigeration systems across a major metro area. The business had a loyal customer base, high repeat rates, and strong technician retention — but was running on manual processes that limited scalability and suppressed margins.
The Challenges
The business had strong fundamentals but was operationally constrained. Manual processes were creating cost drag, and the owners' involvement in daily operations made the business difficult to transfer.
Paper-based job management
Technicians used paper work orders, which were manually entered into the system at the end of each day. Data entry errors, lost paperwork, and delayed invoicing cost the business an estimated $320K annually in unbilled work and late collections.
Reactive scheduling only
The business had no preventive maintenance scheduling engine. Work was dispatched reactively, leading to emergency callouts that were costly to service and disruptive to planned work. Preventive maintenance compliance sat at just 68%.
Owner bottleneck
One founder managed all client relationships and pricing decisions. The other managed technician scheduling, training, and HR. Neither role had been delegated or documented — a significant red flag for any buyer.
Flat-rate pricing disadvantage
Most contracts were priced on flat monthly rates set years earlier. No systematic review process existed. Roughly 40% of contracts were underpriced relative to actual service delivery costs.
No contract-level profitability data
The business could report total P&L but had no visibility into which contracts were profitable and which were subsidised. This made it impossible to demonstrate earnings quality at the contract level.
What We Implemented
Over 16 months, we implemented five AI and automation solutions designed to eliminate the margin drag and reduce operational risk before the business went to market.
Digital job management and mobile capture
Replaced paper work orders with a mobile-first system. Technicians captured job data, photos, and sign-offs on-site. Data flowed directly into billing. Unbilled work dropped from $320K to under $40K annually.
AI-driven preventive maintenance scheduling
Built an automated scheduling engine that generated and dispatched preventive maintenance visits based on contract terms, equipment age, and historical failure patterns. PM compliance rose from 68% to 94%, reducing emergency callouts by 35%.
Contract profitability analytics
Deployed automated tracking of actual labour hours, parts costs, and travel time against each contract. For the first time, the business could see which contracts were profitable and which needed renegotiation. 12 underperforming contracts were repriced, recovering $280K in annual margin.
Automated pricing review system
Implemented an AI-assisted system that flagged contracts due for review based on service cost trends, CPI, and margin thresholds. Manual pricing reviews were replaced with data-driven recommendations that management could approve in minutes.
Workflow documentation and role standardisation
Used AI to map and document all operational workflows, then built standardised procedures for dispatching, escalation, client communication, and reporting. Both founders' roles were documented and transitioned to senior managers over a 6-month period.
The Outcome
The business went to market with 16 months of improved, auditable performance data. Three buyers submitted offers, creating competitive tension that drove the final multiple above initial expectations.
| Metric | Before | After | Change |
|---|---|---|---|
| Revenue | $14.0M | $15.1M | +7.9% |
| EBITDA | $1.5M | $2.28M | +52% |
| EBITDA Margin | 10.7% | 15.1% | +4.4pp |
| Valuation Multiple | 3.2x (est.) | 4.8x (achieved) | +1.6x |
| Enterprise Value | $4.8M (est.) | $10.9M (achieved) | +127% |
| PM Compliance | 68% | 94% | +26pp |
| Unbilled Work | ~$320K/yr | ~$40K/yr | -88% |
In this scenario, the business was acquired at a 4.8x EBITDA multiple, achieving an enterprise value of $10.9M. The digital job management system and contract-level profitability data were key factors in the buyer's confidence to pay a premium.
This is an illustrative scenario based on a composite of common operational patterns in field service and maintenance. It does not represent a specific client engagement, and the figures shown are not guaranteed outcomes. Every business is different.
Is your business facing similar challenges?
We begin with a confidential discussion to assess where AI can have the greatest impact on your EBITDA and exit value.