Illustrative Scenario
HVAC, Electrical & Plumbing
How a $17M commercial HVAC and electrical contractor lifted EBITDA by 44% and exited at a 5.0x multiple — up from an estimated 3.5x.
Company Profile
Sector
Commercial HVAC and electrical services
Services
Installation, maintenance, emergency repair
Revenue
$17M
Employees
85 (incl. 62 technicians)
Ownership
Founder-led, 14 years in operation
Exit Timeline
Owner planning retirement, 2-year window
A commercial HVAC and electrical services company with a strong reputation in its metro region. The business held several long-term maintenance contracts with commercial property managers and had a growing emergency repair division. The founder, a qualified engineer, had built the business from a one-man operation and was now looking to retire.
The Challenges
Despite steady revenue, the business was leaving significant margin on the table. Operational inefficiencies were costing real money, and the founder's deep involvement in every aspect created a risk profile that buyers would discount heavily.
Inefficient dispatching
Technicians were dispatched based on availability rather than proximity or skill match. Average drive time between jobs was 48 minutes — well above the 25-minute industry benchmark. Fuel and labour waste ran to approximately $380K per year.
Margin erosion on quotes
Quoting was done manually by the founder and two senior estimators. Inconsistent pricing, underestimated job complexity, and missed scope items resulted in average realised margins 6–8 points below quoted margins.
Critical founder dependency
The founder approved all quotes over $3,000, managed the top 15 client accounts personally, and was the sole decision-maker on hiring and procurement. No buyer would pay a full multiple for a business this dependent on one person.
Reactive financial management
P&L was produced monthly with a 3-week lag. There was no visibility into job-level profitability, technician utilisation rates, or contract-level margins. The business could not demonstrate earnings quality.
Parts and inventory waste
No centralised inventory tracking. Technicians carried excess stock in vans, parts were frequently double-ordered, and the business was carrying $240K in slow-moving inventory with no system to manage it.
What We Implemented
Over 12 months, we deployed four AI and automation solutions targeted at the specific margin and risk issues identified in the assessment.
AI-optimised dispatching and routing
Replaced manual dispatching with an AI engine that assigned technicians based on proximity, skill certification, job priority, and real-time traffic. Average drive time dropped from 48 to 26 minutes. The business completed 12% more jobs per technician per week without adding headcount.
Automated quoting engine
Built a quoting system trained on 4 years of historical job data — materials, labour hours, complexity factors, and margin outcomes. Quotes were generated in minutes rather than hours, with realised margins improving from 22% to 29% on average.
Real-time job costing and dashboards
Deployed automated reporting that tracked profitability at the job, technician, contract, and client level in real time. Management could see margin erosion as it happened — not three weeks later. This data became a powerful asset during buyer diligence.
Inventory management automation
Implemented automated inventory tracking across all vehicles and the central warehouse. AI-driven reordering replaced manual stock checks. Excess inventory was reduced by $140K in the first year, and parts-related job delays dropped by 60%.
The Outcome
After 12 months, the business went to market with dramatically improved unit economics, reduced founder dependency, and granular financial visibility that gave buyers confidence.
| Metric | Before | After | Change |
|---|---|---|---|
| Revenue | $17.0M | $18.2M | +7.1% |
| EBITDA | $1.8M | $2.6M | +44% |
| EBITDA Margin | 10.6% | 14.3% | +3.7pp |
| Valuation Multiple | 3.5x (est.) | 5.0x (achieved) | +1.5x |
| Enterprise Value | $6.3M (est.) | $13.0M (achieved) | +106% |
| Avg. Drive Time Between Jobs | 48 min | 26 min | -46% |
| Realised Job Margins | 22% | 29% | +7pp |
In this scenario, the business was acquired at a 5.0x EBITDA multiple, with an enterprise value of $13.0M. The quality of operational data and the strength of the automated systems were noted as key differentiators.
This is an illustrative scenario based on a composite of common operational patterns in trade services. 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?
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