Illustrative Scenario — Revenue Growth
AI Voice Agents for Facilities Lead Conversion
How a $19M facilities management company deployed an AI voice agent to capture after-hours enquiries, qualify leads, and book site inspections — growing new contract revenue by $2.8M.
Company Profile
Sector
Commercial facilities management
Services
Cleaning, maintenance, security, grounds
Revenue
$19M
Employees
120 (incl. 90 field staff)
Ownership
Founder-led, 15 years in operation
Exit Timeline
Considering sale within 2 years
A well-established facilities management company servicing commercial office buildings, retail centres, and strata properties. The business had a strong reputation and high retention rates with existing clients, but was struggling to grow. New business development depended entirely on the founder's personal network, and the company was losing a significant number of inbound enquiries that arrived outside business hours or during peak periods.
The Revenue Problem
The business was profitable but had flatlined at $19M for three years. Revenue growth had stalled, and the issues were structural — not market-driven. Several factors were leaving revenue on the table.
Lost after-hours enquiries
Roughly 40% of inbound calls arrived outside office hours — evenings, weekends, and early mornings. These went to voicemail. Analysis showed that only 15% of voicemails were returned within 24 hours, and conversion from voicemail to booked inspection was under 8%.
Slow lead response during business hours
During peak periods, the two admin staff responsible for answering phones were also handling scheduling, invoicing, and client queries. Average response time to a new enquiry was 4+ hours. Industry data suggests that responding within 5 minutes increases conversion by 8x.
Founder-dependent sales process
All quoting, site inspections, and proposal writing was done by the founder personally. There was no sales pipeline, no CRM, and no follow-up process. The founder estimated they had capacity to pursue roughly 30% of qualified opportunities.
No lead qualification
Every enquiry — whether a $500/month cleaning contract or a $15,000/month full-service agreement — received the same treatment. There was no process to prioritise high-value leads or disqualify poor fits early.
No visibility into lead pipeline
The business had no data on how many enquiries it received, where they came from, what happened to them, or what the conversion rate was. It was impossible to demonstrate revenue growth potential to a buyer.
What We Implemented
Over 10 months, we deployed an AI-powered lead capture and conversion system designed to turn the business's existing enquiry volume into revenue.
AI voice agent for 24/7 call handling
Deployed an AI voice agent that answered every inbound call — day, night, and weekends. The agent was trained on the company's service offerings, pricing frameworks, and qualification criteria. It could hold a natural conversation, ask qualifying questions (property type, service needs, contract size, timeline), and book a site inspection directly into the founder's calendar. After-hours call capture went from 15% to 98%.
Intelligent lead scoring and routing
Built an AI system that scored each enquiry based on contract value potential, property type, location, and urgency. High-value leads were flagged for immediate follow-up. Low-value or poor-fit enquiries were handled with automated responses, freeing the founder to focus on the opportunities that mattered.
Automated proposal generation
Created an AI-assisted system that generated tailored proposals from site inspection notes and service requirements. What previously took the founder 3–4 hours per proposal was reduced to 30 minutes of review and personalisation. The business went from producing 3–4 proposals per week to 10–12.
CRM and pipeline automation
Implemented a structured pipeline with automated follow-ups, status tracking, and conversion analytics. For the first time, the business could see every lead from first contact to signed contract — and could demonstrate this pipeline to prospective buyers.
Automated client feedback and referral system
Deployed AI-driven post-service surveys and a structured referral request process for satisfied clients. Referral-sourced leads increased from near-zero to 22% of the pipeline, with a 3x higher close rate than cold enquiries.
The Outcome
The revenue growth transformed the business's profile from a stable, mature operation into a demonstrably growing platform — exactly what buyers look for.
| Metric | Before | After | Change |
|---|---|---|---|
| Revenue | $19.0M | $21.8M | +14.7% |
| EBITDA | $2.1M | $3.2M | +52% |
| EBITDA Margin | 11.1% | 14.7% | +3.6pp |
| New Contract Revenue | $1.2M/yr | $4.0M/yr | +233% |
| Valuation Multiple | 3.6x (est.) | 5.4x (achieved) | +1.8x |
| Enterprise Value | $7.6M (est.) | $17.3M (achieved) | +128% |
| Enquiry-to-Contract Rate | ~8% | ~24% | +16pp |
In this scenario, the combination of revenue growth and improved margins transformed the buyer's perception of the business. It sold at a 5.4x EBITDA multiple — a premium reflecting not just current earnings but a demonstrable growth trajectory.
This is an illustrative scenario based on a composite of common patterns in facilities management. 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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