A 12-person call desk, handed to one AI intake
A property-maintenance company serving residential real estate centres ran twelve phone operators just to register incidents. We automated the intake — saving about $550,000 a year while client satisfaction went up.
The challenge
Twelve salaries to answer the phone
Every leak, lift fault and electrical issue in the serviced buildings came in by phone. Twelve employees — at roughly $50,000 a year each, about $600,000 in payroll — spent their days doing one thing: picking up calls and typing incidents into the system.
No intake happened outside their shift, queues built up at peak hours, and the quality of each ticket depended on who picked up. The company was paying a fortune for a process that created no value beyond data entry.
What we did
Automated the intake end to end
- AI call intake — answers every call, asks the right follow-up questions and logs a structured, complete ticket.
- System integration — incidents register directly in the company’s existing ticketing workflow, no re-typing.
- Smart routing — urgency and category detected on the call, so the right crew gets the job first time.
- Human escalation — complex or sensitive calls hand over to an on-duty operator with full context.
- 24/7 coverage — residents can report an incident at 3 a.m. and it is in the queue before morning.
- Consistent quality — every ticket follows the same structure, so nothing is lost between the call and the crew.
Implementation specifics are generalized to protect the client’s identity under NDA.
The result
About $550,000 a year — off the cost of answering the phone
The intake now runs on AI. Calls are answered instantly at any hour, tickets arrive structured and routed, and the team’s budget goes into actual maintenance work instead of data entry. Satisfaction went up — residents get through faster and issues reach the right crew sooner.
Paying a team to type what callers say?
If your intake is phone calls and manual tickets, this is exactly what we automate — measured in payroll, not promises.