The business serviced 600+ clients on annual maintenance contracts — fixed visits on a calendar, regardless of how each compressor was actually running. Growing the client base meant adding technicians in step; healthy machines were serviced on schedule while stressed ones failed between visits; and there was no remote view of any client's machine.
Before building anything, we mapped the operation. These are the findings that shaped what we built.
Maintenance was calendar-based — every client was visited on a fixed cycle regardless of machine condition.
Growth was capped by technician headcount: more clients meant proportionally more scheduled visits.
Compressors that failed between visits became emergency call-outs — costly, unplanned, and bad for client trust.
The data foundation, and the AI agents that turn it into decisions and action.
An IoT device on each client compressor streams tank pressure, motor amperage and temperature to a central platform — continuous signal from the whole client base for the AI to read.
An agent correlates pressure-rebuild cycles with amperage to estimate motor ON-time and duty cycle, flags overuse, and scores each machine's burnout risk from abnormal current — so the business knows which client machines actually need attention.
An agent ranks the connected fleet by risk and builds the technician visit list by condition rather than calendar, pre-empting failures before they become breakdowns — letting the business serve more clients per technician.
Measured against the baseline the audit established — before and after, on the metrics that move the P&L.
Start with a Business Transformation Audit — a structured working session where we map where AI changes your P&L, prioritised by impact.