The problem they faced
A home services company with 20 technicians had outgrown their coordination system — bookings came in via phone calls and WhatsApp messages to a shared spreadsheet, dispatch was managed on a colour-coded calendar that didn't account for travel time between jobs, and job documentation (photos, work orders, parts lists) was scattered across technicians' personal phones. On busy weekends, double-bookings spiked because the shared spreadsheet had no conflict detection. Late arrivals were common because the calendar didn't factor in travel time between sites, leading to customer complaints and refund requests. End-of-day settlement required chasing screenshots and manual reconciliation between POS receipts and payment links. The owner estimated they were losing 2-3 large jobs per month simply because calls went unanswered while staff were on site.
What Phoenix AI built
Phoenix AI implemented a mobile-first field service system focused on the core workflows that mattered most. Incoming calls are answered by an AI receptionist that qualifies the job, captures details, and books the appointment with travel-aware slotting — the system calculates drive time between jobs and prevents scheduling conflicts. Technicians receive job assignments on their phones with checklists, client details, and estimated arrival times. Photo uploads and job completion notes are captured in-app, creating an auditable record for each job. Invoices are generated automatically from completed work orders and sent to the client, with payment links included. The owner gets a daily dashboard showing completed jobs, pending invoices, and tomorrow's schedule.
Outcomes delivered
Double-bookings were eliminated entirely within the first week. The owner recovered the 2-3 large jobs per month that were previously lost to unanswered calls. Dispatch coordination time dropped from 3 hours per day to under 30 minutes of review. Job documentation is now centralised and auditable — no more chasing technicians for photos or work order notes. The travel-aware scheduling reduced late arrivals by an estimated 60%, improving customer satisfaction scores. The system was deliberately kept focused on the core workflows — scheduling, dispatch, documentation, and invoicing — rather than forcing the team into a bloated platform they would only use 20% of.