How a regional HVAC company stopped losing same day calls with AI dispatch triage

A residential HVAC company running 14 service trucks across two metro areas was losing same day work in the most frustrating way: their dispatcher couldn't triage the inbound call queue fast enough during peak season. Calls came in by phone, web form, and a third party booking widget, all routed through a single coordinator. The coordinator had to read each request, judge severity (no heat in February vs. routine maintenance), check truck availability, and route to a tech.

The result was that critical jobs (elderly customers with no heat, families with no cooling on a 95-degree day) sometimes sat in the queue behind routine work because the coordinator was doing five things at once. Customers who didn't get a same day call back called a competitor. The company tracked this loss but couldn't fix it without either adding staff or adopting a heavier dispatch system that the techs would resist.

We did not replace the dispatcher. We built a thin layer in front of her. Inbound requests from all three channels normalize into a single intake record. An LLM with a tightly scoped prompt classifies each request on three axes: severity (emergency / urgent / routine), system type (heat / cool / dual / water heater / other), and the customer's stated preference (today / this week / flexible). Severity uses explicit rules: 'no heat' and 'no cool' with weather thresholds always escalate; everything else uses the model's judgment.

The classifier writes its suggestion, plus a one-line reason, into the dispatcher's existing queue view. She accepts, overrides, or escalates. Overrides feed back into a weekly review prompt so we can tune the classifier on the calls it got wrong. The field service software is unchanged; the techs see the same job tickets they always have.

The whole layer is one TypeScript service plus a webhook that plugs into Zapier. No new app for the dispatcher to learn. No new screen for the techs.

We measured three things over the first 30 days: same day closure rate (closed jobs divided by the requests eligible for same day service), median triage time per call, and how often a job that the AI flagged 'routine' turned out to be an emergency once a tech was on site. The first two improved as expected. The third is the one we watched closely; misclassification of an emergency as routine was the failure mode that would have killed the project. In 612 calls, the AI underclassified severity twice. Both were caught at dispatcher review before a truck was assigned.

The dispatcher's own override rate stabilized at about 9 percent by week three, which the company treated as a healthy floor: too low and she's rubber stamping; too high and the AI isn't earning its place.

We considered automating the dispatch decision itself and having the AI assign trucks based on location, skill mix, and load. We did not. Truck assignment depends on which technician is best at which equipment brand, which customers a tech has worked with before, and a dozen unwritten preferences the dispatcher carries. None of that lives in a system. Forcing it into a model would have produced confidently wrong assignments and slowly eroded customer relationships. The dispatcher kept the assignment decision; the AI kept the triage decision.

We also passed on a chatbot for customers. Homeowners with a broken furnace want a human voice on the phone, not a chat widget. The intake form is for the web channel only.