Close the loop on every completed visit: confirm the repair held, surface the work that was quoted on site, and catch dissatisfaction early.
An AI agent for post-visit follow-up is a 24/7 digital assistant that messages the customer shortly after a job is marked complete, checks the repair is still holding, brings up any additional work the technician recommended on site, and either books the next step or routes an unhappy customer to a human before they write anything publicly. It stops completed jobs ending in silence when the technician is already three streets away and nobody at the office knows whether the fix actually worked. This is the most reliably ignored moment in home services. The technician has done the work, the invoice is settled, and the file closes. Meanwhile the customer is forming an opinion, deciding whether to act on the second repair that was mentioned at the door, and choosing whether to leave a review. A short, well-timed message turns that hour into either a booked follow-on job or a problem you get to fix privately.
One message at the right moment, then the right branch.
Triggers a message a set time after the technician marks the job complete
Names the technician and the work done, so the message reads as personal
Asks whether the repair is holding and whether anything was left unclear
Raises any additional work quoted on site and offers to book it
Routes an unhappy answer straight to a manager instead of asking for a review
Writes the response and the next action back onto the job record
Two things are decided in the hours after a visit and neither is usually managed. The first is whether the customer acts on what the technician recommended, which is often worth more than the visit itself. The second is whether a small irritation becomes a public review. A short automated check-in influences both, and it does it at a moment when the technician is already on the next job and the office has moved on.
A simple, three-step flow.
When the technician closes the job in your field-service software, the agent picks up the customer, the work performed and any recommendations recorded on the visit.
After the delay you set, usually two to twenty-four hours, a short text goes out naming the technician and the job and asking whether everything is working as expected.
A positive reply leads to the quoted extra work and, if you want, a review request. A negative reply is escalated to a manager with the job history rather than answered by the agent.
A realistic use case with concrete timing and output.
Scenario: A company running 6 technicians completes around 130 jobs a week. On Tuesday at 2:40pm a technician finishes a capacitor replacement and notes on the job that the outdoor coil needs a clean, quoted at 240 dollars, which the customer said she would think about. At 5:30pm she receives a text naming the technician, confirming the repair, and asking whether the system has been running steadily since. She replies that it has, and that she would like the coil clean after all. The agent books it for the following Monday and writes it to the job record. That same evening another customer replies that the noise has come back; instead of a review request, a manager gets an alert with the job history and calls before the end of the day.
Roles that gain practical value from this AI agent.
Hear about a failed repair from the customer the same day rather than from a review site.
Recommendations made at the door get followed up without them having to chase anyone.
Turns completed jobs into additional work and into reviews from the right customers.
Complaints arrive as a quiet text they can resolve, not as a public post at the weekend.
A check-in question exposes repairs that did not hold while the fix is still under warranty.
Asking only satisfied customers raises the average rating rather than gambling on it.
Key tools and what the AI agent does inside each.
Signals job completion and supplies the technician name, work performed and on-site recommendations.
Sends the check-in text and carries the customer's reply back into the branch logic.
Records the customer's response on the job and creates the follow-on job when extra work is accepted.
Alerts the service manager immediately when a customer reports the repair has not held.
Books the accepted additional work into a real slot during the same conversation.
Six practical scenarios that this AI agent excels in.
Common questions about using the AI agent in workflows.
An AI agent for post-visit follow-up is a 24/7 digital assistant that messages the customer shortly after a job is marked complete, checks the repair is still holding, brings up any additional work the technician recommended on site, and either books the next step or routes an unhappy customer to a human. It stops completed jobs ending in silence when the technician is already three streets away. Unlike an automated review request, it asks a real question first and only invites a review from customers who said the job went well.
For a repair, two to four hours is usually right, since the customer has had time to see whether it held but the visit is still fresh. For an installation, the next morning works better. You set the delay per job type, and the agent uses the completion timestamp from your field-service software rather than a fixed daily batch.
It can raise it and book it, using the wording and the price the technician recorded on the job. It does not invent recommendations, change the quoted figure, or diagnose anything itself. If the customer asks a technical question, the thread goes to a person.
The agent stops the sequence, does not ask for a review, and alerts a service manager with the job number, the technician and the customer's exact words. Nothing is promised on the company's behalf beyond the acknowledgement wording you approve, because a warranty decision belongs with a person.
Only when the customer has confirmed things are working, and only if you switch that step on. Sending review requests indiscriminately after every job is how companies collect their worst ratings. The check-in question acts as the filter.
Yes, with quiet hours respected. A job closed at 11pm produces a message the following morning rather than at midnight. The timer is based on completion but constrained by the sending window you define.
Only what they already do: close the job and record any recommendation. Everything after that is automatic, which is the point, because asking field crews to follow up personally is the version of this that never survives a busy week.
Close the loop on every completed visit: confirm the repair held, surface the work that was quoted on site, and catch dissatisfaction early.