A review-response agent that gave a GM back the floor
An AI agent that drafts on-brand responses to every guest review, routed for one-click approval — turning a four-hour weekly chore into twenty minutes.
The problem
GMs answer only a fraction of their reviews and lose hours of floor time doing it. Unanswered reviews hurt ranking and signal neglect; answered ones eat the manager's most valuable hours.
What we built
An agent that ingests reviews across platforms, drafts responses in each property's voice and brand standards, flags anything sensitive for human attention, and routes drafts to the GM for one-click approval — inside the tools they already use.
What it moved
Review-response rate up and consistent across properties; the GM's weekly time on responses dropped from roughly four hours to under twenty minutes — time that went back to the floor and the guests.
The stack
- LLM agent
- Review platform APIs
- Brand-voice guardrails
- Approval workflow
- PMS context
This is the build that best captures the GuestEx thesis: the win wasn’t a smarter model, it was putting a capable system exactly where the work already happens and keeping a human in the loop where it matters.
How it works
A guest leaves a review. The agent reads it, understands the context — property, sentiment, any specifics worth acknowledging — and drafts a response that sounds like that property, not a generic template. Anything delicate (a service failure, a refund request, anything legal-adjacent) gets flagged for the manager rather than auto-sent. Everything else lands in an approval queue the GM clears in a couple of minutes.
Why it stuck
Most review tools fail because they live outside the manager’s day. This one routed approvals into the workflow the GM already checked, so adoption didn’t depend on anyone changing their habits. The system earned its place by giving back time the same week it went live.
The stack
- LLM agent
- Review platform APIs
- Brand-voice guardrails
- Approval workflow
- PMS context
This is a reference build illustrating a pattern we deploy for operators. The specifics are representative; the approach is real.