Guests keep asking the same questions, and you cannot read and answer every message yourself. Guesty offers AI replies, so the practical question is which messages you let go out without a person reading them, and what the reply knows when it goes out.
This guide sorts guest questions into what can run on autopilot, what should be a draft, and what always needs a person, then lists the facts a reply must be grounded in. (Scheduled messages such as check-in instructions are a separate topic, covered in Guesty automated messages.)
What Guesty says it offers
Guesty's unified inbox page states that "Guesty ReplyAI and AI Autopilot draft or auto-send on-brand replies" using sentiment analysis and auto-translation. Its AI page describes Autopilot as reading guest messages and responding in brand voice "based on specified rules." The same inbox page says AI Task Creation turns a guest message into an assignable task.
Two things those pages do not say: which booking details a reply draws on, and what review step, if any, sits before an Autopilot message goes out. Ask Guesty both before you turn it on. Confirm which reply modes your plan includes and how each is enabled.
Drafts or autopilot: decide by question, not by account
A draft costs you reading time on every message. Autopilot sends without individual review, so decide which questions it can handle and review the replies regularly. Sort the questions you receive into three groups.
Autopilot candidates. The answer is a fact that does not change by guest: Wi-Fi name, parking rule, where the trash goes, whether pets are allowed under your listing rules. Keep requests for exceptions or permission changes under human review.
Draft-first. The answer depends on the booking: check-in time on a specific day, a date change, a guest count over the listing limit. These need the live reservation, so a person should see the draft until you have watched it get these right for a few weeks.
Always a person. Refunds, complaints, damage, safety, anything with a payment or a platform-policy consequence. No rule should send these on its own.
Start with the first group on autopilot and the second on drafts. Move a question type up only after you have read the replies it actually produced.
What a reply has to be grounded in
An AI reply is only as good as the facts it sees at the moment of sending. Three layers matter.
- The live booking. Guest name, dates, unit, number of guests, booking status and channel. A reply that says "your check-in is Friday" must read Friday from the reservation, not from a template.
- Your rules for that unit. Check-in window, early and late policy, quiet hours, parking, access method. Written per unit, not once for the portfolio.
- What is happening in the operation right now. Is the home occupied tonight? Is the turnover finished? A guest asking for early check-in at 11 a.m. needs an answer that depends on the cleaning, and the cleaning lives in your team's routine, not in the message thread.
Check whether your reply setup can access this third layer. A reply can be perfectly worded and still wrong because it did not know the cleaner had not finished.
How to review what autopilot sent
Turning autopilot on is the start of the work, not the end of it. Once a week, open a sample of the replies that went out without a person and read each against the booking it answered. Look for three failures: a fact that was true for another unit or another date, a promise your team then had to walk back, and a tone that a guest answered with a complaint. Each failure points to a missing rule or a missing fact, and fixing that input is what lets you widen the autopilot group safely. Keep a short log of what you changed and why, so a new team member inherits the reasoning and not just the settings.
An illustrative example
Illustrative scenario, not a customer. A guest writes: "We land at 2:30. Is early check-in possible?" The home was occupied last night and checkout was at 11.
- A rule-only reply says check-in opens at 4 p.m. That is true and unhelpful.
- A reply grounded in the booking and the turnover state can say: the unit is being turned over, your team will confirm after the clean, and it hands the question to a person with the arrival time attached.
The second reply never promises something the operation cannot deliver, and your team receives the question with its context instead of a thread to reconstruct.
Limits worth stating
No reply setup removes the need for a person on complaints, refunds and anything with a safety or payment consequence. Grounding reduces wrong answers; it does not make every answer right, and a reply that is confident but stale is worse than one that says a person will confirm. Platform rules about messaging also apply on the booking channels you use, so check them before you automate anything beyond routine answers.
A short checklist before you switch anything to autopilot
- List your twenty most frequent guest questions and tag each as autopilot, draft or person.
- Write the house rules per unit, including the exceptions you currently handle from memory.
- Confirm with your PMS vendor which booking data the replies read.
- Decide who sees an escalated question and how fast.
- Review a week of sent replies before widening the autopilot group.
Where InnSync fits
InnSync runs guest replies from the booking, the unit and your house rules, and the cleaning, check-in and team routine that follows each booking. Carmen, InnSync's AI assistant, answers from that context and passes unusual situations to your team. You choose, per channel, whether she replies on her own or drafts for review.
If you use Guesty
InnSync connects to Guesty; reservations, units and guests arrive from it.
If you run Guesty and want replies that know the booking and the turnover state, see InnSync for Guesty.