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Brisbane, Australia
February 2026
8 min readWorkflow Demo

GuestStay: Front Office Inbox, Guest Recognition & Loyalty

How GetPost Labs would build the front office workflow: booking and enquiry emails read and replies drafted by AI agents, rates and terms held by rules, and every reply, check-in and courtesy left with your front office team. Returning guests and their preferences are recognised on the same record.

Conceptual Prototype — Illustrating our approach

Executive Summary

The Problem: The front office answers reservation and enquiry emails, keys bookings into the property management system, checks guests in and out, and balances the night audit. A guest's preferences and past stays sit in profile notes, old emails and people's memories, so a returning guest is often treated as new.

The Solution: GuestStay gives the reading and drafting to AI agents — the reply, the reservation details, the returning-guest summary and the night audit pack. Rates, deposits and cancellation terms are fixed rules in software. Every reply sent, every courtesy and every check-in stays with your front office team.

The Outcome: The front office reviews drafts instead of re-typing emails, returning guests are recognised before they reach the desk, and each stay adds to one guest record.

1

The Challenge

Understanding the problem space

"Build strong guest relationships by recognising returning guests and creating personalised and memorable experiences."

— A duty listed in a current Australian front office manager job ad (SEEK, September 2026)

Across 15 current front office and guest relations ads, the same duties come up: take reservations, answer enquiries in person, by phone and by email, check guests in and out, handle feedback, and close the day with the night audit. The same ads ask the team to recognise returning guests and make each stay feel personal.

Both jobs depend on reading. The booking detail is in an email, the preference is in a note on last year's profile, the complaint is in a review. When the property management system, the inbox and the team's memory don't join up, replies are typed from scratch and a regular guest arrives as a stranger.

6 of 15
ads mention reservations
7 of 15
ads ask for personalised guest service
7 of 15
ads name Opera or a hotel PMS

Source: 15 front office and guest relations job ads in Australia on SEEK, September 2026. Employers not named.

2

The Solution

What GetPost Labs would build

Core Capabilities

Inbox Replies (AI agent)
Reads booking and enquiry emails and drafts the reply, with the reservation details filled in
Returning Guests (AI agent)
Matches each booking to past stays and puts preferences, notes and loyalty status in front of the desk
Rates & Terms Rules
Availability, rates, deposits and cancellation terms checked by fixed rules, not AI
The Front Office Decides
Staff send every reply, approve every courtesy and check every guest in; the agent drafts, never commits
Night Audit Pack (AI agent)
Matches the day's charges, payments and room status and flags what doesn't balance for the night auditor
One Guest Record
Emails, stays, preferences and feedback kept together, so the next stay starts where the last one ended
3

Where the Time Comes Back

Reviewing, not re-typing

The front office stops re-typing emails into the PMS and starts checking what the agent has drafted:

Booking Emails

Before

Read the email, check availability, key the booking into the PMS, then type a reply from scratch.

After

The agent has drafted the reply and the reservation. Staff check the details and press send.

Returning Guests

Before

Hope someone remembers the guest, or scroll through old profile notes while they wait at the desk.

After

Past stays and preferences are already on the booking, so the team can act on them before the guest arrives.

Night Audit

Before

Print the reports, tick off charges and payments by hand, and hunt for the one that doesn't balance.

After

The agent has matched the day's charges and flagged the exceptions. The night auditor checks those and closes the day.

4

BPMN Workflow

The business process modelled

Booking Enquiry: Email to Night Audit

GuestAI AgentRules in SoftwareFront OfficeEmail arrivesEmail EnquiryDraft ReplyRate & DepositMatch Past StaysReview & SendCheck Guest InPrep Night AuditDay closed
Guest
AI Agent
Rules in Software
Front Office
AI agent task
Rule in software
Person
5

User Journey

A Booking Enquiry, Email to Night Audit

Scenario: A Thursday afternoon at a 120-room city hotel. A guest emails asking for two nights next month, mentions it will be her third stay, and asks for a quiet room away from the lift.

1
GuestEmail Enquiry

Emails the reservations inbox asking for two nights, a quiet room and a late check-out

2
AI AgentDraft Reply

Reads the email, fills in the reservation details and drafts a reply with the room types available on those dates

3
Rules in SoftwareRate & Deposit

Checks availability, the rate for those dates, the deposit and the cancellation terms. The draft cannot quote a rate the system does not hold

4
AI AgentMatch Past Stays

Finds two earlier stays under the same email address and adds her notes to the booking: quiet room, extra pillows. Flags that late check-out was charged last time

5
Reservations AgentReview & Send

Checks the draft, approves a free late check-out as a returning-guest courtesy, and sends the confirmation

6
Front DeskCheck Guest In

On arrival her preferences are on screen. The front desk allocates a quiet room, welcomes her back and checks her in

7
AI AgentPrepare Night Audit

Matches the day's charges, payments and room status against the PMS reports and flags what doesn't balance for the night auditor to check before closing the day

Outcome: The guest is recognised on her third stay without anyone having to remember her. The agent read and drafted; the front office decided and welcomed her back.

6

Interactive Prototype

Functional dashboard demonstrating the concept

GuestStay
GuestStayCheck-in

Express Check-in

75% time reduction

2 min
Avg Check-in
↓ was 8 min
0
Queue
86%
Pre-registered
94%
Express
Current Guests
Sarah Chen
Room 405 • High floor, quiet
checking-in
James Wong
Room 612 • King bed, city view
in-house
Lisa Brown
Room 301 • Ground floor, accessible
checking-out

© 2026 GETPOST Labs. Full Stack Engineering Solutions.

Functional prototype. Click on cells and entries to see interactions.

7

System Context

Where GuestStay fits in the ecosystem

SYSTEM INTEGRATIONOpera / PMSReservationsGuest profilesFoliosAPIGuestStay• AI inbox drafts• Rates & terms rules• Guest historyAPIGuestsEmailsSpecial requestsFeedbackAPIPayments & POSCard paymentsRestaurant chargesDeposits

Have a Similar Problem?

This is the kind of workflow automation GetPost Labs builds. If your organisation has similar challenges, we'd love to discuss how a custom solution might help.