Accelerator 14 · Hospitality & travel
Guest messaging and review response agent
Answers guest messages from the hotel's own facts, routes requests to the right department, and drafts review replies that fix the underlying issue, not just the wording.
The problem
- Front desks answer the same pre-arrival and in-stay questions on email, chat and messaging apps all day, often in several languages.
- Guest requests get lost between desk, housekeeping and maintenance, and the guest finds out at check-out.
- Online reviews drive bookings, but replies are slow, generic, and nobody tracks the recurring issues behind them.
What it does
- Answers from the property's own fact sheet and policies, in the guest's language, and says when it does not know.
- Turns requests into tasks for the right department with a priority.
- Drafts personal review replies and logs each complaint category so managers see patterns.
- Never promises refunds or upgrades; those go to a person.
Paste a hotel fact sheet and a guest message or review and get a reply, the routed tasks and an issue log.
Live demo: Guest messaging and review response agent
Synthetic data only · nothing you type is stored
Output
Reply to the guest, tasks routed to departments, and an issue log entry.
The demo first calls the Surge in-house AI server and falls back to a hosted open-source model if it is busy. The badge on the output shows which one answered. Limited to a few runs per visitor per day.
How it integrates
In your deployment the accelerator runs inside your environment, behind your single sign-on, and is connected to the systems below. You choose the hosting and the model.
Systems and APIs
- PMS: Oracle OPERA Cloud, Mews, Cloudbeds (reservations and guest profile)
- Messaging: WhatsApp Business, SMS, email, website chat
- Review sites via the hotel's connected accounts
- Housekeeping and maintenance task systems
Data we need for a pilot
- Property fact sheets and policies
- A sample of past guest conversations and review replies
Where it runs
- Private cloud per hotel group; guest data stays in the group's tenancy
Open-source models we start with
- Qwen3-class 14B multilingual instruct model
- Embeddings for the property knowledge base
Walkthrough
A short recorded walkthrough of the guest messaging and review response agent: the input, the output and how a reviewer uses it.
Next accelerator: NDA Intelligence: clause risk, compare and redline →Walkthrough video coming soon
We are recording a short walkthrough of this accelerator. Meanwhile, run the live demo above or book a working session.
Run the hospitality & travel accelerator on your data
In a working session we map it to your process, systems and rules, and agree what a pilot must prove before it goes live.