Hospitality Review Response Automation
Independent hospitality automation prototype
- My role
- AI Automation Developer
- Team
- Solo project
- Tools
- Airtable
- Airtable AI
- Workflow automation
- Timeline
August 2026 — Prototype
- Description
An AI-assisted workflow for preparing responses to hospitality reviews. Reviews are stored in Airtable, compared against editable brand-voice guidance, and used to generate response drafts that the business owner can edit, approve, or reject before manually publishing them.
- Context
Responding consistently to customer reviews can become repetitive for hospitality businesses, particularly when the same owner or manager is responsible for both operations and customer communication. The goal was not to automate public replies without oversight. Instead, I designed a workflow that reduced the time required to produce a good first draft while preserving the owner's control over what ultimately represents the business publicly.
Click around...
Challenge
A generic AI-generated review response is easy to produce but often sounds generic.
A useful system needs enough brand context to maintain a recognisable tone, while also responding appropriately to the specific content and sentiment of each review.
There is also reputational risk in automatically posting generated text without human review.
Constraints
The workflow deliberately does not automatically publish responses.
Review platforms, business policies, and edge cases can all make fully autonomous publication undesirable.
Brand guidance also needed to remain editable by a non-technical user rather than being permanently embedded inside an automation prompt.
Research
I focused on turning brand voice into editable operational data rather than fixed prompt text.
This meant the owner could change response guidelines without rebuilding the workflow itself.
I also structured the process around a review queue so generated responses could be treated as drafts rather than finished outputs.
Iterations
Reviews were stored in Airtable alongside contextual information.
Editable brand-voice guidance was incorporated into the AI drafting process.
Generated responses were then surfaced back inside Airtable with workflow states allowing the owner to edit, approve, or reject them.
Approved text could be copied to the relevant review platform manually.
This kept the final publication decision outside the AI system.
Key Features
- Review ingestion
- Airtable review database
- Editable brand-voice guidelines
- AI-generated response drafts
- Context-aware responses
- Owner editing
- Approve/reject workflow
- Human-controlled publishing
- Reusable architecture for other customer-review industries
Final Deliverable
The final prototype provided a simple review-response queue in which AI handled the repetitive first-draft work while a human retained full editorial and publishing control.
Takeaway
This project reinforced that human-in-the-loop design is not merely a fallback for weak AI.
For customer-facing communication, review and approval can be a deliberate product feature. It gives businesses speed without requiring them to delegate their public voice entirely to a model.