Restaurant · n8n Workflow Automation
Restaurant — Reservation Feedback and Review Recovery
Portfolio DemonstrationOnly confirmed reservations enter the feedback cycle; simulated feedback is classified into Positive/Neutral/Negative, where positive guests get a public review request and loyalty invite, neutral guests get a private manager follow-up, and negative guests always open a private recovery case that escalates to the General Manager only when truly urgent language is detected.

Problem
Negative post-visit feedback often goes unaddressed, while happy guests are rarely prompted to leave a public review.
System Design
Guest-experience tree: an early confirmation gate, then a 3-way sentiment Switch where the negative branch is visibly the longest and deepest of the three.
Workflow Stages
- 01Reservation & Visit Gate
- 02Sentiment Classification
- 03Positive & Neutral Paths
- 04Negative - Private Recovery (Longest Path)
Tools Used
- n8n
- Code (JavaScript)
- If
- Manual Trigger
- Set
- Switch
Safety & Human Review
- Workflow name begins with DEMO | and is inactive.
- No credentials are attached to any node.
- No enabled external send node exists anywhere in this workflow.
- All data is fictional (example.com emails, invented names/addresses).
- Uses only built-in n8n node types.
Implementation Evidence
Real screenshots of the working system — collapsed by default so this page stays readable.
What's Working
7 structural checks passed, including: Workflow JSON parses (local + server-returned copy); active is false; Name begins with DEMO |; No credentials attached to any node. Structural and logic validation completed. Live n8n execution pending manual safe test.
Limitations & Confidentiality
Classification and scoring logic is deterministic/rule-based rather than a live AI model, by design (provider-independent, fully inspectable, no credential required). No real system of record, messaging channel, or calendar is connected - every downstream action is simulated or drafted, never sent. Live n8n execution has not been triggered as part of this build.
Portfolio demonstration built for a fictional business with fictional data. It is not connected to any real client, system, or customer.
What I'd Improve Next
Replace deterministic classification with a real AI model behind the same confidence/human-review pattern where one exists. Connect the mock/simulated steps to real systems of record once reviewed and approved. Wire a real (approved) communication channel behind the draft-message steps.
Have a similar workflow to automate?
I'll tell you honestly whether automation is the right fix.