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Auto Repair Shop · n8n Workflow Automation

Auto Repair — Estimate Approval and Service Reminder

Portfolio Demonstration

Vehicle and service-history data are captured in parallel and merged before a keyword-based complaint classifier and safety check determine whether the item gets a priority inspection hold; a mock cost-table estimate then drives a 3-way approval split, with every outcome producing a mileage-based maintenance reminder.

n8n canvas: Auto Repair — Estimate Approval and Service Reminder — full workflow canvas in n8n

Problem

Shops lose time chasing customers for estimate approvals and manually tracking service reminders.

System Design

Service pipeline with approval split: parallel intake merges into a linear diagnostic/estimate pipeline, ending in a 3-way approval switch (approved/declined/pending).

Workflow Stages

  1. 01Parallel Vehicle + History Intake
  2. 02Safety Triage
  3. 03Estimate & 3-Way Approval Split
  4. 04Reminder & Service Record

Tools Used

  • n8n
  • Code (JavaScript)
  • If
  • Manual Trigger
  • Merge
  • 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?

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