An automated Google Maps review management system built with n8n, Apify, OpenRouter, Slack, and Google Sheets.

The workflow monitors new customer reviews, prevents duplicates, uses AI to summarize feedback and draft personalized responses, then routes positive and negative reviews to the appropriate internal Slack channels.

Monitoring Google Maps reviews manually created several operational problems:

New reviews had to be checked manually every day.

Negative feedback could be missed or addressed too slowly.

Writing professional responses for every review was time-consuming.

Review information was not stored in a centralized system.

Teams lacked an automated process for prioritizing customer feedback.

I built an end-to-end AI review management workflow that automatically collects, analyzes, stores, and routes new Google Maps reviews.

Apify retrieves Google Maps review data including reviewer, rating, review text, URL, publication date, and reviewer ID.

Google Sheets stores previously processed review IDs.

n8n compares incoming reviews against stored IDs to identify genuinely new feedback.

New reviews are sent to an LLM through OpenRouter.

AI generates a management-friendly summary and personalized response draft.

JavaScript combines the original review data with the AI-generated content.

The complete review record is stored in Google Sheets.

Slack routing separates positive reviews from reviews requiring customer-support attention.

The workflow uses rating-based business rules to determine how reviews should be handled.

Reviews with 4+ stars are routed to the positive/wins channel.

The AI generates a thankful response designed to acknowledge the customer's experience and encourage future visits.

Reviews below 4 stars are flagged for customer-support attention.

The AI generates a professional response that acknowledges the customer's concerns, apologizes where appropriate, and communicates willingness to improve.

Every review is assigned a unique identifier.

Before processing, n8n checks the stored Google Sheets records to determine whether that review has already been handled.

This prevents duplicate AI processing, repeated Slack notifications, and duplicate response drafts.

The workflow includes fallback logic for AI failures.

If the AI service does not return a valid response, the workflow generates a default response draft and records the error for later review.

This allows the monitoring pipeline to continue operating instead of failing completely because of a single AI request.

Each processed review can generate an internal notification containing:

Reviewer

Star rating

Review text

AI summary

AI response draft

Google Maps review link

A centralized review database stores:

Review ID

Reviewer

Rating

Review text

AI summary

AI response draft

Review URL

Timestamp

Processing status

Approximately 90% reduction in response preparation time

Automated identification of negative feedback

Duplicate reviews prevented from being processed repeatedly

Centralized customer-feedback database

Faster internal response to reputation issues

Designed to process approximately 10–100 reviews per run

n8n — workflow orchestration

Apify — Google Maps review scraping

OpenRouter — AI review analysis and response generation

Slack API — internal review notifications

Google Sheets — review database and deduplication

JavaScript — data transformation and workflow logic

Build Type: Self-initiated demonstration build Role: Automation Engineer & AI Workflow Architect Platform: n8n Industry: Reputation Management / Customer Feedback