Review Change Monitor — Voice of Customer Alerts (Vigia)

Turn any review dataset into actionable alerts: only newly observed reviews, prioritized by urgency — 1-star spikes, refund threats, standout praise. Deduplicated and charged only on real changes. Ideal for reputation-management and customer-support agents.

Free tier — 10 results free on every run

The first 10 results of every run are free, on every run you ever make — not a one-off trial. Run it on your own subjects and see the real output before you are billed for a single result.

One guard: the free allowance never covers more than half of a run, so a run returning 6 results gets 3 free. That keeps the offer sustainable.

What it does

Normalize any review dataset into a clean new-reviews delta. You get only what changed since your last check — never a full re-dump to diff yourself.

Why Vigia — 5 advantages a raw scraper can't match

1. You pay the alert price only for real change. New-review alerts are billed only for genuinely new reviews since your last check. With monitoring on, a quiet run bills only a small check fee — never the result price on unchanged data; one-off runs pay only per result. Raw scrapers bill every row of a full re-scan every run.
2. Never billed on a failed source. If the source is down or an input fails, that item is free.
3. Append-only delta + spike signal. The first monitored run stores a free baseline; later runs deliver only what's new, plus a velocity-spike alert when volume suddenly surges (trend / review-bomb / hiring-surge).
4. Hard cost cap you control. Set maxTotalChargeUsd and the run can never bill above it — predictable spend, no surprise invoice.
5. Agent-ready output. Clean, normalized rows and a RUN_SUMMARY.json with ready-to-use signals — chainable from any MCP / A2A agent in one step.

Example input

{
  "monitorEnabled": true,
  "maxTotalChargeUsd": 1.0
}

Set monitorEnabled: true so the first run stores a free baseline and later runs return only new results. Pin a monitorId to keep separate targets from sharing state.

Output

Each row is a normalized change record: record_id, review_id, rating, severity, topic, actionable, signals, text. A RUN_SUMMARY.json accompanies every run with the aggregated signal (counts, velocity spikes, trend).

How you're charged

• Review alert — billed once per new review (the only charge).
• Bring-your-own dataset — you pass the records in, so there is no upstream fetch and no source-check fee.
• A run with no new reviews costs $0.

Related tools

• Need the reviews collected first, not just cleaned up? → Google Maps Reviews Scraper - Rating, Text, Reviewer

How to use it in 3 steps

1. Click Try for free and fill in Current reviews (currentReviews).
2. Optional: adjust the other fields; every one has a description and a sensible default.
3. Click Start and download the results as JSON, CSV or Excel, or read them from the API.

Use cases

• Analysts
• Reputation teams

Use it from code (API)

Every Apify Actor is an API. Start a run and read the results with one call.

Python

from apify_client import ApifyClient

client = ApifyClient("<YOUR_APIFY_TOKEN>")
run = client.actor("om_kh/reviews-cleaner-deduplicator").call(run_input={"currentReviews": [{"reviewId": "r1", "rating": 2, "text": "Package arrived late and damaged.", "author": "Sam"}]})
for item in client.dataset(run["defaultDatasetId"]).iterate_items():
    print(item)

JavaScript

import { ApifyClient } from 'apify-client';

const client = new ApifyClient({ token: '<YOUR_APIFY_TOKEN>' });
const run = await client.actor('om_kh/reviews-cleaner-deduplicator').call({
    "currentReviews": [
        {
            "reviewId": "r1",
            "rating": 2,
            "text": "Package arrived late and damaged.",
            "author": "Sam"
        }
    ]
});
const { items } = await client.dataset(run.defaultDatasetId).listItems();
console.log(items);

One HTTP call (cURL) — runs the Actor and returns the dataset in the same response:

curl -X POST "https://api.apify.com/v2/acts/om_kh~reviews-cleaner-deduplicator/run-sync-get-dataset-items?token=<YOUR_APIFY_TOKEN>" \
  -H "Content-Type: application/json" \
  -d '{"currentReviews": [{"reviewId": "r1", "rating": 2, "text": "Package arrived late and damaged.", "author": "Sam"}]}'

Integrations: n8n, Make, Zapier, Google Sheets

• n8n — the official Apify node, operation Run Actor and get dataset, Actor om_kh/reviews-cleaner-deduplicator.
• Make — Apify › Run an Actor, then Apify › Get Dataset Items.
• Zapier — Apify › Run Actor; map the dataset items into your next step.
• Google Sheets, webhooks, Slack — add an integration from the Integrations tab of this Actor.
• Schedules — run it every hour or day from Schedules in Apify Console.

Use it from an AI agent (MCP and x402)

Connect Claude Desktop, Cursor, VS Code or any MCP client to Apify's MCP server at https://mcp.apify.com and add this Actor, om_kh/reviews-cleaner-deduplicator, as a tool — the agent passes the same JSON as the run input and gets the dataset back.

AI agents that pay with x402 can run it too, without an Apify account: the Actor charges only per result delivered, so an agent pays exactly for what it gets.

FAQ

Is it legal to scrape Review Cleaner & Deduplicator? This Actor collects only publicly available Review Cleaner & Deduplicator data and never logs in. You are responsible for how you use it; check Review Cleaner & Deduplicator's terms and your local law (GDPR, CCPA) if you store personal data, and ask a lawyer if unsure.

Do I need an account, cookies or an API key? No. You only need an Apify account to run it.

What data do I get? Structured records. Download it as JSON, CSV, Excel, HTML or XML, or read it from the API.

Am I charged when something fails? No. An input that fails is listed with its reason in the run summary and is never charged.

Can I run it on a schedule? Yes. Create a schedule in Apify Console (hourly, daily, weekly) and connect webhooks, Google Sheets, Slack, n8n, Make or Zapier.

Can I use it from Python, JavaScript or an AI agent? Yes: see the code samples above, or connect AI agents through MCP with the reviews_delta tool.

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