# AI Review Intelligence (`enezli/ai-review-intelligence`) Actor

Customer review analysis that tells you exactly what to fix. Turns reviews into a manager-ready action plan: sentiment, recurring themes, top complaints and praises, competitor gaps and prioritized fixes. Stop reading reviews one by one.

- **URL**: https://apify.com/enezli/ai-review-intelligence.md
- **Developed by:** [Turgay NANTA](https://apify.com/enezli) (community)
- **Categories:** AI, Marketing
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $400.00 / 1,000 per 100 reviews

This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events.

Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event

## What's an Apify Actor?

Actors are web data automations that power AI and operations. They run on the Apify platform to scrape websites, process data, connect APIs, and automate workflows.
In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours,
and optionally produces a well-defined JSON output, datasets with results, or files in key-value store.
In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server.
Actors are written with capital "A".

## How to integrate an Actor?

If asked about integration, you help developers integrate Actors into their projects.
You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready.
The best way to integrate Actors is as follows.

- **AI agents and MCP clients** — the [Apify MCP server](https://docs.apify.com/integrations/mcp.md) at `https://mcp.apify.com` (remote, streamable HTTP, OAuth on first use).
- **Agentic workflows and local Actor development** — [Agent Skills](https://apify.com/.well-known/agent-skills/index.json) with the [Apify CLI](https://docs.apify.com/cli/docs.md): `npm install -g apify-cli`, then `apify login`.
- **JavaScript/TypeScript projects** — the official [JS/TS client](https://docs.apify.com/api/client/js/docs.md): `npm install apify-client`.
- **Python projects** — the official [Python client](https://docs.apify.com/api/client/python/docs.md): `pip install apify-client`.
- **Any other language** — the [REST API](https://docs.apify.com/api/v2.md).

For usage examples, see the [API](#api) section below.

For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt).

# README

## AI Review Intelligence — From Reviews to an Action Plan

Turns hundreds of customer reviews into a **manager-ready action plan** in seconds. It doesn't just tell you *what customers think* — it tells you **what to do about it**: prioritized steps, quick wins, and the single critical move to make this week.

### What it does

Connect the output of a review-scraper (Google Maps, Trustpilot, Amazon, App Store) or your own review list, and the actor produces:

- **Overall sentiment** + star-rating distribution
- **Recurring themes** (frequency + positive/negative)
- **Top complaints and praises**
- **🎯 Action plan** — prioritized, concrete steps (each with benefit/cost + expected gain)
- **⚡ Quick wins** — low-effort, high-impact moves
- **The single critical step for this week**
- **Competitor opportunities** + a short executive summary

### Why it's different

Most review tools just hand you a raw sentiment score. This actor **behaves like a consultant who makes decisions**: it links every finding to an actionable step. Managers don't read the report and ask "so what do I do?" — the plan is already in their hands.

### Input

| Field | Description |
|---|---|
| `reviews` | The review list: plain strings OR `{text, rating}` objects. A review-scraper dataset can be connected directly. |
| `businessName` | Business name (used as report context) |
| `language` | Output language of the analysis content: English / Türkçe / Deutsch / Español / Français |
| `model` | (Advanced) LLM model — the default is fast and economical |

### Output

Delivered in two layers:

- **Dataset (table):** Prioritized **action plan** rows — columns: `Priority` · `Issue / Area` · `Risk of Inaction` · `Risk Level` · `Benefit` · `Cost` · `Benefit/Cost` · `Score` · `Recommended Action`. Scan, sort, and export directly.
- **Report (key-value store → `MANAGER_REPORT`):** Full narrative + machine-readable data with English keys: `overall_sentiment` · `themes` · `top_complaints` · `top_praises` · `competitor_opportunities` · `critical_step` · `quick_wins` · `executive_summary` · `rating_distribution` · `review_count` · `all_actions` (including more than what is shown in the main table).

### How to use

1. Collect your reviews with a review-scraper (or paste in your own list).
2. Connect the output to this actor as `reviews`.
3. Set the business name and language → **Start**.
4. Grab your action plan from the Dataset tab.

### Pricing (Pay-Per-Event)

You only pay for what you use: per run + per 100 reviews processed + per generated report. No monthly subscription.

# Actor input Schema

## `reviews` (type: `array`):

The reviews to analyze. Provide an array of plain strings OR {text, rating} objects. You can connect a review-scraper's dataset output directly.

## `businessName` (type: `string`):

Used in the report title and as context for the analysis.

## `language` (type: `string`):

Language of the analysis content (sentiment and action text).

## `model` (type: `string`):

LLM model. The default is cheap and fast; pick a stronger model for deeper analysis.

## `maxAksiyon` (type: `integer`):

How many prioritized actions to show in the main table (for focus). All of them are always kept in the report (MANAGER\_REPORT). Range 3-10.

## Actor input object example

```json
{
  "reviews": [
    {
      "text": "Hizmet hızlıydı ama fiyat yüksek.",
      "rating": 3
    },
    {
      "text": "Personel ilgisizdi.",
      "rating": 2
    },
    {
      "text": "Harika lezzet, tekrar geleceğim!",
      "rating": 5
    }
  ],
  "businessName": "Business",
  "language": "English",
  "model": "claude-haiku-4-5-20251001",
  "maxAksiyon": 5
}
```

# API

You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup.

## JavaScript example

```javascript
import { ApifyClient } from 'apify-client';

// Initialize the ApifyClient with your Apify API token
// Replace the '<YOUR_API_TOKEN>' with your token
const client = new ApifyClient({
    token: '<YOUR_API_TOKEN>',
});

// Prepare Actor input
const input = {
    "reviews": [
        {
            "text": "Hizmet hızlıydı ama fiyat yüksek.",
            "rating": 3
        },
        {
            "text": "Personel ilgisizdi.",
            "rating": 2
        },
        {
            "text": "Harika lezzet, tekrar geleceğim!",
            "rating": 5
        }
    ]
};

// Run the Actor and wait for it to finish
const run = await client.actor("enezli/ai-review-intelligence").call(input);

// Fetch and print Actor results from the run's dataset (if any)
console.log('Results from dataset');
console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`);
const { items } = await client.dataset(run.defaultDatasetId).listItems();
items.forEach((item) => {
    console.dir(item);
});

// 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs

```

## Python example

```python
from apify_client import ApifyClient

# Initialize the ApifyClient with your Apify API token
# Replace '<YOUR_API_TOKEN>' with your token.
client = ApifyClient("<YOUR_API_TOKEN>")

# Prepare the Actor input
run_input = { "reviews": [
        {
            "text": "Hizmet hızlıydı ama fiyat yüksek.",
            "rating": 3,
        },
        {
            "text": "Personel ilgisizdi.",
            "rating": 2,
        },
        {
            "text": "Harika lezzet, tekrar geleceğim!",
            "rating": 5,
        },
    ] }

# Run the Actor and wait for it to finish
run = client.actor("enezli/ai-review-intelligence").call(run_input=run_input)

# Fetch and print Actor results from the run's dataset (if there are any)
print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"])
for item in client.dataset(run["defaultDatasetId"]).iterate_items():
    print(item)

# 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start

```

## CLI example

```bash
echo '{
  "reviews": [
    {
      "text": "Hizmet hızlıydı ama fiyat yüksek.",
      "rating": 3
    },
    {
      "text": "Personel ilgisizdi.",
      "rating": 2
    },
    {
      "text": "Harika lezzet, tekrar geleceğim!",
      "rating": 5
    }
  ]
}' |
apify call enezli/ai-review-intelligence --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "command": "npx",
            "args": [
                "mcp-remote",
                "https://mcp.apify.com/?tools=enezli/ai-review-intelligence",
                "--header",
                "Authorization: Bearer <YOUR_API_TOKEN>"
            ]
        }
    }
}

```

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/actors/Vbe3eZ3mO31eK4O5H/builds/YBYUK17B6ySvHSxWA/openapi.json