# Ai Visibility Checker (`muhammad-bilal/ai-visibility-checker`) Actor

Measure how visible a brand is inside AI-generated answers across ChatGPT, Gemini, Claude, and Perplexity.

For every question you provide, this Actor asks each selected AI answer engine, waits for the
response to finish streaming, and extracts a structured record

- **URL**: https://apify.com/muhammad-bilal/ai-visibility-checker.md
- **Developed by:** [Muhammad Bilal](https://apify.com/muhammad-bilal) (community)
- **Categories:** AI, Agents, SEO tools
- **Stats:** 1 total users, 0 monthly users, 0.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $0.10 / actor start

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 Visibility Checker

**Measure how visible a brand is inside AI-generated answers** across ChatGPT, Gemini, Claude, and Perplexity.

[![Apify SDK](https://img.shields.io/badge/Apify-SDK%20v3-green)](https://sdk.apify.com)
[![Playwright](https://img.shields.io/badge/Playwright-v1-blue)](https://playwright.dev)
[![TypeScript](https://img.shields.io/badge/TypeScript-strict-blue)](https://www.typescriptlang.org)

### Overview

For every question you provide, this Actor asks each selected AI answer engine, waits for the
response to finish streaming, and extracts a structured record: whether your **brand is mentioned**,
its **position**, the **sentiment**, the **competitors** named alongside it, and the **citations/URLs**
used. It then computes a **Visibility Score** and a **Recommendation Score** and produces per-answer
dataset rows plus aggregate JSON / Markdown / CSV reports.

#### Key capabilities

- Multi-engine querying via pluggable Playwright **adapters** (ChatGPT, Gemini, Claude, Perplexity)
- Deterministic, fully-tested **parser** (brand mention, position, sentiment, competitors)
- **Citation extraction** with domain normalization and categorization
- **Scores**: composite Visibility Score (0-100) and Recommendation Score (0-100)
- Proxy rotation, stealth, cookies, captcha/login-wall handling, screenshots on failure
- Strict TypeScript, ESLint, Prettier, and a comprehensive Jest test suite

### Architecture

```
src/
├── main.ts                 # Actor.main() entry point (pipeline orchestration)
├── types.ts                # Actor-specific input/record/summary types
├── config/index.ts         # Input validation → strict config
├── analysis/index.ts       # RawAnswer → record, scores, summary
├── report/index.ts         # Markdown report builder
└── shared/                 # Reusable library (identical across all 3 actors)
    ├── types.ts            # Provider-agnostic domain types
    ├── logging/            # Structured logger over Apify log
    ├── retry/              # Exponential backoff with jitter
    ├── validation/         # Input validators
    ├── prompt/             # Prompt builder (context-aware)
    ├── parser/             # Answer parsing (pure, fixture-tested)
    ├── citations/          # URL/domain extraction + categorization
    ├── statistics/         # Small stat helpers
    ├── export/             # Dataset writer, CSV, Markdown, KV reports
    ├── browser/            # Playwright launcher, stealth, screenshots
    ├── adapters/           # AI platform adapters (base + 4 providers + registry)
    └── engine/             # Cross-platform answer collection (DI-friendly)
```

The pipeline: **validate → build tasks → collect answers (Playwright adapters) → parse → analyze → persist**.

### Configuration / Input

| Field | Type | Default | Description |
|-------|------|---------|-------------|
| `brand` | string (required) | — | Brand to measure |
| `questions` | string\[] (required) | — | Questions asked on every platform |
| `industry` | string | — | Category context (e.g. "CRM software") |
| `country` | string | — | Market context (e.g. "United States") |
| `platforms` | string\[] | all four | `ChatGPT`, `Gemini`, `Claude`, `Perplexity` |
| `knownCompetitors` | string\[] | `[]` | Improves position/competitor accuracy |
| `headless` | boolean | `true` | Headless browser |
| `screenshotOnFailure` | boolean | `true` | Save screenshots to KV store on failure |
| `maxRetries` | integer | `2` | Retries per (platform, question) |
| `askTimeoutMs` | integer | `90000` | Max wait per answer |
| `useApifyProxy` | boolean | `true` | Route through Apify Proxy (residential) |
| `cookies` | array | `[]` | Session cookies for authenticated platforms |

#### Input example

```json
{
  "brand": "HubSpot",
  "industry": "CRM software",
  "country": "United States",
  "questions": ["best CRM for startups", "best CRM for small business", "HubSpot alternatives"],
  "platforms": ["ChatGPT", "Gemini", "Claude", "Perplexity"]
}
```

### Output

#### Dataset record (one per question × platform)

```json
{
  "question": "best CRM for startups",
  "platform": "ChatGPT",
  "brand": "HubSpot",
  "brandMentioned": true,
  "position": 1,
  "sentiment": "positive",
  "competitors": ["Salesforce", "Pipedrive", "Zoho CRM"],
  "citations": [{ "url": "https://www.g2.com/products/hubspot/reviews", "domain": "g2.com", "category": "review" }],
  "urls": ["https://www.g2.com/products/hubspot/reviews"],
  "answer": "…",
  "answeredAt": "2026-01-01T00:00:00.000Z",
  "error": null
}
```

#### Key-Value Store artifacts

- `SUMMARY` — aggregate JSON (scores, per-platform stats, top competitors)
- `REPORT` — human-readable Markdown report
- `EXPORT_CSV` — flat CSV export of all records

### Scores

- **Visibility Score** `= 100 × (0.5·mentionRate + 0.3·positionQuality + 0.2·sentiment)`
- **Recommendation Score** = share of answers where the brand is in the **top 3** and **not negative**

### Rate limits & known limitations

- AI answer engines frequently require **authentication** (ChatGPT, Claude, Gemini). Supply session
  `cookies` for reliable results. Perplexity generally works anonymously.
- UI selectors change often; selectors live in `src/shared/adapters/*` and are easily updated.
- Captchas/login walls are detected; the Actor waits, then records a graceful `error` for that answer.
- Sentiment/position are heuristic (lexicon + structure), not a hosted LLM, keeping runs cheap & deterministic.

### Deployment

```bash
npm install            # install dependencies
npm run build          # compile TypeScript → dist/
npm test               # run the Jest suite
npm run lint           # ESLint
npx playwright install chromium   # local browser (Docker image already has it)
apify push             # deploy to Apify
apify run --purge      # run locally
```

The Docker image is `apify/actor-node-playwright-chrome:22` (Chromium preinstalled); `npm run build`
runs during the image build.

### Testing guide

`npm test` runs unit, parser (fixture-based), retry, validation, statistics, adapter (mock page),
engine integration, and full end-to-end (browserless) tests. Fixtures live in `tests/fixtures/`.

### Troubleshooting

| Symptom | Cause | Fix |
|---------|-------|-----|
| All answers `error: login wall` | Not authenticated | Provide `cookies` |
| `error: captcha …` | Bot check triggered | Enable Apify residential proxy; retry |
| Empty answers | Selectors changed | Update `src/shared/adapters/<platform>.ts` |

### Version history

- **1.0.0** — Initial production release: four adapters, parser, scoring, reports, full test suite.

# Actor input Schema

## `brand` (type: `string`):

The brand whose visibility inside AI answers you want to measure.

## `industry` (type: `string`):

Industry / category context used to ground the questions (e.g. 'CRM software').

## `country` (type: `string`):

Target market/geography context (e.g. 'United States').

## `questions` (type: `array`):

Questions to ask each AI platform. Each question is asked on every selected platform.

## `platforms` (type: `array`):

Which AI answer engines to query. Leave empty to query all four.

## `knownCompetitors` (type: `array`):

Optional list of known competitors to improve position/competitor detection accuracy.

## `headless` (type: `boolean`):

Run the browser in headless mode.

## `screenshotOnFailure` (type: `boolean`):

Capture a screenshot to the Key-Value Store when an answer cannot be obtained.

## `maxRetries` (type: `integer`):

Number of retry attempts per (platform, question) on transient failures.

## `askTimeoutMs` (type: `integer`):

Maximum time to wait for a single answer to complete.

## `useApifyProxy` (type: `boolean`):

Route browser traffic through Apify Proxy (residential group preferred).

## `cookies` (type: `array`):

Optional array of cookies (name/value/domain/path) to authenticate sessions on platforms that require login.

## `mockMode` (type: `boolean`):

Run fully offline with deterministic synthetic answers (no browser, no auth). Useful for demos, testing, and example runs.

## Actor input object example

```json
{
  "brand": "HubSpot",
  "industry": "CRM software",
  "country": "United States",
  "questions": [
    "best CRM for startups",
    "best CRM for small business",
    "HubSpot alternatives"
  ],
  "platforms": [
    "ChatGPT",
    "Gemini",
    "Claude",
    "Perplexity"
  ],
  "knownCompetitors": [],
  "headless": true,
  "screenshotOnFailure": true,
  "maxRetries": 2,
  "askTimeoutMs": 90000,
  "useApifyProxy": true,
  "cookies": [],
  "mockMode": false
}
```

# Actor output Schema

## `results` (type: `string`):

Dataset with one record per (question, platform).

## `summary` (type: `string`):

Aggregate statistics and scores.

## `report` (type: `string`):

Human-readable visibility report.

# 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 = {
    "questions": [
        "best CRM for startups",
        "best CRM for small business",
        "HubSpot alternatives"
    ]
};

// Run the Actor and wait for it to finish
const run = await client.actor("muhammad-bilal/ai-visibility-checker").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 = { "questions": [
        "best CRM for startups",
        "best CRM for small business",
        "HubSpot alternatives",
    ] }

# Run the Actor and wait for it to finish
run = client.actor("muhammad-bilal/ai-visibility-checker").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 '{
  "questions": [
    "best CRM for startups",
    "best CRM for small business",
    "HubSpot alternatives"
  ]
}' |
apify call muhammad-bilal/ai-visibility-checker --silent --output-dataset

```

## MCP server setup

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

```

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/actors/7Ua9ixhC0vXbI7v6j/builds/qqvhi4lE2aa1ickb3/openapi.json