# AI Brand Visibility Tracker — ChatGPT, Perplexity, Gemini (`alaudinburki/ai-brand-visibility-tracker`) Actor

Ask the AI assistants the questions your customers ask, and see per answer whether your brand is mentioned, how it ranks against competitors, and which sources get cited. Bring your own API keys — no token markup.

- **URL**: https://apify.com/alaudinburki/ai-brand-visibility-tracker.md
- **Developed by:** [alaudin burki](https://apify.com/alaudinburki) (community)
- **Categories:**
- **Stats:** 2 total users, 1 monthly users, 0.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $50.00 / 1,000 results

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/actors/running/actors-in-store.md#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 Brand Visibility Tracker — ChatGPT, Claude, Perplexity, Gemini

37% of consumers now start product research in AI tools instead of Google. This Actor asks the AI assistants the questions your customers ask them — and tells you, per answer, whether your brand was mentioned, where it ranked against competitors, and which sources the AI cited.

### What you get

One flat, CSV-ready row per (provider × question):

| Field | Description |
|---|---|
| `provider`, `model` | Which AI answered (openai / anthropic / perplexity / gemini) |
| `prompt` | The question asked |
| `mentioned` | Was your brand in the answer? |
| `mentionCount`, `firstPosition` | How often and how early |
| `rankAmongTracked` | 1 = your brand was named before every tracked competitor |
| `competitorsMentioned`, `competitorMentionCounts` | Who else the AI recommended |
| `citedUrls`, `citedDomains` | The sources behind the answer — the pages to get featured on |
| `answerText` | The full answer, for auditing |
| `askedAt` | ISO timestamp |

Plus a **SUMMARY** in the key-value store: overall visibility %, average rank when mentioned, per-provider split, and competitor share of voice — one object a scheduled run can diff week over week.

### Sample output

```json
{
  "provider": "perplexity",
  "model": "sonar",
  "prompt": "What are the best note-taking apps?",
  "mentioned": true,
  "mentionCount": 2,
  "rankAmongTracked": 1,
  "entitiesDetected": 3,
  "competitorsMentioned": "Evernote,Obsidian",
  "competitorMentionCounts": "Evernote:1,Obsidian:1",
  "citedDomains": "zapier.com,pcmag.com,reddit.com",
  "answerLength": 1874,
  "askedAt": "2026-09-02T09:00:00.000Z"
}
```

### How it works

1. Give it your **brand**, your **competitors**, and either a **topic** (auto-generates a 5-question panel: best / recommend / compare / choose / list) or your own custom questions.
2. Add **your own API keys** for any of OpenAI, Anthropic, Perplexity, Gemini. Keys are stored as Apify secrets and used only to run your queries — **this Actor does not resell tokens**, so you pay the providers their real price, not a markup.
3. Every question is asked to every provider you enabled. Answers are analyzed deterministically — mention detection is whole-word and case-insensitive, rank is order of first appearance among tracked names.

### Typical setups

- **Agency weekly report** — schedule it weekly per client brand; diff `SUMMARY` to show visibility moving.
- **Citation-source mining** — `citedDomains` across Perplexity answers is the list of pages to pitch or optimize for AI search (GEO).
- **Competitor watch** — leave `brandName` as your client and load `competitors` with the category; share of voice comes out per run.

### Honest limitations

- **Only Perplexity searches the live web and returns citations.** ChatGPT, Claude and Gemini (as called here, without browsing) answer from training data — that's still what their hundreds of millions of users see, but it moves slowly. The output labels every row by provider so you never mix the two up.
- LLM answers vary between runs by nature. Visibility % over a panel of questions is meaningful; a single answer is not. For trend data, keep the same inputs and schedule the run.
- Mention detection is exact whole-word matching on the name and your aliases. Add misspellings or old names as aliases if the AI uses them.

### Cost control

- You bring the API keys, so LLM cost is yours at provider prices — the defaults (gpt-4o-mini, sonar, gemini-2.0-flash, claude-sonnet-5) are the cheap tiers, roughly a cent or less per answer.
- `maxItems` is a hard cap on analyzed answers, strictly enforced.
- A rejected API key fails the run immediately with a readable message — not after burning your quota.

# Actor input Schema

## `brandName` (type: `string`):

The brand or product name to look for in AI answers, e.g. Notion.

## `brandAliases` (type: `array`):

Other names the AI might use for the same brand (old names, abbreviations, product lines). Counted as brand mentions.

## `competitors` (type: `array`):

Competitor names to track in the same answers. Rank is the order of first mention among your brand and these names.

## `topic` (type: `string`):

What your customers would ask about, e.g. 'note-taking apps'. Generates a 5-question panel (best/recommend/compare/choose/list). Ignored if you supply custom prompts below.

## `prompts` (type: `array`):

Your own questions, one per line. If set, these replace the auto-generated panel.

## `openaiApiKey` (type: `string`):

Enables ChatGPT answers (default model gpt-4o-mini). Your key is used only to run your own queries — this Actor does not resell tokens.

## `anthropicApiKey` (type: `string`):

Enables Claude answers (default model claude-sonnet-5).

## `perplexityApiKey` (type: `string`):

Enables Perplexity answers (default model sonar). Perplexity searches the live web and returns citations — the strongest signal for who AI search recommends today.

## `geminiApiKey` (type: `string`):

Enables Gemini answers (default model gemini-2.0-flash).

## `models` (type: `object`):

Optional model per provider, e.g. {"openai": "gpt-4o", "anthropic": "claude-opus-5"}. Leave empty for the cost-efficient defaults.

## `maxItems` (type: `integer`):

Hard limit on analyzed answers (providers × questions). Strictly enforced — you will never be charged for more than this.

## Actor input object example

```json
{
  "brandName": "Notion",
  "brandAliases": [],
  "competitors": [
    "Evernote",
    "Obsidian"
  ],
  "topic": "note-taking apps",
  "prompts": [],
  "models": {},
  "maxItems": 100
}
```

# Actor output Schema

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

The dataset of results produced by this run.

# 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 = {
    "brandName": "Notion",
    "brandAliases": [],
    "competitors": [
        "Evernote",
        "Obsidian"
    ],
    "topic": "note-taking apps",
    "prompts": [],
    "maxItems": 100
};

// Run the Actor and wait for it to finish
const run = await client.actor("alaudinburki/ai-brand-visibility-tracker").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 = {
    "brandName": "Notion",
    "brandAliases": [],
    "competitors": [
        "Evernote",
        "Obsidian",
    ],
    "topic": "note-taking apps",
    "prompts": [],
    "maxItems": 100,
}

# Run the Actor and wait for it to finish
run = client.actor("alaudinburki/ai-brand-visibility-tracker").call(run_input=run_input)

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

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

```

## CLI example

```bash
echo '{
  "brandName": "Notion",
  "brandAliases": [],
  "competitors": [
    "Evernote",
    "Obsidian"
  ],
  "topic": "note-taking apps",
  "prompts": [],
  "maxItems": 100
}' |
apify call alaudinburki/ai-brand-visibility-tracker --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,alaudinburki/ai-brand-visibility-tracker"
        }
    }
}

```

The hosted server signs you in with OAuth on first connect, so no API token belongs in this config. Clients without OAuth support can send an `Authorization: Bearer <APIFY_API_TOKEN>` header instead, using a token from API & Integrations in Apify Console (https://console.apify.com/settings/integrations).

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/actors/ZowdQ2WVIJ3VawphY/builds/xdjJoIztlt4CPIJ3W/openapi.json