# AI Brand Monitor: GEO / AI Search Visibility Tracker (`foxlabs/ai-brand-monitor`) Actor
Track how your brand appears in AI answers across ChatGPT, Gemini & Claude. Measure mention rate, share-of-voice vs competitors, ranking & cited sources — with multi-sample reliability. GEO / AEO visibility data for SEO agencies, brand & PR teams. Pay-as-you-go.
- **URL**: https://apify.com/foxlabs/ai-brand-monitor.md
- **Developed by:** [Berkan Kaplan](https://apify.com/foxlabs) (community)
- **Categories:** SEO tools, AI
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks
- **User rating**: No ratings yet
## Pricing
Pay per event
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 a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases.
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.
In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js.md):
```bash
npm install apify-client
```
In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.md):
```bash
pip install apify-client
```
In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md):
````bash
# MacOS / Linux
curl -fsSL https://apify.com/install-cli.sh | bash
# Windows
irm https://apify.com/install-cli.ps1 | iex
```bash
In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp.md).
If your project is in a different language, use 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 Monitor — GEO / AI Search Visibility Tracker
**Is your brand recommended by ChatGPT, Gemini, Perplexity and Claude — or are your competitors?**
Buyers no longer "Google it." They ask an AI. When someone asks *"what's the best project management software?"*, the AI names a handful of brands — and if yours isn't one of them, you lost that customer before you ever saw them. This is the new SEO, and it's called **GEO** (Generative Engine Optimization) / **AEO** (Answer Engine Optimization).
This Actor measures, with statistical rigor, **exactly how visible your brand is inside AI answers** — and tells you what to do about it.
---
### Why this tracker is different
Most AI-visibility tools ask each question **once** and report the result as if it were the truth. But AI answers are **non-deterministic** — ask the same question three times and you can get three different brand lists. A single-shot check is noise dressed up as data.
| | Typical trackers | **AI Brand Monitor** |
|---|---|---|
| Sampling | Ask once per prompt | **Ask N times, report a mention RATE + stability** |
| Reliability | A coin-flip snapshot | Statistically grounded, repeatable score |
| Cited sources | Opaque redirect URLs / titles | **Real publisher domains** (`g2.com`, `wrike.com`…) |
| Actionability | "Improve your visibility" | **Citation-gap list**: the exact domains to earn citations on next |
| Sentiment | Sometimes | Positive / neutral / critical, on the brand's actual context |
> We ran the same prompt 3× during testing and watched the mention rate move between runs. That variance is *real* — and it's exactly why one-shot tools mislead. We report the rate **and** the stability, so you know how much to trust each number.
---
### What you get
For a brand + category (and optional competitors), the Actor sends realistic buyer-intent prompts to AI engines with **live web search enabled**, samples each prompt multiple times, and returns:
**Per prompt (one row each):**
- **Mention rate %** — in how many samples your brand appeared
- **Stability %** — how consistent that outcome was across samples
- **Average rank** — where your brand lands among the brands the AI lists
- **Sentiment** — how the AI describes your brand (positive / neutral / critical)
- **Competitors mentioned** — who you're up against in that answer
- **Cited sources** — the real domains the AI grounded its answer on
- **Answer excerpt** — the raw AI response, for evidence
**Summary scorecard (one row):**
- **AI Visibility Index (0–100)** — one headline score (mention rate 50% + rank 30% + citation rate 20%), with a band: *strong (70+) / mid-tier / visibility gap*
- **Trend** — Δ vs. your previous run for the same brand, so scheduled runs show whether you're gaining or losing ground
- **AI visibility %** — overall mention rate across all samples and engines
- **Share of voice %** — your mentions vs. each competitor
- **Overall sentiment** + breakdown
- **Citation rate** — how often AI engines cite *your own domain* as a source (add your `brandDomain`)
- **Top cited sources** — the domains shaping answers in your category
- **Citation gap** — domains that ground *competitor-only* answers where you're absent. **This is your GEO to-do list:** earn a presence on these and you move the needle.
#### Built for automation & AI agents
- **Webhooks / alerts** — set a `webhookUrl` and we POST the full scorecard to Slack, Make, Zapier or n8n the moment a run finishes.
- **Schedule it** — pair with Apify Scheduler for weekly tracking; the trend delta does the rest.
- **Agent-friendly inputs** — accepts the field names AI agents use interchangeably (`brand`/`brandName`/`company`, `competitors`/`rivals`, `engines`/`llms`, `prompts`/`questions`).
#### Use it as an MCP tool (inside any AI agent — no install)
Call this Actor directly from Claude, Cursor, ChatGPT agents, LangChain, Make, Zapier or n8n via the **Model Context Protocol**:
**Hosted (zero setup)** — point your MCP client at:
````
https://mcp.apify.com?tools=foxlabs/ai-brand-monitor
````
**Self-hosted (stdio clients — Claude Desktop, Cursor, Cline, Continue):**
```bash
npx @apify/actors-mcp-server --tools foxlabs/ai-brand-monitor
````
```json
{
"mcpServers": {
"ai-brand-monitor": {
"command": "npx",
"args": ["-y", "@apify/actors-mcp-server", "--tools", "foxlabs/ai-brand-monitor"],
"env": { "APIFY_TOKEN": "YOUR_APIFY_TOKEN" }
}
}
}
```
Your agent can then ask *"How visible is Notion in AI search?"* and get the scorecard back inline.
***
### Quick start
1. Enter your **brand** (e.g. `Notion`) and **category** (e.g. `project management software`).
2. (Optional) Add **competitors** to benchmark against.
3. Run it.
That's it — no API keys, no setup. Sensible defaults are pre-filled.
```json
{
"brand": "Notion",
"category": "project management software",
"competitors": ["Asana", "Trello", "ClickUp", "Monday.com"],
"engines": ["gemini", "openai", "anthropic"],
"maxPromptsPerEngine": 5,
"samplesPerPrompt": 3
}
```
***
### Engines
| Engine | Status |
|---|---|
| **Gemini (Google)** — live Google Search grounding | ✅ Supported out of the box, no setup |
| **ChatGPT (OpenAI)** — live web search | ✅ Supported out of the box, no setup |
| **Claude (Anthropic)** — live web search | ✅ Supported out of the box, no setup |
| Perplexity | 🔑 Bring your own API key |
Three engines run with **zero setup** — Gemini, ChatGPT and Claude, each with real-time web search enabled. To also query Perplexity, paste your own Perplexity API key in the **Advanced** inputs; it runs on your account.
> Engines disagree more than you'd think. In our own testing, Gemini and ChatGPT both recommended Notion for "project management software" — but Claude didn't mention it at all. Tracking multiple engines is the whole point: your visibility isn't one number, it's different on every surface.
***
### Input reference
| Field | What it does |
|---|---|
| `brand` | The brand to track in AI answers |
| `category` | Product category — used to auto-generate realistic buyer prompts |
| `competitors` | Brands to benchmark share-of-voice against |
| `engines` | Which AI engines to query (Gemini supported by default) |
| `maxPromptsPerEngine` | How many buyer-intent prompts (1–25). More = broader coverage |
| `samplesPerPrompt` | How many times to repeat each prompt (1–5). More = more reliable |
| `prompts` | *(Advanced)* Your own exact prompts, overriding auto-generation |
| `*ApiKey` | *(Advanced)* Bring your own key per engine |
***
### How it works (transparent by design)
1. **Prompt generation** — realistic buyer-intent questions are built from your category (or you supply your own).
2. **Multi-sample querying** — each prompt is sent to each engine `samplesPerPrompt` times, with live web search on.
3. **Deterministic analysis** — brand and competitor mentions are detected by exact token matching (no fuzzy guessing); rank is the order brands appear in; sentiment is classified from the sentences that mention your brand.
4. **Aggregation** — mention rate, stability, share-of-voice, sentiment, cited sources and the citation gap are computed across all samples.
No scraping, no cookie tricks — only official AI provider APIs.
> **A note on sentiment (v1):** sentiment is classified with a transparent, deterministic keyword model over the brand's mention context. It's a fast, explainable signal — not a black-box LLM judgment.
***
### Who it's for
- **SEO / GEO agencies** — sell evidence-backed AI-visibility reports to clients (recurring).
- **Brand & marketing teams** — track your standing vs. competitors over time.
- **Founders** — find out, today, whether AI is recommending you or your rivals.
Schedule it weekly to watch your AI visibility trend — because the answers change, and so should your strategy.
***
*Built by foXLabs.*
# Actor input Schema
## `brand` (type: `string`):
The brand you want to track in AI answers (e.g. "Notion"). This is what we look for in each AI engine's response.
## `category` (type: `string`):
The product category buyers ask about (e.g. "project management software"). Used to auto-generate realistic buyer-intent prompts. Leave the Advanced prompts field empty to use these.
## `brandDomain` (type: `string`):
Your brand's website domain (e.g. "notion.so"). If provided, we measure how often AI engines cite YOUR domain as a source — the citation-rate component of the Visibility Index.
## `competitors` (type: `array`):
Competitor brands to compare against. We measure how often each is mentioned vs your brand. Leave empty to track your brand only.
## `engines` (type: `array`):
Which AI search engines to check. An engine is skipped automatically if no API key is available for it (yours or ours).
## `maxPromptsPerEngine` (type: `integer`):
How many buyer-intent prompts to run per engine. More prompts = broader coverage, higher cost.
## `samplesPerPrompt` (type: `integer`):
AI answers change every time you ask. We run each prompt this many times and report a mention RATE + stability — not a single noisy snapshot. 3 is recommended; higher = more statistically reliable, higher cost.
## `prompts` (type: `array`):
POWER USERS ONLY. Exact prompts to send to every engine. If filled, this OVERRIDES the auto-generated prompts from Category. Use {brand} and {category} as placeholders if you like.
## `webhookUrl` (type: `string`):
If set, we POST the summary scorecard (JSON) to this URL when the run finishes — wire it to Slack, Make, Zapier or n8n for AI-visibility alerts.
## `openaiApiKey` (type: `string`):
Bring your own OpenAI key to run ChatGPT queries on your own account (no markup). Leave empty to use ours (billed via the actor price).
## `anthropicApiKey` (type: `string`):
Bring your own Anthropic key for Claude queries. Leave empty to use ours.
## `perplexityApiKey` (type: `string`):
Bring your own Perplexity key for Perplexity queries. Leave empty to use ours.
## `geminiApiKey` (type: `string`):
Bring your own Google AI Studio (Gemini) key. Leave empty to use ours.
## Actor input object example
```json
{
"brand": "Notion",
"category": "project management software",
"brandDomain": "notion.so",
"competitors": [
"Asana",
"Trello",
"ClickUp",
"Monday.com"
],
"engines": [
"gemini",
"openai",
"anthropic"
],
"maxPromptsPerEngine": 5,
"samplesPerPrompt": 2,
"prompts": []
}
```
# Actor output Schema
## `dataset` (type: `string`):
No description
## `summary` (type: `string`):
No description
# 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 '' with your token
const client = new ApifyClient({
token: '',
});
// Prepare Actor input
const input = {
"brand": "Notion",
"category": "project management software",
"brandDomain": "notion.so",
"competitors": [
"Asana",
"Trello",
"ClickUp",
"Monday.com"
],
"engines": [
"gemini",
"openai",
"anthropic"
],
"maxPromptsPerEngine": 5,
"samplesPerPrompt": 2,
"prompts": []
};
// Run the Actor and wait for it to finish
const run = await client.actor("foxlabs/ai-brand-monitor").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 '' with your token.
client = ApifyClient("")
# Prepare the Actor input
run_input = {
"brand": "Notion",
"category": "project management software",
"brandDomain": "notion.so",
"competitors": [
"Asana",
"Trello",
"ClickUp",
"Monday.com",
],
"engines": [
"gemini",
"openai",
"anthropic",
],
"maxPromptsPerEngine": 5,
"samplesPerPrompt": 2,
"prompts": [],
}
# Run the Actor and wait for it to finish
run = client.actor("foxlabs/ai-brand-monitor").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 '{
"brand": "Notion",
"category": "project management software",
"brandDomain": "notion.so",
"competitors": [
"Asana",
"Trello",
"ClickUp",
"Monday.com"
],
"engines": [
"gemini",
"openai",
"anthropic"
],
"maxPromptsPerEngine": 5,
"samplesPerPrompt": 2,
"prompts": []
}' |
apify call foxlabs/ai-brand-monitor --silent --output-dataset
```
## MCP server setup
```json
{
"mcpServers": {
"apify": {
"command": "npx",
"args": [
"mcp-remote",
"https://mcp.apify.com/?tools=foxlabs/ai-brand-monitor",
"--header",
"Authorization: Bearer "
]
}
}
}
```
## OpenAPI specification
```json
{
"openapi": "3.0.1",
"info": {
"title": "AI Brand Monitor: GEO / AI Search Visibility Tracker",
"description": "Track how your brand appears in AI answers across ChatGPT, Gemini & Claude. Measure mention rate, share-of-voice vs competitors, ranking & cited sources — with multi-sample reliability. GEO / AEO visibility data for SEO agencies, brand & PR teams. Pay-as-you-go.",
"version": "0.1",
"x-build-id": "8mzwRIefJgrheBBiS"
},
"servers": [
{
"url": "https://api.apify.com/v2"
}
],
"paths": {
"/acts/foxlabs~ai-brand-monitor/run-sync-get-dataset-items": {
"post": {
"operationId": "run-sync-get-dataset-items-foxlabs-ai-brand-monitor",
"x-openai-isConsequential": false,
"summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.",
"tags": [
"Run Actor"
],
"requestBody": {
"required": true,
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/inputSchema"
}
}
}
},
"parameters": [
{
"name": "token",
"in": "query",
"required": true,
"schema": {
"type": "string"
},
"description": "Enter your Apify token here"
}
],
"responses": {
"200": {
"description": "OK"
}
}
}
},
"/acts/foxlabs~ai-brand-monitor/runs": {
"post": {
"operationId": "runs-sync-foxlabs-ai-brand-monitor",
"x-openai-isConsequential": false,
"summary": "Executes an Actor and returns information about the initiated run in response.",
"tags": [
"Run Actor"
],
"requestBody": {
"required": true,
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/inputSchema"
}
}
}
},
"parameters": [
{
"name": "token",
"in": "query",
"required": true,
"schema": {
"type": "string"
},
"description": "Enter your Apify token here"
}
],
"responses": {
"200": {
"description": "OK",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/runsResponseSchema"
}
}
}
}
}
}
},
"/acts/foxlabs~ai-brand-monitor/run-sync": {
"post": {
"operationId": "run-sync-foxlabs-ai-brand-monitor",
"x-openai-isConsequential": false,
"summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.",
"tags": [
"Run Actor"
],
"requestBody": {
"required": true,
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/inputSchema"
}
}
}
},
"parameters": [
{
"name": "token",
"in": "query",
"required": true,
"schema": {
"type": "string"
},
"description": "Enter your Apify token here"
}
],
"responses": {
"200": {
"description": "OK"
}
}
}
}
},
"components": {
"schemas": {
"inputSchema": {
"type": "object",
"properties": {
"brand": {
"title": "Brand / company name",
"type": "string",
"description": "The brand you want to track in AI answers (e.g. \"Notion\"). This is what we look for in each AI engine's response."
},
"category": {
"title": "Category / market (for auto prompts)",
"type": "string",
"description": "The product category buyers ask about (e.g. \"project management software\"). Used to auto-generate realistic buyer-intent prompts. Leave the Advanced prompts field empty to use these."
},
"brandDomain": {
"title": "Brand domain (optional, for citation rate)",
"type": "string",
"description": "Your brand's website domain (e.g. \"notion.so\"). If provided, we measure how often AI engines cite YOUR domain as a source — the citation-rate component of the Visibility Index."
},
"competitors": {
"title": "Competitors (for share-of-voice)",
"type": "array",
"description": "Competitor brands to compare against. We measure how often each is mentioned vs your brand. Leave empty to track your brand only.",
"items": {
"type": "string"
}
},
"engines": {
"title": "AI engines to query",
"type": "array",
"description": "Which AI search engines to check. An engine is skipped automatically if no API key is available for it (yours or ours).",
"items": {
"type": "string",
"enum": [
"gemini",
"openai",
"anthropic",
"perplexity"
],
"enumTitles": [
"Gemini (Google)",
"ChatGPT (OpenAI)",
"Claude (Anthropic)",
"Perplexity — bring your own key"
]
}
},
"maxPromptsPerEngine": {
"title": "Prompts per engine",
"minimum": 1,
"maximum": 25,
"type": "integer",
"description": "How many buyer-intent prompts to run per engine. More prompts = broader coverage, higher cost."
},
"samplesPerPrompt": {
"title": "Samples per prompt (reliability)",
"minimum": 1,
"maximum": 5,
"type": "integer",
"description": "AI answers change every time you ask. We run each prompt this many times and report a mention RATE + stability — not a single noisy snapshot. 3 is recommended; higher = more statistically reliable, higher cost."
},
"prompts": {
"title": "Advanced — custom prompts (overrides auto)",
"type": "array",
"description": "POWER USERS ONLY. Exact prompts to send to every engine. If filled, this OVERRIDES the auto-generated prompts from Category. Use {brand} and {category} as placeholders if you like.",
"items": {
"type": "string"
}
},
"webhookUrl": {
"title": "Advanced — webhook / alert URL (optional)",
"type": "string",
"description": "If set, we POST the summary scorecard (JSON) to this URL when the run finishes — wire it to Slack, Make, Zapier or n8n for AI-visibility alerts."
},
"openaiApiKey": {
"title": "Advanced — your OpenAI API key (optional)",
"type": "string",
"description": "Bring your own OpenAI key to run ChatGPT queries on your own account (no markup). Leave empty to use ours (billed via the actor price)."
},
"anthropicApiKey": {
"title": "Advanced — your Anthropic API key (optional)",
"type": "string",
"description": "Bring your own Anthropic key for Claude queries. Leave empty to use ours."
},
"perplexityApiKey": {
"title": "Advanced — your Perplexity API key (optional)",
"type": "string",
"description": "Bring your own Perplexity key for Perplexity queries. Leave empty to use ours."
},
"geminiApiKey": {
"title": "Advanced — your Google Gemini API key (optional)",
"type": "string",
"description": "Bring your own Google AI Studio (Gemini) key. Leave empty to use ours."
}
}
},
"runsResponseSchema": {
"type": "object",
"properties": {
"data": {
"type": "object",
"properties": {
"id": {
"type": "string"
},
"actId": {
"type": "string"
},
"userId": {
"type": "string"
},
"startedAt": {
"type": "string",
"format": "date-time",
"example": "2025-01-08T00:00:00.000Z"
},
"finishedAt": {
"type": "string",
"format": "date-time",
"example": "2025-01-08T00:00:00.000Z"
},
"status": {
"type": "string",
"example": "READY"
},
"meta": {
"type": "object",
"properties": {
"origin": {
"type": "string",
"example": "API"
},
"userAgent": {
"type": "string"
}
}
},
"stats": {
"type": "object",
"properties": {
"inputBodyLen": {
"type": "integer",
"example": 2000
},
"rebootCount": {
"type": "integer",
"example": 0
},
"restartCount": {
"type": "integer",
"example": 0
},
"resurrectCount": {
"type": "integer",
"example": 0
},
"computeUnits": {
"type": "integer",
"example": 0
}
}
},
"options": {
"type": "object",
"properties": {
"build": {
"type": "string",
"example": "latest"
},
"timeoutSecs": {
"type": "integer",
"example": 300
},
"memoryMbytes": {
"type": "integer",
"example": 1024
},
"diskMbytes": {
"type": "integer",
"example": 2048
}
}
},
"buildId": {
"type": "string"
},
"defaultKeyValueStoreId": {
"type": "string"
},
"defaultDatasetId": {
"type": "string"
},
"defaultRequestQueueId": {
"type": "string"
},
"buildNumber": {
"type": "string",
"example": "1.0.0"
},
"containerUrl": {
"type": "string"
},
"usage": {
"type": "object",
"properties": {
"ACTOR_COMPUTE_UNITS": {
"type": "integer",
"example": 0
},
"DATASET_READS": {
"type": "integer",
"example": 0
},
"DATASET_WRITES": {
"type": "integer",
"example": 0
},
"KEY_VALUE_STORE_READS": {
"type": "integer",
"example": 0
},
"KEY_VALUE_STORE_WRITES": {
"type": "integer",
"example": 1
},
"KEY_VALUE_STORE_LISTS": {
"type": "integer",
"example": 0
},
"REQUEST_QUEUE_READS": {
"type": "integer",
"example": 0
},
"REQUEST_QUEUE_WRITES": {
"type": "integer",
"example": 0
},
"DATA_TRANSFER_INTERNAL_GBYTES": {
"type": "integer",
"example": 0
},
"DATA_TRANSFER_EXTERNAL_GBYTES": {
"type": "integer",
"example": 0
},
"PROXY_RESIDENTIAL_TRANSFER_GBYTES": {
"type": "integer",
"example": 0
},
"PROXY_SERPS": {
"type": "integer",
"example": 0
}
}
},
"usageTotalUsd": {
"type": "number",
"example": 0.00005
},
"usageUsd": {
"type": "object",
"properties": {
"ACTOR_COMPUTE_UNITS": {
"type": "integer",
"example": 0
},
"DATASET_READS": {
"type": "integer",
"example": 0
},
"DATASET_WRITES": {
"type": "integer",
"example": 0
},
"KEY_VALUE_STORE_READS": {
"type": "integer",
"example": 0
},
"KEY_VALUE_STORE_WRITES": {
"type": "number",
"example": 0.00005
},
"KEY_VALUE_STORE_LISTS": {
"type": "integer",
"example": 0
},
"REQUEST_QUEUE_READS": {
"type": "integer",
"example": 0
},
"REQUEST_QUEUE_WRITES": {
"type": "integer",
"example": 0
},
"DATA_TRANSFER_INTERNAL_GBYTES": {
"type": "integer",
"example": 0
},
"DATA_TRANSFER_EXTERNAL_GBYTES": {
"type": "integer",
"example": 0
},
"PROXY_RESIDENTIAL_TRANSFER_GBYTES": {
"type": "integer",
"example": 0
},
"PROXY_SERPS": {
"type": "integer",
"example": 0
}
}
}
}
}
}
}
}
}
}
```