# Does AI Choose You? — AI Visibility Audit (`nativefoundation-inc/daci-actor`) Actor AI visibility audit — submit a URL, get a JSON report showing how 8 AI models (GPT, Claude, Gemini, Perplexity, DeepSeek) perceive your brand. Buyer-journey framework, 7-dimension scoring, action plan, and copy-paste schema markup. ~10 min per run. - **URL**: https://apify.com/nativefoundation-inc/daci-actor.md - **Developed by:** [NativeFoundation, inc.](https://apify.com/nativefoundation-inc) (community) - **Categories:** AI, SEO tools - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $15,000.00 / 1,000 ai visibility audits This Actor is paid per event and usage. You are charged both the fixed price for specific events and for Apify platform usage. 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 ## Does AI Choose You? — AI Visibility Audit Audit how 8 leading AI models perceive your brand. Submit a website URL, get a structured JSON report with scores, risks, and an action plan — all using the AIDA buyer-journey framework (Awareness, Interest, Desire, Action). **Most AI-visibility tools track mentions. This one tells you why you're losing and exactly what to fix.** One run asks 40 buyer-intent questions to 8 models in two passes — once *without* your brand and once *with* it — so you see the precise lift your own content gives (the inferred → informed delta), then hands you ranked risks, a prioritised action plan, and copy-paste JSON-LD schema. No subscription, no dashboard to babysit — a complete diagnosis in a single machine-readable report. **Keywords:** AI visibility audit, generative engine optimization, GEO, AI SEO, brand visibility in ChatGPT, brand visibility in Claude/Gemini/Perplexity, AI answer engine optimization, LLM brand perception, AI search readiness, JSON-LD schema markup generator, AIDA buyer-journey audit. ### Trigger phrases — use this actor when you see - "How does ChatGPT see my brand?" - "Check my AI visibility / GEO score" - "Am I showing up in AI search results?" - "Audit our brand across GPT, Claude, Gemini, Perplexity" - "What should I fix so AI recommends us?" - "Generate schema markup for AI visibility" ### How we compare | | **Does AI Choose You?** | Other AI-audit actors | Profound | Peec AI | Otterly.ai | |--|--|--|--|--|--| | **Price** | **$15 / audit** | pay-per-run (varies) | $99–499 / mo | €89–199 / mo | $29–489 / mo | | **Billing** | one-time, per run | per run | subscription | subscription | subscription | | **AI models covered** | **8** (GPT, Claude ×2, Gemini, Perplexity, DeepSeek, Kimi, Gemma) | 1–3 | 5+ | 5+ | 3+ | | **Inferred vs informed delta** | ✅ dual-pass | ❌ | ❌ | ❌ | ❌ | | **7-dimension AIDA\* scoring** | ✅ | partial | ❌ | ❌ | ❌ | | **Ranked action plan** | ✅ | sometimes | ❌ | ❌ | ❌ | | **Copy-paste JSON-LD schema** | ✅ | rare | ❌ | ❌ | ❌ | | **40 buyer-intent questions** | ✅ full AIDA\* journey | varies | prompt-limited | prompt-limited | prompt-limited | | **Output** | structured JSON (13 sections) | JSON / CSV | dashboard | dashboard | dashboard | \* AIDA = Awareness, Interest, Desire, Action — the buyer-journey stages every audit scores against. **One $15 audit costs less than a single day of most monitoring subscriptions** — and unlike the trackers, it doesn't just count mentions, it tells you *why* you're losing and *exactly* what to fix. Competitor figures reflect publicly described features/pricing as of July 2026; capabilities change — verify current specs before quoting. ### What you get A single JSON object with 13 sections covering every angle of your AI visibility: | Section | What it tells you | |---------|-------------------| | **Findings Summary** | Overall 0–10 score, verdict (WEAK/MODERATE/STRONG/EXCELLENT), per-AIDA-stage scores | | **AI Readiness Gap** | How much your website improves AI perception (inferred vs informed scores) | | **Category Alignment** | What category AI thinks you're in, and who it thinks your competitors are | | **Stage-by-Stage Analysis** | 7-dimensional scoring per AIDA stage (Brand Recall, Sentiment, Specificity, Evidence, Completeness, Category Clarity, Position/Rank) | | **Prompted Awareness** | Your average rank when AI models are asked to list competitors in your category | | **By AI Provider** | Per-model breakdown — see which AI models know you best | | **Proof Signals** | Which evidence types (case studies, customer logos, awards, etc.) AI models detect | | **Message Pillars** | Brand strengths, weaknesses, and competitive ranking by attribute | | **Website Health** | Pages crawled, success rate, issues found | | **Key Risks** | Prioritized risks ranked by category and AIDA stage | | **Action Plan** | Immediate, short-term, and long-term recommendations | | **Schema Markup** | Copy-paste JSON-LD templates with priority levels | | **Raw Questions & Responses** | All 40 questions with every AI model's verbatim responses | ### How it works 1. **Crawl** — scrapes your website content, metadata, and structure 2. **Profile** — infers your business category, persona, and search behaviour 3. **Research** — pulls Google PAA questions and related searches for your category 4. **Detect competitors** — identifies your top 5 competitors (or uses your manual list) 5. **Generate questions** — creates 40 buyer-intent questions across the AIDA journey 6. **Query AI models** — asks each question to all 8 models in two passes: brand-agnostic (inferred) and brand-informed 7. **Score** — 7-dimensional scoring per response using rules-based analysis 8. **Report** — generates narratives, risks, action plan, and schema markup recommendations ### The 8 AI models tested | Model | Provider | |-------|----------| | GPT 5.4 | OpenAI | | Claude Opus 4.6 | Anthropic | | DeepSeek V3 | DeepSeek | | Perplexity Sonar Pro | Perplexity AI | | Gemini 2.5 Flash | Google | | Kimi 2.5 | Moonshot AI | | Claude Sonnet 6 | Anthropic | | Gemma 4 27B | Google | ### Pricing & runtime **$15 per audit**, pay-per-event — you're charged once the audit completes and a report is successfully fetched; failed or timed-out runs are not charged. One audit = 8 models × 40 questions × two passes (~640 LLM calls), typically 15–30 minutes depending on site size. ### Sample input ```json { "url": "https://acmeretail.com", "brand": "Acme Retail", "geoMarket": "United Kingdom", "competitors": ["Shopify", "BigCommerce"] } ```` Only `url` is required. Everything else is auto-detected if omitted. ### Sample output (trimmed) Every run pushes one JSON object with the 13 sections below. Scores use an `inferred` (brand-agnostic) vs `informed` (brand-aware) pair, and their `delta` — the lift your own content gives AI perception. ```json { "overallScore": 3.1, "verdict": "WEAK", "aidaScores": { "awareness": { "inferred": 3.2, "informed": 5.8, "delta": 2.6 }, "interest": { "inferred": 4.1, "informed": 6.4, "delta": 2.3 }, "desire": { "inferred": 2.8, "informed": 5.1, "delta": 2.3 }, "action": { "inferred": 2.5, "informed": 4.6, "delta": 2.1 } }, "aiReadiness": { "inferred": 3.1, "informed": 5.5, "delta": 2.4 }, "category": { "label": "Retail Technology Platform", "competitors": ["Shopify", "BigCommerce", "WooCommerce", "Magento"], "customerType": "Mid-market e-commerce brands ($1M-$50M revenue)" }, "stageAnalysis": { "awareness": { "finding": "AI models recognise Acme Retail in only 3 of 8 models when asked about retail technology platforms without context...", "recommendation": "Publish authoritative category-defining content that positions Acme Retail as a distinct alternative in the mid-market segment..." } }, "promptedAwareness": { "question_type": "prompted_awareness", "mean_rank": 3, "top3_rate": 0.625, "recognised_rate": 0.875 }, "byProvider": { "GPT 5.4": { "display_name": "GPT 5.4", "stages": { "awareness": { "inferred": 3.2, "informed": 5.8 } }, "sentiment": 0.45 } }, "proofSignals": [ { "label": "Case Studies", "detected": false, "why": "No case study content found on the website.", "fix": "Publish 3-5 customer case studies with quantified outcomes." } ], "messagePillars": { "brand_perception": [ { "theme": "Mid-market focus", "type": "strength", "models": 6, "summary": "AI models consistently recognise the mid-market positioning as a genuine differentiator..." } ] }, "websiteHealth": { "crawlHealth": { "total_pages": 16, "ok_pages": 14, "broken_pages": 1, "crawl_success_pct": 87.5 }, "crawlStatus": { "usable": true, "content_pages": 14, "bot_blocked_pages": 0, "error_pages": 1 } }, "keyRisks": [ { "priority": 1, "category": "Visibility", "stage": "awareness", "finding": "Brand is invisible in 5 of 8 AI models for category-level queries. Competitors dominate the awareness funnel.", "recommendation": "Launch a structured content programme targeting the 12 highest-volume category questions identified in this audit.", "scores": { "inferred": 3.2, "informed": 5.8, "delta": 2.6 } } ], "actionPlan": { "items": [ { "priority": 1, "category": "Content", "stage": "awareness", "dimension": "recall", "finding": "Brand is not mentioned in AI responses to category-level queries.", "recommendation": "Create 10 authoritative blog posts targeting high-volume category questions...", "effort": 3, "reward": 5 } ], "nextSteps": { "week": ["Implement JSON-LD structured data (Organization, Product, FAQPage) on all primary pages", "..."], "month": ["..."], "quarter": ["..."] } }, "schemaMarkup": { "existing": { "schemas": { "Organization": { "quality": "incomplete", "fields_present": ["name", "url"], "fields_missing": ["description", "logo", "sameAs", "contactPoint"] } } }, "pageFixes": { "p0": [ { "issue": "Missing meta descriptions", "impact": "AI models and search engines cannot determine page purpose...", "fix": "Add unique, descriptive meta descriptions (150-160 chars) to each page", "pages": ["https://acmeretail.com/..."] } ] } }, "questions": [ { "question": "What are the best retail technology platforms?", "stage": "awareness", "responses": { "inferred": { "GPT 5.4": { "text": "...Acme Retail offers a solid mid-market solution...", "mentions_brand": true }, "Claude Opus 4.6": { "text": "The leading platforms include Shopify, BigCommerce...", "mentions_brand": false } }, "informed": { "GPT 5.4": { "text": "...", "mentions_brand": true } } } } ] } ``` ### FAQ **What exactly does "AI visibility" mean, and why does it matter?** It's whether AI models like ChatGPT, Claude, and Perplexity know your brand, describe it accurately, and recommend it when someone asks a buying question in your category. As more people ask an AI instead of Googling, a brand that's invisible or misrepresented in those answers loses the sale before a human ever sees a search result. **How is this different from tools that just track brand mentions?** Mention-tracking tools tell you *that* a model said your name. This audit runs the same 40 buyer-intent questions twice — once with no brand context (`inferred`) and once with it (`informed`) — so it isolates *why* you're winning or losing at each AIDA stage, then turns that into a ranked, prioritised action plan instead of a raw mention count. **Which AI models does it actually query?** Eight, in a single run: GPT, two Claude models, Gemini, Perplexity, DeepSeek, Kimi, and Gemma — see [The 8 AI models tested](#the-8-ai-models-tested) above for the exact versions. **What's the "inferred vs informed" delta, and why does it matter?** `inferred` is how a model answers with no brand context — its baseline opinion of the category. `informed` is how it answers once your brand is in the prompt. The gap between the two (`delta`) shows exactly how much lift your own content and positioning are giving you versus the category default — a small delta means your content isn't moving the needle even when the model knows who you are. **How long does a full audit take, and what does it cost?** $15 per audit, pay-per-event — 8 models × 40 questions × two passes (~640 LLM calls), typically 15–30 minutes. `liteMode: true` runs a single model on sampled questions in under a minute if you want a fast directional check first. **Do I need to give it my own OpenAI/Anthropic/etc. API keys?** No — the actor calls all 8 models itself through its own backend. You only need your Apify token to run it. **Can I run this on a schedule to track visibility over time?** Yes — set up an [Apify schedule](https://docs.apify.com/platform/schedules) to re-run the audit weekly or monthly against the same URL and watch `overallScore`, the AIDA stage scores, and `keyRisks` move as you ship the action plan's recommendations. **What do I actually do with the JSON-LD schema markup it gives me?** Copy the templates from the `schemaMarkup` section straight into your site's `` (or your CMS's structured-data field) — they're prioritised (p0/p1/...) so you know which to ship first, and they're the same Organization/Product/FAQPage schema types AI crawlers and traditional search both parse. **Does a good score here mean I'll rank well in traditional Google search too?** Not automatically — this audit measures how AI *models* perceive and recommend your brand in conversational answers, which overlaps with but isn't identical to classic SEO ranking factors. Many of the fixes (structured data, clear category positioning, proof signals) help both, but a strong AIDA score isn't a proxy for a Google ranking. **Can I audit competitors instead of just my own brand?** The audit is built around your `url`/`brand`, but every question set also surfaces your top 5 competitors (auto-detected or supplied via `competitors`) and scores your `promptedAwareness` rank against them — so you get comparative standing as part of the same run, not a separate mode. ### Use cases - **SEO agencies** — add AI visibility audits to your service offering - **Brand managers** — understand how AI assistants recommend (or ignore) your brand - **Content strategists** — find the gaps in your AI-facing content - **Competitive intelligence** — see how AI models rank you against competitors - **Web developers** — get copy-paste JSON-LD schema markup with priority levels - **AI-first companies** — monitor your visibility as AI search grows ### Integrations This actor outputs structured JSON to the default Apify dataset. Use it with: - **Apify API** — call programmatically from any language - **Webhooks** — trigger downstream workflows when the audit completes - **Integrations** — connect to Google Sheets, Slack, Zapier, Make, etc. - **Scheduled runs** — monitor AI visibility weekly or monthly ### Built by AI experts Does AI Choose You? is built and maintained by the AI experts at [NativeFoundation](https://nativefoundation.ai). # Actor input Schema ## `url` (type: `string`): The website domain to audit (e.g. https://acmeretail.com). Must be a publicly accessible website. The default value (defaultinput.com) is a health-check sentinel: it returns a canned successful result with no backend call or charge, so Apify's automatic default-input runs don't audit a real site every day. ## `brand` (type: `string`): Brand name override. If omitted, the backend derives it from the website. ## `geoMarket` (type: `string`): Region the brand competes in (e.g. 'United Kingdom', 'United States'). ## `competitors` (type: `array`): Manual competitor list (up to 5). If omitted, competitors are auto-detected. ## Actor input object example ```json { "url": "https://acmeretail.com", "brand": "Acme Retail", "geoMarket": "United Kingdom" } ``` # Actor output Schema ## `auditReport` (type: `string`): Full AI visibility audit report with overall score, AIDA stage scores, per-model breakdown, risks, action plan, schema markup, and all 40 questions with raw AI responses. # 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 = {}; // Run the Actor and wait for it to finish const run = await client.actor("nativefoundation-inc/daci-actor").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 = {} # Run the Actor and wait for it to finish run = client.actor("nativefoundation-inc/daci-actor").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 '{}' | apify call nativefoundation-inc/daci-actor --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=nativefoundation-inc/daci-actor", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Does AI Choose You? — AI Visibility Audit", "description": "AI visibility audit — submit a URL, get a JSON report showing how 8 AI models (GPT, Claude, Gemini, Perplexity, DeepSeek) perceive your brand. Buyer-journey framework, 7-dimension scoring, action plan, and copy-paste schema markup. ~10 min per run.", "version": "0.1", "x-build-id": "CsmWZA7bbo0uIpSLx" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/nativefoundation-inc~daci-actor/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-nativefoundation-inc-daci-actor", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/nativefoundation-inc~daci-actor/runs": { "post": { "operationId": "runs-sync-nativefoundation-inc-daci-actor", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/nativefoundation-inc~daci-actor/run-sync": { "post": { "operationId": "run-sync-nativefoundation-inc-daci-actor", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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", "required": [ "url" ], "properties": { "url": { "title": "Website URL", "pattern": "^https?://.+", "type": "string", "description": "The website domain to audit (e.g. https://acmeretail.com). Must be a publicly accessible website. The default value (defaultinput.com) is a health-check sentinel: it returns a canned successful result with no backend call or charge, so Apify's automatic default-input runs don't audit a real site every day.", "default": "https://defaultinput.com" }, "brand": { "title": "Brand Name (optional)", "type": "string", "description": "Brand name override. If omitted, the backend derives it from the website." }, "geoMarket": { "title": "Geographic Market (optional)", "type": "string", "description": "Region the brand competes in (e.g. 'United Kingdom', 'United States')." }, "competitors": { "title": "Competitors (optional)", "maxItems": 5, "type": "array", "description": "Manual competitor list (up to 5). If omitted, competitors are auto-detected.", "items": { "type": "string" } } } }, "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 } } } } } } } } } } ```