# Company Lookup – B2B Firmographic Enrichment | $1.50/1K (`bovi/company-lookup-scraper`) Actor **Enrich company names or domains** with firmographic data: industry, **employee count**, founded year, HQ location, website, logo, and **social profiles** (LinkedIn, Twitter, GitHub). - **URL**: https://apify.com/bovi/company-lookup-scraper.md - **Developed by:** [Vitalii Bondarev](https://apify.com/bovi) (community) - **Categories:** Other, MCP servers - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $1.50 / 1,000 companies 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 ## Company Lookup – B2B Firmographic Data by Name or Domain Used by sales reps enriching CRM exports before outreach, growth teams validating prospect firmographics at scale, and AI agents looking up company context mid-workflow. **Enrich a list of company names or domains** with structured firmographic data in one run. Returns industry, employee count, founded year, HQ location, website, and social profiles (LinkedIn, Twitter, GitHub, Facebook) — sourced from Wikipedia/Wikidata via the free DuckDuckGo Instant Answer API, with domain resolution via Clearbit autocomplete. No API key required. **Pay per result.** **$1.50/1K records** (Pay Per Event). Social profiles add-on +$0.50/1K (when LinkedIn/Twitter/GitHub found). First 10 results free. **Failed lookups are never charged** — you pay only for successful enrichments. This is the fastest zero-cost path from a raw list of company names (from a CRM export, a scraped list, or a spreadsheet) to a fully enriched B2B dataset. --- ### What you get per company | Field | Description | |---|---| | `query` | Original input (name or domain) | | `name` | Canonical company name | | `domain` | Primary domain (e.g. `stripe.com`) | | `description` | Short company description from Wikipedia | | `industry` | Industry label(s) (e.g. `Financial services, Payment processor`) | | `size_range` | Employee band: `1-10`, `11-50`, `51-200`, `201-500`, `501-1000`, `1001-5000`, `5001-10000`, `10000+` | | `employees` | Raw employee count (most recent figure available) | | `founded` | Founding year as 4-digit string (e.g. `2010`) | | `hq_city` | HQ city | | `hq_country` | HQ country | | `hq_address` | Full address / location string if available | | `website` | Canonical website URL | | `logo_url` | Company logo image URL (free Clearbit logo CDN, by domain) | | `twitter` | Twitter handle (without @) | | `linkedin` | LinkedIn company slug | | `github` | GitHub org/user handle | | `facebook` | Facebook page slug or ID | | `company_type` | `Public` / `Private` / `Non-profit` / etc. | | `registration_id` | Stock ticker, ISIN, or other registration ID | | `status` | `Active` for found companies | | `wikipedia_url` | Link to Wikipedia article | | `wikidata_id` | Wikidata Q-identifier (e.g. `Q7624104`) | | `sources` | Which sources contributed to this record (`ddg`, `wikidata`, `wikipedia`, `clearbit`) | | `field_sources` | Per-field provenance map — exactly which source supplied each value | | `parse_confidence` | 0–1 data quality score | | `warnings` | Machine-readable quality codes (empty = clean) | --- ### Use cases - **Lead enrichment** — bulk-enrich CRM exports with firmographics before outreach - **Domain verification** — resolve company names to canonical domains at scale - **Market research** — industry, size, and founding year across a sector - **Prospect scoring** — segment by size band and company type for ABM targeting - **Competitive intelligence** — gather social profiles and Wikipedia descriptions for a competitor list - **B2B outreach personalization** — add HQ city + industry context to cold email sequences --- ### How to use #### By company name ```json { "companies": ["Stripe", "Shopify", "Databricks", "Figma Inc", "OpenAI"], "maxItems": 0 } ```` #### By domain ```json { "companies": ["stripe.com", "shopify.com", "vercel.com"], "maxItems": 0 } ``` #### Mixed names and domains (with quality filtering) ```json { "companies": ["Stripe", "shopify.com", "Databricks", "vercel.com"], "skipLowConfidence": true, "requestDelayMs": 500 } ``` *** ### Input parameters | Parameter | Type | Default | Description | |---|---|---|---| | `companies` | `string[]` | required | Company names and/or domains to look up | | `maxItems` | `integer` | `0` | Max records to output. `0` = no limit | | `skipLowConfidence` | `boolean` | `false` | Skip records with `parse_confidence < 0.3` | | `requestDelayMs` | `integer` | `500` | Milliseconds between requests (increase to 1000–2000 for large batches) | *** ### Data sources — four free APIs, merged with redundancy Every field is sourced from a layered set of **free, no-key-required** public endpoints, then merged with explicit per-field provenance. No single source is a point of failure: if one is down or has no match, the others still return a record. 1. **DuckDuckGo Instant Answer API** *(primary)* — Wikipedia/Wikidata-backed infoboxes: industry, employees, founded year, HQ, socials, company type, ISIN/ticker. 2. **Wikidata** *(independent fallback + corroboration)* — queried **directly** (entity search → entity claims → label resolution), not through DuckDuckGo. This means a DuckDuckGo outage or a change to its response shape does **not** break the lookup — Wikidata alone returns a full firmographic record. When DuckDuckGo *does* hit, Wikidata fills the gaps it left blank (e.g. exact HQ city, employee count, founding year) and corroborates the match. 3. **Wikipedia REST summary** *(description fallback)* — supplies a company description when neither DuckDuckGo nor Wikidata had one. 4. **Clearbit Autocomplete** — free name↔domain resolution. Confirms `Stripe → stripe.com` and resolves a canonical name from a bare domain. The `sources` and `field_sources` fields in every record tell you **exactly which API supplied each value** — full data lineage, so you can trust (or filter) field-by-field. *** ### Coverage | Company type | Coverage | |---|---| | Public companies (Fortune 500, NASDAQ/NYSE) | Excellent — full infobox + ISIN/ticker | | Well-known private tech (Stripe, Vercel, Notion, Airbnb) | Very good — employees, founded, socials | | Mid-tier B2B SaaS (Series B+, notable) | Good — description + domain; some fields may be missing | | Small / niche / local companies | Low — Clearbit domain only, `parse_confidence < 0.5` | | Unknown companies | Record with `parse_confidence < 0.3`, `ddg_no_hit` warning | *** ### Confidence scoring - `1.0` — full DDG hit with description, industry, employees, founded - `0.7–0.9` — good DDG hit, minor fields missing - `0.5–0.7` — DDG hit but sparse infobox - `0.3–0.5` — no DDG hit, Clearbit gave name/domain only - `< 0.3` — unrecognized company; query is echoed back Enable `skipLowConfidence: true` to drop sparse records automatically. *** ### Why this beats the alternatives The Apify Store company-enrichment field splits into two camps — and this actor sits in the gap between them. Real incumbents (researched 2026-06): `nexgendata/company-enrichment-tool` (from **$12/1k**), `andok/company-enrichment` (from **$10/1k**), `fortunate_favorite/company-intelligence`, `scrapepilot` Company Research Scraper. - **Rich-but-opaque enrichers** (`nexgendata`) return a deep record (`tech_stack`, `funding_stage`, `revenue_range`, `naics/sic`, …) but ship **no per-field provenance and no confidence score** — a value with no way to know where it came from or how far to trust it. - **Provenance-aware enrichers** (`andok`) ship an "evidence" array with a source URL per data point — so we **don't** claim per-field lineage is uniquely ours. But `andok` has **no parse-confidence / trust score**, uses website-HTML + VIES + SSL + Wayback (no Wikidata/Wikipedia firmographics), and bills from $10/1k. We deliberately **don't** chase `tech_stack` / `funding` / `revenue` — those need live per-company crawls. Our lane is the **fast, zero-cost firmographic baseline with full lineage and an honest trust score**: the layer you run *first* on a raw list before paying anyone for tech/funding depth. | Feature | This Actor | nexgendata | andok | Clearbit / Apollo API | |---|---|---|---|---| | Data source | DDG + **Wikidata (direct)** + Wikipedia + Clearbit | proprietary enrichment | website HTML + VIES + SSL + Wayback | paid vendor DB | | No API key required | **Yes** | Yes | Yes | No ($99–499/mo) | | Multi-source redundancy (any can fail) | **Yes — 4 sources** | No | Partial | Single | | Per-field data lineage (`field_sources`) | **Yes** | **No** | Yes (evidence URLs) | No | | `parse_confidence` on every record | **Yes** | **No** | **No** | No | | Lineage **and** trust score together | **Yes — unique** | No | No (lineage only) | No | | Failed lookups billed | **Never** | Sometimes | Sometimes | Sometimes | | Logo URL included | **Yes** (Clearbit CDN) | Varies | Yes | Yes (paid) | | Base cost / 1k | **$1.50** | $12 | $10 | $50–200+ | **The one-sentence edge:** the firmographics lookup that gives you per-field source **lineage** *and* a **trust score** on every record (the combination nobody else ships), survives any single source going down (4 redundant free APIs), and **never charges a failed lookup** — at **$1.50/1k** vs the $10–$12/1k incumbents. Full breakdown in [`COMPETITIVE_EDGE.md`](./COMPETITIVE_EDGE.md). *** ### FAQ **Can it look up private companies?** Yes, as long as they have a Wikipedia article. Many notable private tech companies are covered. Truly niche or local companies return sparse records with `parse_confidence < 0.5`. **Can I input URLs instead of domains?** Yes. `https://stripe.com/` and `www.stripe.com` are both automatically normalized to `stripe.com`. **Does it need proxies?** No. All four sources are free, unauthenticated APIs — no proxies, no keys needed. **What if a name is ambiguous (e.g. "Linear")?** The actor retries DuckDuckGo with suffixes ("Linear Inc", "Linear company"), and Wikidata's entity search rejects non-company matches (it filters out disambiguation noise like a pattern or a fictional character). The first organization-type hit is returned. **What happens if DuckDuckGo is down or changes its format?** The lookup continues. Wikidata is queried independently (not through DuckDuckGo) and can return a complete firmographic record on its own. This redundancy is the core design of the actor — see *Data sources* above. **How do I know which source a value came from?** Read the `field_sources` map on each record — it maps every populated field to the API that supplied it (`ddg`, `wikidata`, `wikipedia`, or `clearbit`). The `sources` array lists every API that contributed. *** ### Output sample A successful lookup for `"Stripe"` returns: ```json { "query": "Stripe", "name": "Stripe, Inc.", "domain": "stripe.com", "description": "American financial infrastructure platform for businesses", "industry": "Financial services, Payment processor", "size_range": "1001-5000", "employees": 4000, "founded": "2010", "hq_city": "San Francisco", "hq_country": "United States", "website": "https://stripe.com", "logo_url": "https://logo.clearbit.com/stripe.com", "twitter": "stripe", "linkedin": "stripe", "github": "stripe", "sources": ["ddg", "wikidata", "clearbit"], "parse_confidence": 0.95, "warnings": [] } ``` *** ### Use with AI agents (MCP) An agent calls this tool to **look up company firmographics mid-conversation** — e.g. "What industry is Stripe in?", "How many employees does Databricks have?", or "Get LinkedIn and Twitter for these 50 companies." Point your MCP client at this tool: ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=bovi/company-lookup-scraper", "--header", "Authorization: Bearer " ] } } } ``` Minimal agent input (name or domain lookup): ```json { "companies": ["Stripe", "Anthropic", "stripe.com"] } ``` No API key needed inside the tool — auth is your Apify token in the client config above. The `field_sources` map on every record tells the agent exactly which source supplied each value. ### Pricing **Pay-per-result (PPE):** | Event | Rate | Trigger | |---|---|---| | `company-result` (base) | **$1.50/1K** | Every company record pushed | | `social-enriched` (add-on) | **$0.50/1K** | Records where LinkedIn, Twitter, or GitHub is present | Social premium fires only when at least one social profile was found — failed or empty lookups are never billed. Total cost example: 1,000 records with 60% social hit rate = **$1.80** ($1.50 base + $0.30 social add-on). ### Integrations Built for sales-ops and growth teams enriching CRM exports with firmographic data by company name or domain — the JSON/dataset output drops into the tools you already run, no glue code: - **n8n / Make / Zapier** — trigger a run or pipe every new dataset item into 500+ apps (Google Sheets, Airtable, Slack, HubSpot, your database) with no code: [n8n](https://docs.apify.com/platform/integrations/n8n), [Make](https://docs.apify.com/platform/integrations/make), [Zapier](https://docs.apify.com/platform/integrations/zapier). - **Webhooks** — fire your own endpoint the moment a run finishes, to push results straight into your pipeline ([docs](https://docs.apify.com/platform/integrations/webhooks)). - **MCP server** — expose this actor as a tool to Claude, Cursor, or any [MCP client](https://mcp.apify.com) so an AI agent can pull this data mid-conversation ([guide](https://blog.apify.com/how-to-use-mcp/)). - **API & SDKs** — fetch the dataset as JSON, CSV, or Excel through the Apify REST API or the Python / JS SDKs. See all [Apify integrations](https://apify.com/integrations). ### Legal Data sourced from DuckDuckGo Instant Answers (Wikipedia/Wikidata) and Clearbit Autocomplete — both free, public, unauthenticated endpoints. Use responsibly. # Actor input Schema ## `companies` (type: `array`): List of company names (e.g. 'Stripe', 'Shopify') or domains (e.g. 'stripe.com', 'shopify.com') to look up. You may mix names and domains in the same list. ## `maxItems` (type: `integer`): Maximum total company records to output. 0 = no limit. Useful for cost control in large runs. Default: 0. ## `skipLowConfidence` (type: `boolean`): If true, skip results with parse\_confidence below 0.3 (companies where only a name/domain was found with no enrichment). If false, always output a record even if it is sparse. Default: false. ## `requestDelayMs` (type: `integer`): Milliseconds to wait between requests. Increase to 1000–2000 for very large batches to reduce rate-limiting risk. Default: 500. ## Actor input object example ```json { "companies": [ "Stripe", "Shopify", "vercel.com" ], "requestDelayMs": 500 } ``` # 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 = { "companies": [ "Stripe", "Shopify", "vercel.com" ], "maxItems": 0, "skipLowConfidence": false, "requestDelayMs": 500 }; // Run the Actor and wait for it to finish const run = await client.actor("bovi/company-lookup-scraper").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 = { "companies": [ "Stripe", "Shopify", "vercel.com", ], "maxItems": 0, "skipLowConfidence": False, "requestDelayMs": 500, } # Run the Actor and wait for it to finish run = client.actor("bovi/company-lookup-scraper").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 '{ "companies": [ "Stripe", "Shopify", "vercel.com" ], "maxItems": 0, "skipLowConfidence": false, "requestDelayMs": 500 }' | apify call bovi/company-lookup-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=bovi/company-lookup-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Company Lookup – B2B Firmographic Enrichment | $1.50/1K", "description": "**Enrich company names or domains** with firmographic data: industry, **employee count**, founded year, HQ location, website, logo, and **social profiles** (LinkedIn, Twitter, GitHub).", "version": "0.1", "x-build-id": "jREXAXHl77rKM1Blc" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/bovi~company-lookup-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-bovi-company-lookup-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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/bovi~company-lookup-scraper/runs": { "post": { "operationId": "runs-sync-bovi-company-lookup-scraper", "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/bovi~company-lookup-scraper/run-sync": { "post": { "operationId": "run-sync-bovi-company-lookup-scraper", "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": [ "companies" ], "properties": { "companies": { "title": "Company names or domains", "type": "array", "description": "List of company names (e.g. 'Stripe', 'Shopify') or domains (e.g. 'stripe.com', 'shopify.com') to look up. You may mix names and domains in the same list.", "items": { "type": "string" } }, "maxItems": { "title": "Max items", "minimum": 0, "type": "integer", "description": "Maximum total company records to output. 0 = no limit. Useful for cost control in large runs. Default: 0." }, "skipLowConfidence": { "title": "Skip low-confidence results", "type": "boolean", "description": "If true, skip results with parse_confidence below 0.3 (companies where only a name/domain was found with no enrichment). If false, always output a record even if it is sparse. Default: false." }, "requestDelayMs": { "title": "Request delay (ms)", "minimum": 0, "maximum": 10000, "type": "integer", "description": "Milliseconds to wait between requests. Increase to 1000–2000 for very large batches to reduce rate-limiting risk. Default: 500." } } }, "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 } } } } } } } } } } ```