# Italian Companies Data Scraper (`ayrtondavoli97/italy-companies-scraper`) Actor Extract Italian company data by category from aziende.it. Get company name, revenue range, ATECO code, location and optional VAT, address, postal code and employees. Ideal for market research, CRM prep, BI datasets and B2B analysis. - **URL**: https://apify.com/ayrtondavoli97/italy-companies-scraper.md - **Developed by:** [Francesco Ayrton Davoli](https://apify.com/ayrtondavoli97) (community) - **Categories:** AI, Other, Jobs - **Stats:** 4 total users, 2 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $1.00 / 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/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 ## Italian Company Data Scraper Extract structured Italian company data from aziende.it by business category. This Actor is built for users who need clean Italian business datasets for market research, B2B research, CRM preparation, territory mapping, competitive intelligence, SEO analysis, supplier discovery, investment scouting, and BI or AI data workflows. The default mode is fast and lightweight. It collects company listing data such as company name, revenue range, ATECO code, province, city and source detail URL. An optional details mode opens each company page and enriches the dataset with VAT number, address, postal code and employee range when available. Because the details mode fetches one extra page per company, it takes noticeably more time and platform resources than listing mode — see **Optional full-detail mode** below before enabling it. ### What you can scrape Validated categories currently supported: - `informatica` - `software` - `consulenza` - `marketing` - `edilizia` - `immobiliare` - `turismo` - `trasporti` Categories such as food, textile, restaurants and mechanics were tested but are not exposed as validated presets yet because the tested source URLs returned no compatible rows. You can still use **Advanced category URLs** if you have a specific aziende.it category URL you want to scrape. ### Main use cases - Build Italian company datasets by business sector - Map companies by city, province, ATECO code or revenue range - Prepare CRM imports before email/contact enrichment - Identify companies in a target industry or region - Analyze market density and regional business distribution - Build lead research datasets without claiming direct email coverage - Feed dashboards, spreadsheets, BI tools, data warehouses or AI workflows - Research suppliers, competitors, agencies, transport companies, real estate firms or construction businesses ### Fast listing mode By default, **Include company details** is disabled. This mode is much faster because it only reads category listing pages. Listing fields include: - Company name - Requested category - Revenue range - Parsed revenue minimum in EUR - Parsed revenue maximum in EUR - ATECO code - Province - City - Source category label - Company detail page URL - Source category URL Use this mode when you need thousands of records quickly. ### Optional full-detail mode Enable **Include company details** when you need richer registry-style fields. Full-detail enrichment can add: - VAT number - Address - Postal code - Employee range - Tax code, when available - REA registry number, when available - Legal form, when available - Activity status, when available - Foundation date, when available Full-detail mode is slower and more resource-intensive than listing mode, because **every company requires its own additional detail-page request**. In practice this means: - **More time:** runs take much longer — plan to raise the run timeout (see below). - **More compute:** longer runs consume more Apify compute units (and therefore more platform usage/cost). - **More proxy traffic:** one extra request per company increases proxy bandwidth usage. Based on current tests, about **100 companies with details are pushed to the dataset in roughly 1 minute**. The exact speed depends on source response time, proxy performance and Apify runtime conditions. As a rule of thumb: listing-only is the right choice for large datasets, and details mode is worth its extra cost only when you specifically need VAT number, address, postal code or employee range. For full-detail runs, increase the Actor timeout in **Run options**: - 500 companies with details: use at least **900 seconds** - 1,000 companies with details: use at least **1,800 seconds** - 5,000 companies with details: use around **7,200 seconds** For larger full-detail runs, increasing memory from 1 GB to 2 GB can be useful. ### Balanced multi-category runs Use **Maximum results per category** when scraping multiple categories and you want a balanced dataset. Example: if you select `software`, `marketing` and `edilizia`, and set: ```json "maxItemsPerCategory": 1000 ```` then the Actor will try to collect up to 1,000 companies per requested category, while still respecting the overall **Maximum results** limit. Set **Maximum results per category** to `0` to disable category-level balancing and use only the global **Maximum results** limit. ### Input fields #### Business category Main business category to scrape. Validated values: - `informatica` - `software` - `consulenza` - `marketing` - `edilizia` - `immobiliare` - `turismo` - `trasporti` #### Additional categories Optional extra categories to merge into the same dataset. Example: ```json ["software", "consulenza", "marketing"] ``` #### Maximum results per category Optional category-level limit. Use it when scraping more than one category and you want balanced results. Examples: - `0`: disabled - `100`: up to 100 companies per category - `1000`: up to 1,000 companies per category #### Maximum results Overall maximum number of companies to save. This always acts as a global safety cap. #### Include company details Optional checkbox. - Disabled: fast listing-only dataset - Enabled: slower full-detail dataset with VAT number, address, postal code and employee range when available #### Try website contact enrichment Advanced optional setting. If a real company website is found, the Actor can try to inspect it for public contact data. This is slower and often returns few results because aziende.it rarely exposes company websites. #### Scrape all validated categories Runs all validated preset categories. Recommended only when you want a broad dataset. Use it with **Maximum results per category** to avoid one large category dominating the output. #### Advanced category URLs Optional direct aziende.it category URLs for users who want to bypass the preset category mapping. ### Example: fast default run ```json { "category": "informatica", "categories": ["software"], "maxItemsPerCategory": 0, "maxItems": 5000, "includeDetails": false, "includeWebsiteContacts": false, "proxyConfig": { "useApifyProxy": true } } ``` ### Example: balanced multi-category run ```json { "category": "software", "categories": ["marketing", "edilizia", "immobiliare"], "maxItemsPerCategory": 1000, "maxItems": 4000, "includeDetails": false, "includeWebsiteContacts": false, "proxyConfig": { "useApifyProxy": true } } ``` ### Example: full-detail run ```json { "category": "software", "categories": ["consulenza"], "maxItemsPerCategory": 250, "maxItems": 500, "includeDetails": true, "includeWebsiteContacts": false, "proxyConfig": { "useApifyProxy": true } } ``` For this full-detail example, set the run timeout to at least 900 seconds. ### Output schema The dataset table uses English labels for international buyers. Raw field keys are source-compatible and may use Italian names internally. | Dataset label | Raw field | Mode | Description | |---|---|---|---| | Company name | `ragioneSociale` | Listing | Legal/company name | | Requested category | `categoryKey` | Listing | User-selected/preset category key | | Revenue range | `fatturato` | Listing | Revenue range from the source | | Revenue min EUR | `fatturatoMinEur` | Listing | Parsed lower revenue estimate | | Revenue max EUR | `fatturatoMaxEur` | Listing | Parsed upper revenue estimate | | ATECO code | `ateco` | Listing | Italian business activity code | | Province | `provincia` | Listing | Italian province | | City | `citta` | Listing | City | | Source category label | `categoria` | Listing | Category label shown by the source | | Company detail page | `detailUrl` | Listing | Company detail page URL | | VAT number | `partitaIva` | Details | Italian VAT number, when detail mode is enabled | | Address | `indirizzo` | Details | Company address, when detail mode is enabled | | Postal code | `cap` | Details | Italian postal code, when detail mode is enabled | | Employees | `dipendenti` | Details | Employee range, when detail mode is enabled | | Tax code | `codiceFiscale` | Details | Tax code, when available | | REA | `rea` | Details | REA registry number, when available | | Phone | `telefono` | Rare | Phone, rarely available from the source | | Email | `email` | Rare | Email, rarely available from the source | | PEC | `pec` | Rare | Certified email, rarely available from the source | | Website | `sitoWeb` | Rare | Website, rarely available from the source | | Legal form | `formaGiuridica` | Details | Legal form, when available | | Activity status | `statoAttivita` | Details | Activity status, when available | | Foundation date | `dataCostituzione` | Details | Foundation/incorporation date, when available | | Source category URL | `sourceCategoryUrl` | Listing | aziende.it category URL used as source | | Details included | `detailScraped` | System | Boolean flag indicating whether detail enrichment was performed | ### Important limitation about contacts This Actor should be positioned as an **Italian company registry dataset scraper**, not as an email scraper. aziende.it rarely exposes real email, PEC, phone or website fields on company detail pages. These fields are included in the output schema because they may appear in some cases, but they are often null. For email, PEC, phone or website discovery at scale, use a separate contact-enrichment workflow after exporting this dataset. ### Performance guidance Listing-only mode is the recommended default for large datasets. Full-detail mode is useful when VAT number, address, postal code and employee range are required, but it is slower because every company requires an additional detail-page request. Current tested speed for full-detail enrichment is roughly **100 enriched companies per minute pushed to the dataset**. For large full-detail runs, always increase timeout and monitor retries. ### Recommended positioning Recommended product positioning: > Extract structured Italian company data by validated business category, including company name, revenue range, ATECO code, location and optional VAT/address/employee enrichment. Avoid positioning this Actor as: > Italian business email scraper The source is strong for company registry-style fields, not for contact emails. # Actor input Schema ## `category` (type: `string`): Choose the main Italian business category to scrape. Validated categories: informatica, software, consulenza, marketing, edilizia, immobiliare, turismo, trasporti. ## `categories` (type: `array`): Optional extra validated categories to merge into the same dataset. Example: software, consulenza, marketing. Leave empty for a single-category run. ## `maxItemsPerCategory` (type: `integer`): Optional category-level limit. Use it when scraping multiple categories and you want a balanced dataset, for example 1000 companies per category. Set to 0 to use only the overall Maximum results limit. ## `maxItems` (type: `integer`): Maximum number of companies to save overall. Listing-only runs are fast. Full-detail runs are slower because each company detail page must be opened. ## `includeDetails` (type: `boolean`): Enable this to enrich each company with VAT number, address, postal code and employee range when available. Detail enrichment is slower: based on current tests, about 100 companies with details are pushed to the dataset in roughly 1 minute. For large full-detail runs, increase the Actor run timeout and memory in Run options. ## `includeWebsiteContacts` (type: `boolean`): Optional advanced enrichment. If a real company website is found, the Actor can try to inspect it for public emails, PEC and phone numbers. This slows runs down and often returns few results because aziende.it rarely exposes company websites. ## `allCategories` (type: `boolean`): When enabled, the Actor runs all validated preset categories. Recommended with Maximum results per category to keep the dataset balanced. ## `startUrls` (type: `array`): Optional. Add specific aziende.it category URLs if you want to bypass the simple category presets. ## `proxyConfig` (type: `object`): Apify proxy configuration. ## Actor input object example ```json { "category": "informatica", "categories": [], "maxItemsPerCategory": 0, "maxItems": 5000, "includeDetails": false, "includeWebsiteContacts": false, "allCategories": false, "startUrls": [], "proxyConfig": { "useApifyProxy": true } } ``` # Actor output Schema ## `companies` (type: `string`): Extracted Italian company records. # 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("ayrtondavoli97/italy-companies-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 = {} # Run the Actor and wait for it to finish run = client.actor("ayrtondavoli97/italy-companies-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 '{}' | apify call ayrtondavoli97/italy-companies-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=ayrtondavoli97/italy-companies-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Italian Companies Data Scraper", "description": "Extract Italian company data by category from aziende.it. Get company name, revenue range, ATECO code, location and optional VAT, address, postal code and employees. Ideal for market research, CRM prep, BI datasets and B2B analysis.", "version": "0.0", "x-build-id": "Use8gYKXhbtitbzbb" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/ayrtondavoli97~italy-companies-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-ayrtondavoli97-italy-companies-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/ayrtondavoli97~italy-companies-scraper/runs": { "post": { "operationId": "runs-sync-ayrtondavoli97-italy-companies-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/ayrtondavoli97~italy-companies-scraper/run-sync": { "post": { "operationId": "run-sync-ayrtondavoli97-italy-companies-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "category", "maxItems" ], "properties": { "category": { "title": "Business category", "type": "string", "description": "Choose the main Italian business category to scrape. Validated categories: informatica, software, consulenza, marketing, edilizia, immobiliare, turismo, trasporti.", "default": "informatica" }, "categories": { "title": "Additional categories", "type": "array", "description": "Optional extra validated categories to merge into the same dataset. Example: software, consulenza, marketing. Leave empty for a single-category run.", "default": [], "items": { "type": "string" } }, "maxItemsPerCategory": { "title": "Maximum results per category", "minimum": 0, "maximum": 100000, "type": "integer", "description": "Optional category-level limit. Use it when scraping multiple categories and you want a balanced dataset, for example 1000 companies per category. Set to 0 to use only the overall Maximum results limit.", "default": 0 }, "maxItems": { "title": "Maximum results", "minimum": 1, "maximum": 500000, "type": "integer", "description": "Maximum number of companies to save overall. Listing-only runs are fast. Full-detail runs are slower because each company detail page must be opened.", "default": 5000 }, "includeDetails": { "title": "Include company details", "type": "boolean", "description": "Enable this to enrich each company with VAT number, address, postal code and employee range when available. Detail enrichment is slower: based on current tests, about 100 companies with details are pushed to the dataset in roughly 1 minute. For large full-detail runs, increase the Actor run timeout and memory in Run options.", "default": false }, "includeWebsiteContacts": { "title": "Try website contact enrichment", "type": "boolean", "description": "Optional advanced enrichment. If a real company website is found, the Actor can try to inspect it for public emails, PEC and phone numbers. This slows runs down and often returns few results because aziende.it rarely exposes company websites.", "default": false }, "allCategories": { "title": "Scrape all validated categories", "type": "boolean", "description": "When enabled, the Actor runs all validated preset categories. Recommended with Maximum results per category to keep the dataset balanced.", "default": false }, "startUrls": { "title": "Advanced category URLs", "type": "array", "description": "Optional. Add specific aziende.it category URLs if you want to bypass the simple category presets.", "default": [], "items": { "type": "string" } }, "proxyConfig": { "title": "Proxy", "type": "object", "description": "Apify proxy configuration.", "default": { "useApifyProxy": true } } } }, "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 } } } } } } } } } } ```