# Company Deep Research ā€” AI Agent Dossier API (`logiover/company-deep-research-scraper`) Actor One-call company intelligence dossier for AI agents: website meta, tech stack, socials, contacts, SEO basics, news signals, competitors and blog/RSS from a company name or domain. No API key, no browser. - **URL**: https://apify.com/logiover/company-deep-research-scraper.md - **Developed by:** [Logiover](https://apify.com/logiover) (community) - **Categories:** Developer tools, Lead generation - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $15.00 / 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. Since this Actor supports Apify Store discounts, the price gets lower the higher subscription plan you have. 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 Deep Research Scraper ā€” AI Agent Company Intelligence API > One-call **company intelligence dossier** for AI agents, LLM apps, sales/rev-tech and market researchers. Drop in a company name or domain and get back a clean, structured JSON record with the website meta, tech stack, social profiles, contact channels, SEO basics, recent news signals, competitor domains and RSS/blog feeds ā€” all from a single Apify Actor run. No API key, no headless browser, no per-site scrapers to maintain. Built for the new wave of **AI agents that research companies autonomously** ā€” coding agents that need to understand a vendor before integrating, sales agents that qualify accounts, analyst agents that build market maps, and RAG pipelines that ground LLM answers in fresh, structured company data instead of stale training-set knowledge. --- ### šŸŽÆ What this Actor is for Large language models have a knowledge cutoff and no live web access. When an AI agent is asked *"research Acme Corp"* or *"who are Notion's competitors and what stack do they run?"*, it needs **structured, fresh, machine-readable company data** ā€” not a pile of raw HTML to re-parse every time. `company-deep-research-scraper` is that data layer: - **One call, one dossier.** Pass `stripe.com` and get back a single JSON object with ~40 fields covering identity, tech, socials, contacts, SEO, news and competitors. No need to chain five separate scrapers. - **AI-agent friendly schema.** Predictable field names, nullable values, arrays where multiple values exist, ISO timestamps. Drop straight into a prompt or a vector DB. - **Batch by default.** Feed 500 domains and get 500 dossiers back, enriched in parallel ā€” perfect for building a company database or enriching a CRM. - **Name ā†’ domain resolution.** Don't know the website? Pass `"Figma"` or `"Linear"` and the Actor resolves it to `figma.com` / `linear.app` via Clearbit's company autocomplete, then enriches. - **No keys, no browser.** Pure HTTP + lightweight HTML parsing on a small Node 20 container. Cheap, fast, and resilient ā€” no Playwright to keep warm. --- ### āœØ Key features - **šŸ§  Company identity** ā€” name, description, logo, favicon, OG metadata, site name, detected languages. - **šŸ› ļø Tech stack fingerprinting** ā€” 40+ technologies detected from HTML + HTTP headers: Next.js, React, Vue, Angular, Svelte, Nuxt, Gatsby, Astro, Remix, WordPress, Shopify, Webflow, Squarespace, Wix, Drupal, Joomla, Ghost, jQuery, Tailwind, Bootstrap, Cloudflare, Vercel, Netlify, Google Analytics, GTM, Facebook Pixel, Hotjar, HubSpot, Intercom, Segment, Mixpanel, Stripe, PayPal, Sentry, Datadog, Algolia, Elasticsearch, Font Awesome, Google Fonts, and more. - **šŸ”— Social profiles** ā€” LinkedIn, X/Twitter, GitHub, Facebook, Instagram, YouTube, TikTok, Discord, Telegram URLs extracted from the homepage + `schema.org` `sameAs`. - **āœ‰ļø Contact channels** ā€” emails (from `mailto:` + text regex, junk filtered) and phone numbers (from `tel:` + international regex). - **šŸ“ˆ SEO basics** ā€” title tag, meta description, H1/H2 headings, `robots.txt`, discovered sitemaps. - **šŸ“° News signals** ā€” recent news for the company (last 30 days) via Google News RSS, with title, URL, publish date and source. - **āš”ļø Competitor discovery** ā€” alternative/competitor domains mined from DuckDuckGo result pages (junk domains filtered). - **šŸ“” RSS / blog feeds** ā€” `` feed tags, blog links and common feed-path guesses (`/feed`, `/rss`, `/blog/feed`). - **šŸ¢ Organization JSON-LD** ā€” `schema.org` `Organization`/`Corporation` parsed for founded year, employee range, industry, country, city. - **šŸŒ Proxy-aware** ā€” Apify datacenter proxy by default (avoids per-IP rate limits), optional residential for bot-walled targets. - **šŸ’° Pay-per-result** ā€” you're charged per company dossier produced, not per run. Empty/blocked results are not billed. --- ### šŸ¤– Why AI agents need this Modern agentic workflows (Claude, ChatGPT, Cursor, LangGraph, CrewAI, AutoGen, Microsoft Copilot, Google Gemini) increasingly delegate research to tools. The bottleneck is no longer reasoning ā€” it's **grounding**: getting current, structured facts about real-world entities. Company research is one of the highest-frequency grounding tasks: 1. **Vendor / tool evaluation.** A coding agent is asked to pick a payment provider. It needs to know each candidate's website, tech stack, docs URL and recent news ā€” not just the name. 2. **Sales account research.** A GTM agent enriches a lead list with industry, employee size, tech stack, LinkedIn and a competitor set before drafting outreach. 3. **Market mapping.** An analyst agent builds a landscape of 200 companies in a niche and needs structured rows it can cluster, filter and rank. 4. **RAG grounding.** A support agent answers *"does Acme integrate with Stripe?"* by pulling Acme's tech stack dossier instead of guessing from training data. 5. **Due diligence.** A researcher agent checks a startup's footprint: founded year, location, social reach, news momentum, competitors. Each of these is one Actor call per company. Batch a list and you've got a database. --- ### šŸ“¦ What you get (output schema) Every run streams **one dossier per company** to the default dataset. A dossier looks like: ```json { "input": "stripe.com", "resolvedDomain": "stripe.com", "websiteUrl": "https://stripe.com/", "companyName": "Stripe", "title": "Stripe | Financial Infrastructure to Grow Your Revenue", "description": "Stripe is a suite of APIs powering online payment processing...", "logo": "https://stripe.com/img/v3/home/twitter.png", "favicon": "https://www.google.com/s2/favicons?domain=stripe.com&sz=64", "languages": ["en-US"], "ogType": "website", "techStack": ["Next.js", "React", "Cloudflare", "Stripe", "Google Analytics", "Segment", "Sentry", "Google Fonts"], "socials": [ { "platform": "twitter", "url": "https://twitter.com/stripe" }, { "platform": "linkedin", "url": "https://www.linkedin.com/company/stripe" }, { "platform": "github", "url": "https://github.com/stripe" } ], "emails": ["support@stripe.com"], "phones": [], "linkedinUrl": "https://www.linkedin.com/company/stripe", "twitterUrl": "https://twitter.com/stripe", "githubUrl": "https://github.com/stripe", "facebookUrl": null, "instagramUrl": null, "youtubeUrl": "https://www.youtube.com/stripe", "employeesRange": "5001-10000", "foundedYear": 2010, "industry": "Fintech", "country": "US", "city": "South San Francisco", "seoTitleTag": "Stripe | Financial Infrastructure to Grow Your Revenue", "seoMetaDescription": "Millions of businesses of all sizes...", "seoHeadings": ["Payments", "Online payments", "In-person payments"], "robotsTxt": "User-agent: *\nDisallow: /...", "sitemapUrls": ["https://stripe.com/sitemap.xml"], "news": [ { "title": "Stripe raises new round...", "url": "https://...", "publishedAt": "2026-06-...", "source": "TechCrunch" } ], "competitors": ["paypal.com", "adyen.com", "braintreepayments.com", "square.com"], "rssFeeds": ["https://stripe.com/blog/feed"], "blogUrl": "https://stripe.com/blog", "httpsValid": true, "scrapedAt": "2026-07-02T12:00:00.000Z" } ```` Use the **Overview** view to scan many companies, the **Social & contact** view for outreach lists, and the **News signals** view for monitoring. *** ### šŸš€ How to use #### 1. Enrich a batch of domains (highest volume) ```json { "mode": "domain", "domains": ["stripe.com", "linear.app", "figma.com", "notion.so", "vercel.com"], "sections": ["meta", "techStack", "socials", "contacts", "seo"], "concurrency": 5 } ``` #### 2. Resolve company names ā†’ websites ā†’ dossiers ```json { "mode": "name", "companyNames": ["Notion", "Figma", "Linear", "Vercel", "Supabase"], "sections": ["meta", "techStack", "socials", "contacts", "news", "competitors"], "concurrency": 4 } ``` #### 3. Deep single dossier (all sections) ```json { "mode": "single", "domain": "openai.com", "maxNews": 15, "maxCompetitors": 10 } ``` #### From code (Apify SDK) ```js import { ApifyClient } from 'apify-client'; const client = new ApifyClient({ token: process.env.APIFY_TOKEN }); const run = await client.actor('logiover/company-deep-research-scraper').call({ mode: 'domain', domains: ['stripe.com', 'linear.app'], sections: ['meta', 'techStack', 'socials', 'contacts'], }); const { items } = await client.dataset(run.defaultDatasetId).listItems(); console.log(items); // array of company dossiers ``` #### As an MCP tool for AI agents This Actor pairs naturally with an MCP server that wraps Apify. An agent calls the tool with a company name or domain and receives the structured dossier in its context ā€” no browsing, no HTML parsing on the agent side. *** ### šŸ”§ Input fields | Field | Type | Default | Description | |---|---|---|---| | `mode` | enum | `domain` | `domain` (batch enrich), `name` (resolve names ā†’ website ā†’ enrich), `single` (one deep dossier). | | `domains` | array | ā€” | Domains/URLs for `domain`/`single` modes. Normalized (protocol + `www` stripped). | | `domain` | string | ā€” | Single domain for `single` mode. | | `companyNames` | array | ā€” | Free-text company names for `name` mode (resolved via Clearbit autocomplete). | | `sections` | array | all | Which dossier sections to collect: `meta`, `techStack`, `socials`, `contacts`, `seo`, `news`, `competitors`, `rss`. Fewer = faster. | | `maxNews` | int | 10 | Max news items per company (0ā€“50). | | `maxCompetitors` | int | 10 | Max competitor domains per company (0ā€“30). | | `concurrency` | int | 5 | Parallel companies in batch modes (1ā€“20). | | `useApifyProxy` | bool | true | Route through Apify datacenter proxy. Recommended. | | `proxyGroups` | array | ā€” | Override proxy group, e.g. `["RESIDENTIAL"]` for bot-walled targets. | *** ### šŸ§© How it works 1. **Resolve.** In `name` mode, the company name is sent to Clearbit's keyless company autocomplete API, which returns the canonical domain (`"Notion"` ā†’ `notion.so`). 2. **Fetch homepage.** The website root is fetched over the Apify proxy with a browser-like User-Agent, following redirects. The final URL, HTTP headers and HTML body are captured. 3. **Parse meta.** ``, meta description, Open Graph / Twitter Card tags, `<html lang>`, `hreflang` alternates, favicon and apple-touch-icon are extracted from the `<head>`. 4. **Detect tech stack.** A library of 40+ regex fingerprints runs against the HTML, script `src` attributes, stylesheet `href`s and HTTP response headers (e.g. `cf-ray` ā†’ Cloudflare, `server: vercel` ā†’ Vercel, `wp-content` ā†’ WordPress). The `generator` meta tag is also checked. 5. **Extract socials.** Link `href`s and `schema.org` `sameAs` arrays are matched against LinkedIn, X/Twitter, GitHub, Facebook, Instagram, YouTube, TikTok, Discord and Telegram patterns. 6. **Extract contacts.** `mailto:` links and inline email regex (junk/image/sentry domains filtered), plus `tel:` links and international phone regex. 7. **SEO basics.** Title tag, meta description, H1/H2 text. The `/robots.txt` is fetched and parsed for `Sitemap:` directives. 8. **JSON-LD organization.** `<script type="application/ld+json">` blocks are parsed; `Organization`/`Corporation` blocks fill in name, founded year, employee range, industry, country, city and additional `sameAs` socials. 9. **News signals.** Google News RSS is queried with the company name (last 30 days, US locale) and parsed into title/url/date/source items. 10. **Competitors.** DuckDuckGo's HTML endpoint is queried with `"<company> alternatives competitors"`; result domains are extracted, junk (Wikipedia, YouTube, social) filtered, and the target's own domain excluded. 11. **RSS / blog.** `<link rel=alternate type=application/rss+xml>` tags, blog links and common feed paths (`/feed`, `/rss`, `/blog/feed`) are collected. 12. **Stream.** The complete dossier is pushed to the dataset and one `result` event is charged per company. *** ### šŸ’” Tips & best practices - **Batch for cost efficiency.** `domain` mode with 5ā€“10 concurrency is the sweet spot. A run of 100 companies typically completes in a few minutes. - **Trim sections for speed.** If you only need tech stack + socials, set `sections: ["meta","techStack","socials"]`. News and competitors add extra HTTP calls per company. - **Use `name` mode for messy lead lists.** Got a column of company names from a form? `name` mode cleans them into domains and enriches in one step. - **Residential proxy for tough targets.** If a site blocks datacenter IPs (rare for homepages, common for some SaaS), set `proxyGroups: ["RESIDENTIAL"]`. - **Pipe to a vector DB.** Serialize each dossier's description + tech stack + headings into a text chunk and embed it. Now your agent can semantically search companies. - **Schedule recurring runs.** News and competitors change weekly. Schedule a weekly run over your watchlist and diff the datasets to track movement. - **Combine with related Actors.** Pair with `website-contact-scraper`, `website-tech-stack-detector` and `linkedin-company-scraper` for deeper enrichment on a shortlist. *** ### ā“ FAQ #### Does this Actor need any API keys? No. It uses only keyless public endpoints (Clearbit company autocomplete, Google News RSS, DuckDuckGo HTML) and direct HTTP fetches of company websites. Just an Apify account. #### How accurate is the tech stack detection? The fingerprint library covers 40+ of the most common technologies. It detects client-side signals (script paths, framework markers) and server headers. It will miss server-only tech (databases, backend languages) and deeply bundled apps ā€” but for vendor reconnaissance and quick triage it's reliable. For exhaustive Wappalyzer-grade detection, pair with `website-tech-stack-detector`. #### Will it work on sites behind Cloudflare/login? Homepages rarely block. If a target returns 403/503, the dossier is still produced with the fields that could be collected and the domain recorded. For systematically walled targets, enable residential proxy. #### How many companies can I enrich per run? Practically unlimited ā€” the Actor streams results and charges per dossier. Concurrency is capped at 20 to be polite. A few hundred per run is comfortable; for thousands, split into batches. #### What's the difference between `domain` and `single` mode? `domain` is batch-oriented (many companies, lean sections). `single` is one company with all sections at full depth. The output schema is identical. #### Does it find personal emails of employees? No. It collects contact emails published on the company homepage (support, info, press). For employee/person emails use `linkedin-profile-scraper` + `youtube-creator-email-finder` style Actors. #### Can I get historical news? The news section covers the last 30 days via Google News RSS. For deeper history, combine with `google-news-scraper` or archive.org. #### How is this priced? Pay-per-result: you're charged one `result` event per company dossier produced. Runs that yield zero companies (bad input) are free. #### Is the output schema stable? Yes. Fields are additive ā€” new sections may add fields, but existing field names and types won't change within a major version. Nullable fields are marked. #### Can AI agents call this directly? Yes. Expose it through an MCP server or an Apify tool integration; the agent passes a company name/domain and gets structured JSON back. This is the primary design target. *** ### šŸ”— Related Actors - **website-tech-stack-detector** ā€” deeper, Wappalyzer-style tech detection on a single site. - **website-contact-scraper** ā€” emails, phones, socials, addresses from a whole site crawl. - **linkedin-company-scraper** ā€” LinkedIn company page (size, industry, specialties). - **clutch-co-scraper** / **goodfirms-scraper** ā€” B2B agency profiles & reviews. - **y-combinator-companies-directory-scraper** ā€” YC startup directory. - **bulk-social-profile-extractor** ā€” pull social profiles from a list of URLs. - **subdomain-finder** ā€” find subdomains for a company domain (recon). - **news-intelligence-scraper** ā€” topic-level multi-source news + sentiment. *** ### šŸ“ Changelog #### 2026-07-02 ā€” v1.0 - Initial release. - 3 modes: `domain` (batch), `name` (resolve ā†’ enrich), `single` (deep). - 8 dossier sections: meta, techStack, socials, contacts, seo, news, competitors, rss. - 40+ tech-stack fingerprints. - schema.org `Organization` JSON-LD parsing. - Apify datacenter proxy by default, residential opt-in. - Pay-per-result (`result` event per dossier). *** ### āš–ļø Disclaimer This Actor fetches publicly available web pages and keyless public APIs. It does not authenticate, bypass access controls, or scrape behind logins. Respect each website's Terms of Service and `robots.txt`. Use for research, sales intelligence and AI-agent grounding on data that is already public. # Actor input Schema ## `mode` (type: `string`): How to read the targets. ā€¢ **domain** ā€” enrich one or many domains/websites (highest volume: batch up to 500) ā€¢ **name** ā€” resolve a free-text company name to its website via DuckDuckGo + favicon lookup, then enrich ā€¢ **single** ā€” deep dossier for one domain (all sections, maximum depth) ## `domains` (type: `array`): Array of domains or URLs for **domain** mode, e.g. `stripe.com`, `https://openai.com`. Normalized automatically (protocol stripped, www stripped). ## `domain` (type: `string`): One domain or URL for the deep **single** dossier mode. ## `companyNames` (type: `array`): Array of free-text company names for **name** mode, e.g. `Notion`, `Figma`. The actor resolves each to a website first. ## `sections` (type: `array`): Which dossier sections to collect. Each adds HTTP calls. Defaults to all. Uncheck to speed up batch runs. ## `maxNews` (type: `integer`): Cap on news results per company (news section). ## `maxCompetitors` (type: `integer`): Cap on competitor domains returned (competitors section). ## `concurrency` (type: `integer`): Parallel companies in domain/name modes. Lower for fragile targets. ## `useApifyProxy` (type: `boolean`): Route requests through Apify datacenter proxy (AUTO group). Recommended ā€” avoids per-IP rate limits. Turn off only for public APIs that geo-block proxies. ## `proxyGroups` (type: `array`): Override proxy group, e.g. `RESIDENTIAL` for bot-walled targets. Leave empty for datacenter (AUTO). ## Actor input object example ```json { "mode": "domain", "domains": [ "stripe.com" ], "domain": "linear.app", "companyNames": [ "Notion" ], "sections": [ "meta", "techStack", "socials", "contacts", "seo" ], "maxNews": 5, "maxCompetitors": 5, "concurrency": 3, "useApifyProxy": true, "proxyGroups": [] } ``` # Actor output Schema ## `results` (type: `string`): Full dataset of company intelligence records. One row = one company dossier. # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '<YOUR_API_TOKEN>' with your token const client = new ApifyClient({ token: '<YOUR_API_TOKEN>', }); // Prepare Actor input const input = { "mode": "domain", "domains": [ "stripe.com" ], "domain": "linear.app", "companyNames": [ "Notion" ], "sections": [ "meta", "techStack", "socials", "contacts", "seo" ], "maxNews": 5, "maxCompetitors": 5, "concurrency": 3, "useApifyProxy": true }; // Run the Actor and wait for it to finish const run = await client.actor("logiover/company-deep-research-scraper").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`šŸ’¾ 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); }); // šŸ“š Want to learn more šŸ“–? Go to ā†’ https://docs.apify.com/api/client/js/docs ``` ## Python example ```python from apify_client import ApifyClient # Initialize the ApifyClient with your Apify API token # Replace '<YOUR_API_TOKEN>' with your token. client = ApifyClient("<YOUR_API_TOKEN>") # Prepare the Actor input run_input = { "mode": "domain", "domains": ["stripe.com"], "domain": "linear.app", "companyNames": ["Notion"], "sections": [ "meta", "techStack", "socials", "contacts", "seo", ], "maxNews": 5, "maxCompetitors": 5, "concurrency": 3, "useApifyProxy": True, } # Run the Actor and wait for it to finish run = client.actor("logiover/company-deep-research-scraper").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("šŸ’¾ Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # šŸ“š Want to learn more šŸ“–? Go to ā†’ https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "mode": "domain", "domains": [ "stripe.com" ], "domain": "linear.app", "companyNames": [ "Notion" ], "sections": [ "meta", "techStack", "socials", "contacts", "seo" ], "maxNews": 5, "maxCompetitors": 5, "concurrency": 3, "useApifyProxy": true }' | apify call logiover/company-deep-research-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=logiover/company-deep-research-scraper", "--header", "Authorization: Bearer <YOUR_API_TOKEN>" ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Company Deep Research ā€” AI Agent Dossier API", "description": "One-call company intelligence dossier for AI agents: website meta, tech stack, socials, contacts, SEO basics, news signals, competitors and blog/RSS from a company name or domain. No API key, no browser.", "version": "1.0", "x-build-id": "diDRmnWQmgQaIkAds" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/logiover~company-deep-research-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-logiover-company-deep-research-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/logiover~company-deep-research-scraper/runs": { "post": { "operationId": "runs-sync-logiover-company-deep-research-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/logiover~company-deep-research-scraper/run-sync": { "post": { "operationId": "run-sync-logiover-company-deep-research-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "mode" ], "properties": { "mode": { "title": "Mode", "enum": [ "domain", "name", "single" ], "type": "string", "description": "How to read the targets.\n\nā€¢ **domain** ā€” enrich one or many domains/websites (highest volume: batch up to 500)\nā€¢ **name** ā€” resolve a free-text company name to its website via DuckDuckGo + favicon lookup, then enrich\nā€¢ **single** ā€” deep dossier for one domain (all sections, maximum depth)", "default": "domain" }, "domains": { "title": "Domains / websites (batch)", "type": "array", "description": "Array of domains or URLs for **domain** mode, e.g. `stripe.com`, `https://openai.com`. Normalized automatically (protocol stripped, www stripped).", "items": { "type": "string" }, "default": [] }, "domain": { "title": "Single domain (single mode)", "type": "string", "description": "One domain or URL for the deep **single** dossier mode." }, "companyNames": { "title": "Company names (name mode)", "type": "array", "description": "Array of free-text company names for **name** mode, e.g. `Notion`, `Figma`. The actor resolves each to a website first.", "items": { "type": "string" }, "default": [] }, "sections": { "title": "Intelligence sections", "type": "array", "description": "Which dossier sections to collect. Each adds HTTP calls. Defaults to all. 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