# B2B Research Brief Generator (`digital-ai-lab.de/b2b-research-brief-generator`) Actor Generate ready-to-use B2B research briefs from company website URLs for sales calls, account research, and lightweight due diligence. - **URL**: https://apify.com/digital-ai-lab.de/b2b-research-brief-generator.md - **Developed by:** [Digital AI Lab](https://apify.com/digital-ai-lab.de) (community) - **Categories:** AI, Agents, Lead generation - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing $50.00 / 1,000 research briefs 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 ## B2B Research Brief Generator Generate ready-to-use B2B research briefs from company website URLs. This Actor is built for SDR teams, founders, consultants, RevOps teams, agencies, and AI-agent builders who need fast account research before sales calls, outbound campaigns, or CRM enrichment. It crawls a company homepage plus high-signal same-domain pages such as pricing, about, customers, solutions, careers, and contact pages. It returns one structured dataset item per company with a concise executive summary, business model, target-customer signals, sales triggers, talking points, questions to ask, suggested next steps, optional outreach email drafts, and a Markdown brief. Typical workflow: run 10-100 company URLs, export the dataset to a CRM or Google Sheet, and use each Markdown brief as sales-call preparation or account research. ### Best use cases - Prepare for sales calls and account research. - Create lightweight company briefs before outbound campaigns. - Enrich CRM records with public website signals. - Build market-research datasets from company URLs. - Generate briefing material for consultants, founders, agencies, and RevOps teams. - Feed Markdown briefs into n8n, Make, Zapier, Airtable, Notion, Google Sheets, or AI agents. ### Input ```json { "startUrls": [ "https://www.apify.com", "https://example.com" ], "briefType": "sales-call", "audience": "sales", "maxPagesPerSite": 3, "includeEmailDrafts": true, "analysisMode": "heuristic", "openAiModel": "gpt-4.1-mini", "includeRawSignals": false } ```` ### Brief types - `sales-call`: sales talking points, outreach angle, and practical questions. - `market-research`: company positioning, signals, caveats, and next research steps. - `vendor-review`: buyer-oriented trust, risk, and verification questions. - `crm-enrichment`: structured fields and follow-up signals for workflow automation. ### Output Each result is pushed to the default dataset: ```json { "url": "https://example.com/", "status": "ok", "briefType": "sales-call", "audience": "sales", "companyName": "Example AI Platform", "executiveSummary": "Example AI Platform appears to be a software / saas company serving B2B companies...", "businessModel": "Software / SaaS", "targetCustomers": ["B2B companies", "Software teams"], "salesTriggers": ["Public pricing page found", "Hiring signal detected"], "riskFlags": ["No public email found"], "recommendedOutreachAngle": "Lead with measurable conversion, pricing-page clarity, or competitor positioning improvements.", "talkingPoints": ["Example AI Platform's public positioning suggests software / saas."], "questionsToAsk": ["Which business process is currently most expensive or manual for your team?"], "suggestedNextSteps": ["Use the strongest signal as the opening context for outreach."], "emailDrafts": [ { "label": "Short outbound", "subject": "Example AI Platform - quick idea", "body": "Hi,\n\nI was looking at Example AI Platform's website..." } ], "briefMarkdown": "## Example AI Platform - B2B Research Brief\n\n...", "confidence": 0.71, "pagesAnalyzed": ["https://example.com/", "https://example.com/pricing"], "contacts": { "emails": [], "socialLinks": [] } } ``` ### Analysis modes #### Heuristic Cheapest mode. Uses deterministic extraction, keyword signals, page-type detection, and brief templates. This is suitable for high-volume account research and predictable per-result cost. #### AI assisted Uses an OpenAI-compatible chat completion call when `OPENAI_API_KEY` is configured as an Actor secret. If the key is missing or the API returns an error, the Actor falls back to the heuristic result and includes an `aiError` field when relevant. ### Pricing This Actor is designed for pay-per-event pricing. One charged event equals one generated B2B research brief written to the default dataset. The primary event is `Research brief`. ### Tips for better results - Use canonical company homepage URLs. - Set `maxPagesPerSite` to 3-5 for fast sales research. - Increase `maxPagesPerSite` when researching larger enterprise websites. - Use `sales-call` for account prep and outbound workflows. - Use `crm-enrichment` when feeding results into a CRM, spreadsheet, or automation. - Use AI assisted mode only when you have configured an API key and want more nuanced summaries. - Works well with Google Sheets, Airtable, n8n, Make, Zapier, CRMs, and AI-agent workflows. ### Search keywords B2B research brief generator, sales call prep, account research automation, CRM enrichment from website URLs, company intelligence API, outbound research workflow. ### Limitations - Some websites block automated fetches and may return `403` or `429`. - JavaScript-heavy websites may expose limited text without browser rendering. - Email drafts are starting points and should be reviewed before sending. - AI assisted mode adds LLM cost and needs an API key. - This Actor should not be used for spam. It is designed for legitimate B2B research workflows. # Actor input Schema ## `startUrls` (type: `array`): One or more company websites to research. Use canonical homepage URLs when possible. ## `briefType` (type: `string`): Choose the kind of brief to generate. ## `audience` (type: `string`): The reader this brief should be optimized for. ## `maxPagesPerSite` (type: `integer`): Maximum number of same-domain pages to fetch for each website. ## `includeEmailDrafts` (type: `boolean`): Generate short, ready-to-edit outreach email drafts for each company. ## `analysisMode` (type: `string`): Heuristic is cheapest and deterministic. AI assisted uses an OpenAI-compatible API when OPENAI\_API\_KEY is configured as an Actor secret. ## `openAiModel` (type: `string`): Model used only in AI assisted mode. ## `includeRawSignals` (type: `boolean`): Include extracted titles, emails, social links, keywords, and sampled page text in each dataset item. ## Actor input object example ```json { "startUrls": [ "https://www.apify.com" ], "briefType": "sales-call", "audience": "sales", "maxPagesPerSite": 3, "includeEmailDrafts": true, "analysisMode": "heuristic", "openAiModel": "gpt-4.1-mini", "includeRawSignals": false } ``` # Actor output Schema ## `results` (type: `string`): No description # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "startUrls": [ "https://www.apify.com" ] }; // Run the Actor and wait for it to finish const run = await client.actor("digital-ai-lab.de/b2b-research-brief-generator").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 = { "startUrls": ["https://www.apify.com"] } # Run the Actor and wait for it to finish run = client.actor("digital-ai-lab.de/b2b-research-brief-generator").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 '{ "startUrls": [ "https://www.apify.com" ] }' | apify call digital-ai-lab.de/b2b-research-brief-generator --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=digital-ai-lab.de/b2b-research-brief-generator", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "B2B Research Brief Generator", "description": "Generate ready-to-use B2B research briefs from company website URLs for sales calls, account research, and lightweight due diligence.", "version": "0.1", "x-build-id": "zrJ1yxgef6FeL6cjC" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/digital-ai-lab.de~b2b-research-brief-generator/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-digital-ai-lab.de-b2b-research-brief-generator", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/digital-ai-lab.de~b2b-research-brief-generator/runs": { "post": { "operationId": "runs-sync-digital-ai-lab.de-b2b-research-brief-generator", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/digital-ai-lab.de~b2b-research-brief-generator/run-sync": { "post": { "operationId": "run-sync-digital-ai-lab.de-b2b-research-brief-generator", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "startUrls" ], "properties": { "startUrls": { "title": "Company website URLs", "minItems": 1, "maxItems": 50, "type": "array", "description": "One or more company websites to research. Use canonical homepage URLs when possible.", "items": { "type": "string" } }, "briefType": { "title": "Brief type", "enum": [ "sales-call", "market-research", "vendor-review", "crm-enrichment" ], "type": "string", "description": "Choose the kind of brief to generate.", "default": "sales-call" }, "audience": { "title": "Audience", "enum": [ "sales", "founder", "researcher", "revops", "investor" ], "type": "string", "description": "The reader this brief should be optimized for.", "default": "sales" }, "maxPagesPerSite": { "title": "Max pages per site", "minimum": 1, "maximum": 20, "type": "integer", "description": "Maximum number of same-domain pages to fetch for each website.", "default": 3 }, "includeEmailDrafts": { "title": "Include email drafts", "type": "boolean", "description": "Generate short, ready-to-edit outreach email drafts for each company.", "default": true }, "analysisMode": { "title": "Analysis mode", "enum": [ "heuristic", "ai-assisted" ], "type": "string", "description": "Heuristic is cheapest and deterministic. AI assisted uses an OpenAI-compatible API when OPENAI_API_KEY is configured as an Actor secret.", "default": "heuristic" }, "openAiModel": { "title": "OpenAI model", "type": "string", "description": "Model used only in AI assisted mode.", "default": "gpt-4.1-mini" }, "includeRawSignals": { "title": "Include raw signals", "type": "boolean", "description": "Include extracted titles, emails, social links, keywords, and sampled page text in each dataset item.", "default": false } } }, "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 } } } } } } } } } } ```