# AI Prospect Research Agent (`listenllc/prospect-research-agent`) Actor Turn any company or person name into a detailed outreach brief. AI-powered research that finds what they care about, recent activity, pain points, and writes your first outreach message. - **URL**: https://apify.com/listenllc/prospect-research-agent.md - **Developed by:** [Wiz](https://apify.com/listenllc) (community) - **Categories:** AI, Lead generation - **Stats:** 1 total users, 0 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $250.00 / 1,000 prospect researcheds 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 ## AI Prospect Research Agent > Your next sales call shouldn't start with "So, tell me about your company." It should start with something that makes them think: "How did you know that?" This Actor turns a name and company into a full intelligence brief — background, recent moves, pain points, communication style, and a personalized outreach message ready to send. Stop Googling prospects for 20 minutes each. Let AI do the research in 10 seconds. --- ### What makes this different Most lead tools on Apify **scrape contact info** — emails, phone numbers, LinkedIn URLs. This one **thinks**. It doesn't just find your prospect. It tells you what they care about, what keeps them up at night, what they've been posting about, and exactly what to say to get a response. The difference between a cold email that gets deleted and one that gets a reply? **Research**. This Actor does the research. --- ### What you get per prospect - **Background** — career history, role, company overview - **Recent activity** — what they've been doing, saying, posting, launching - **Strategic priorities** — what they're focused on right now - **Pain points** — likely challenges and frustrations - **Communication style** — formal vs. casual, data-driven vs. narrative - **Outreach angles** — 2-5 specific approaches with reasoning for each - **Ice breakers** — 3 conversation starters that reference real details - **Ready-to-send message** — a personalized cold email or LinkedIn DM - **Confidence level** — how confident the AI is in its research - **Research gaps** — what it couldn't determine (so you know where to dig) --- ### Pricing **$0.25 per prospect researched.** No subscription. No monthly fee. | Use case | Prospects | Cost | |---|---|---| | Test it out | 1 prospect | $0.25 | | Prep for a sales meeting | 5 prospects | $1.25 | | Weekly outreach batch | 25 prospects | $6.25 | | Agency client campaign | 100 prospects | $25.00 | Compare: a human researcher spends 15-30 minutes per prospect. At $25/hour, that is $6-12 per prospect. This Actor does it for $0.25. --- ### 4 output styles | Style | Best for | What it emphasizes | |---|---|---| | **Outreach Brief** | Cold email, LinkedIn DMs | Conversation starters, personalization hooks | | **Sales Intelligence** | Discovery calls, demos | Budget signals, decision authority, pain points | | **Investor Memo** | Fundraising, pitch prep | Market position, growth signals, investment readiness | | **Partnership Pitch** | Strategic partnerships | Complementary strengths, mutual value, shared audiences | --- ### 3 research depths | Depth | Speed | Detail | Best for | |---|---|---|---| | **Quick** | ~5 sec | Key facts + 1 angle | High-volume outreach | | **Standard** | ~10 sec | Full brief + personalized message | Sales prep | | **Deep** | ~20 sec | Comprehensive dossier + multi-angle strategy | Enterprise deals, VIP prospects | --- ### How to use it #### Step 1: Add your prospects ```json [ {"name": "Sarah Chen", "company": "Stripe"}, {"name": "Marcus Johnson", "company": "HubSpot", "role": "VP of Marketing"}, {"name": "Lisa Park", "company": "Notion", "notes": "Met at SaaStr 2025"} ] ```` #### Step 2: Tell it about you In the "Your Context" field, describe what you do: *"We build AI-powered content tools for marketing teams"* — this lets the AI tailor outreach angles to your specific offering. #### Step 3: Pick your depth and style Quick for volume. Standard for most use cases. Deep for the deals that matter. #### Step 4: Get your briefs Each prospect returns a structured JSON object with everything you need to write a message that actually gets a response. *** ### Sample output ```json { "prospect_name": "Sarah Chen", "title_role": "Head of Developer Experience", "company": "Stripe", "company_overview": "Stripe is a financial infrastructure platform serving millions of businesses...", "background": "Sarah joined Stripe in 2022 after leading developer relations at...", "recent_activity": "Recently spoke at DevX Conf about reducing API integration time...", "strategic_priorities": "Expanding Stripe's developer ecosystem, reducing time-to-first-transaction...", "pain_points": "Developer onboarding friction, competing with emerging fintech APIs...", "communication_style": "Technical but accessible. Prefers data-backed arguments. Active on Twitter...", "outreach_angles": [ { "angle": "Reference her DevX Conf talk on integration speed", "reasoning": "Shows you follow her work and care about the same problems" }, { "angle": "Share a relevant case study about developer tool adoption", "reasoning": "Provides immediate value and positions you as a peer" } ], "ice_breakers": [ "Your DevX Conf talk on reducing integration time from days to hours resonated...", "I noticed Stripe just launched the new embedded components SDK...", "A mutual connection mentioned your team is rethinking onboarding flows..." ], "outreach_message": "Hi Sarah — your DevX Conf talk on cutting integration time stuck with me...", "confidence_level": "high", "research_gaps": "Could not determine current team size or specific Q2 priorities" } ``` *** ### Who this is for - **SDRs and BDRs** — stop sending generic "I noticed your company..." emails - **Founders doing their own outreach** — punch above your weight with Fortune 500 research - **Recruiting teams** — personalize candidate outreach that actually gets responses - **Agency account managers** — prep for client pitches with real intelligence - **BD and partnerships** — know exactly what to propose and why they'd care - **Investors** — research portfolio companies and potential deals *** ### Supported languages English, Spanish, Portuguese (Brazil), German, French, Italian, Dutch, Japanese, Korean. *** ### API key setup This Actor requires your own AI provider API key. Your key is encrypted and never stored. **Claude (Anthropic) — recommended for research quality:** 1. Go to [console.anthropic.com](https://console.anthropic.com/) 2. Create an account and add credits 3. Paste your API key in the input field **GPT (OpenAI):** 1. Go to [platform.openai.com](https://platform.openai.com/) 2. Create an account and add credits 3. Paste your API key in the input field *** ### FAQ **How much does each lookup cost in AI API fees?** Roughly $0.005-0.03 per prospect depending on depth and provider. A batch of 20 standard-depth lookups typically costs under $0.40 in API fees on top of the per-prospect charge. **How accurate is the research?** The AI uses its training knowledge, which covers public figures, executives, and well-known companies well. For lesser-known individuals, accuracy depends on their public footprint. Every brief includes a confidence level and research gaps field so you know what to verify. **Can I research anyone?** The AI works best with people who have a public professional presence — LinkedIn activity, conference talks, published articles, press coverage. For very private individuals with minimal online footprint, the research will be limited (and the confidence level will reflect that). **Can I run this on a schedule?** Yes. Set up an Apify schedule to research new prospects weekly. Combine with webhooks to send briefs to Slack, email, or your CRM automatically. **Can I upload a CSV of prospects?** Not directly in this version. Format your CSV data as a JSON array and paste it into the input field. CSV upload coming in a future update. *** ### Integrations Export your research briefs to: - **Google Sheets** — via Apify's native integration - **Zapier / Make / n8n** — trigger workflows when research is complete - **Slack** — get prospect briefs delivered to a channel before meetings - **HubSpot / Salesforce** — enrich CRM records with AI research - **CSV / JSON / Excel** — download directly from the dataset *** ### Related Actors - [AI Social Media Content Generator](https://apify.com/listenllc/social-content-generator) — generate a week of social posts in 60 seconds *** ### Changelog - **1.0** (2026-04-09) — Initial release: prospect research with 3 depths, 4 output styles, 9 languages, Claude + GPT support, personalized outreach messages *** Built by [Jungle Wiz](https://www.junglewiz.de) — AI-powered research and automation tools. # Actor input Schema ## `prospects` (type: `array`): List of prospects to research. Each item should include a name and optionally a company. Example: \[{"name": "Jane Smith", "company": "Acme Corp"}] ## `researchDepth` (type: `string`): How deep should the AI research each prospect? ## `outputStyle` (type: `string`): What format do you want the research delivered in? ## `includeOutreachMessage` (type: `boolean`): Generate a ready-to-send personalized cold email or LinkedIn message for each prospect. ## `outreachContext` (type: `string`): Briefly describe what you do and what you're reaching out about. This helps the AI write a relevant outreach message. Example: 'We build AI automation tools for marketing agencies' or 'I'm a fractional CMO looking for B2B SaaS clients' ## `language` (type: `string`): Language for the research output and outreach messages. ## `aiProvider` (type: `string`): Choose which AI model powers the research. ## `anthropicApiKey` (type: `string`): Your Anthropic API key. Get one at https://console.anthropic.com/ ## `openaiApiKey` (type: `string`): Your OpenAI API key. Get one at https://platform.openai.com/ ## Actor input object example ```json { "prospects": [ { "name": "Tim Cook", "company": "Apple" }, { "name": "Jensen Huang", "company": "NVIDIA" } ], "researchDepth": "standard", "outputStyle": "outreach_brief", "includeOutreachMessage": true, "outreachContext": "We help companies automate their content marketing with AI tools", "language": "en", "aiProvider": "anthropic" } ``` # 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 = { "prospects": [ { "name": "Tim Cook", "company": "Apple" }, { "name": "Jensen Huang", "company": "NVIDIA" } ], "researchDepth": "standard", "outputStyle": "outreach_brief", "outreachContext": "We help companies automate their content marketing with AI tools", "language": "en" }; // Run the Actor and wait for it to finish const run = await client.actor("listenllc/prospect-research-agent").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 = { "prospects": [ { "name": "Tim Cook", "company": "Apple", }, { "name": "Jensen Huang", "company": "NVIDIA", }, ], "researchDepth": "standard", "outputStyle": "outreach_brief", "outreachContext": "We help companies automate their content marketing with AI tools", "language": "en", } # Run the Actor and wait for it to finish run = client.actor("listenllc/prospect-research-agent").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 '{ "prospects": [ { "name": "Tim Cook", "company": "Apple" }, { "name": "Jensen Huang", "company": "NVIDIA" } ], "researchDepth": "standard", "outputStyle": "outreach_brief", "outreachContext": "We help companies automate their content marketing with AI tools", "language": "en" }' | apify call listenllc/prospect-research-agent --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=listenllc/prospect-research-agent", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "AI Prospect Research Agent", "description": "Turn any company or person name into a detailed outreach brief. AI-powered research that finds what they care about, recent activity, pain points, and writes your first outreach message.", "version": "1.0", "x-build-id": "cBvmLRoFQWokTxjgl" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/listenllc~prospect-research-agent/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-listenllc-prospect-research-agent", "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/listenllc~prospect-research-agent/runs": { "post": { "operationId": "runs-sync-listenllc-prospect-research-agent", "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/listenllc~prospect-research-agent/run-sync": { "post": { "operationId": "run-sync-listenllc-prospect-research-agent", "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": [ "prospects" ], "properties": { "prospects": { "title": "Prospects to Research", "type": "array", "description": "List of prospects to research. Each item should include a name and optionally a company. Example: [{\"name\": \"Jane Smith\", \"company\": \"Acme Corp\"}]" }, "researchDepth": { "title": "Research Depth", "enum": [ "quick", "standard", "deep" ], "type": "string", "description": "How deep should the AI research each prospect?", "default": "standard" }, "outputStyle": { "title": "Output Style", "enum": [ "outreach_brief", "sales_intel", "investor_memo", "partnership_pitch" ], "type": "string", "description": "What format do you want the research delivered in?", "default": "outreach_brief" }, "includeOutreachMessage": { "title": "Generate Outreach Message", "type": "boolean", "description": "Generate a ready-to-send personalized cold email or LinkedIn message for each prospect.", "default": true }, "outreachContext": { "title": "Your Context (for outreach)", "type": "string", "description": "Briefly describe what you do and what you're reaching out about. This helps the AI write a relevant outreach message. Example: 'We build AI automation tools for marketing agencies' or 'I'm a fractional CMO looking for B2B SaaS clients'", "default": "" }, "language": { "title": "Language", "enum": [ "en", "es", "pt-BR", "de", "fr", "it", "nl", "ja", "ko" ], "type": "string", "description": "Language for the research output and outreach messages.", "default": "en" }, "aiProvider": { "title": "AI Provider", "enum": [ "anthropic", "openai" ], "type": "string", "description": "Choose which AI model powers the research.", "default": "anthropic" }, "anthropicApiKey": { "title": "Anthropic API Key", "type": "string", "description": "Your Anthropic API key. Get one at https://console.anthropic.com/" }, "openaiApiKey": { "title": "OpenAI API Key", "type": "string", "description": "Your OpenAI API key. Get one at https://platform.openai.com/" } } }, "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 } } } } } } } } } } ```