# Hero Customer Discovery v2 - Customer Proof Candidates (`shashghosh/hero-customer-discovery-v2`) Actor Discover public customer-proof pages, case-study candidates, testimonials, and transformation-story evidence from a company website. - **URL**: https://apify.com/shashghosh/hero-customer-discovery-v2.md - **Developed by:** [Shashwat Ghosh](https://apify.com/shashghosh) (community) - **Categories:** AI, Lead generation, Automation - **Stats:** 2 total users, 2 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $3.00 / 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 ## Hero Customer Discovery v2 — Find Customer Proof & Case Study Candidates Hero Customer Discovery v2 finds public customer-proof signals that can become case studies, testimonials, sales proof, or customer-story candidates. ### What it does Give it a company name and website. It looks for customer pages, case-study pages, testimonial pages, proof assets, and fallback public evidence. It returns structured rows with candidate names, evidence URLs, signal type, confidence, and recommended next action. ### Best for - Customer marketing teams looking for case-study candidates - Founders and GTM teams trying to find proof assets - Agencies preparing customer-story or proof-gap audits - RevOps and demand-gen teams building customer proof datasets ### Example input ```json { "companyName": "Freshworks", "companyWebsite": "https://www.freshworks.com", "targetRegion": "India", "maxCandidates": 5, "includeFallbackSearch": true, "maxPages": 3 } ```` ### Output Each row is a customer-proof candidate with fields such as `recordType`, `companyName`, `title`, `url`, `snippet`, `heroScore`, `confidence`, `sourceType`, `status`, and `recommendation`. ### Recommended workflow 1. Run this Actor on your company or a target account. 2. Review high-confidence rows. 3. Use Hero Content Analyzer to inspect proof gaps. 4. Use Hero Report Generator to package the final executive report. ### Limitations Public websites may block crawling. The Actor uses fallback discovery but cannot guarantee exhaustive customer discovery. It does not scrape private CRM data or contact information. # Actor input Schema ## `companyName` (type: `string`): Company to analyze. ## `companyWebsite` (type: `string`): Website URL to crawl for customers, case studies, testimonials, and proof pages. ## `targetRegion` (type: `string`): Optional market/region context. ## `maxPages` (type: `integer`): Maximum internal proof pages to fetch after the homepage. ## `includeSearchLinks` (type: `boolean`): Include durable G2/Capterra/search links for manual follow-up. ## `testMode` (type: `boolean`): Return deterministic sample output without crawling. ## `enableSearchFallback` (type: `boolean`): If the website blocks crawling or has no obvious proof pages, search the public web for customer stories/case studies/testimonials. ## `maxSearchResults` (type: `integer`): Maximum search-result proof candidates to keep when fallback search is used. ## Actor input object example ```json { "companyName": "Freshworks", "companyWebsite": "https://www.freshworks.com", "targetRegion": "Global", "maxPages": 8, "includeSearchLinks": true, "testMode": false, "enableSearchFallback": true, "maxSearchResults": 8 } ``` # Actor output Schema ## `results` (type: `string`): Structured canonical rows produced by the actor. ## `summary` (type: `string`): Human/agent-readable run summary stored in the default key-value store. # 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 = { "companyName": "Freshworks", "companyWebsite": "https://www.freshworks.com" }; // Run the Actor and wait for it to finish const run = await client.actor("shashghosh/hero-customer-discovery-v2").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 = { "companyName": "Freshworks", "companyWebsite": "https://www.freshworks.com", } # Run the Actor and wait for it to finish run = client.actor("shashghosh/hero-customer-discovery-v2").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 '{ "companyName": "Freshworks", "companyWebsite": "https://www.freshworks.com" }' | apify call shashghosh/hero-customer-discovery-v2 --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=shashghosh/hero-customer-discovery-v2", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Hero Customer Discovery v2 - Customer Proof Candidates", "description": "Discover public customer-proof pages, case-study candidates, testimonials, and transformation-story evidence from a company website.", "version": "1.2", "x-build-id": "zwU99GffZJPqMOrom" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/shashghosh~hero-customer-discovery-v2/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-shashghosh-hero-customer-discovery-v2", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/shashghosh~hero-customer-discovery-v2/runs": { "post": { "operationId": "runs-sync-shashghosh-hero-customer-discovery-v2", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/shashghosh~hero-customer-discovery-v2/run-sync": { "post": { "operationId": "run-sync-shashghosh-hero-customer-discovery-v2", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "companyName", "companyWebsite" ], "properties": { "companyName": { "title": "Company name", "type": "string", "description": "Company to analyze." }, "companyWebsite": { "title": "Company website", "type": "string", "description": "Website URL to crawl for customers, case studies, testimonials, and proof pages." }, "targetRegion": { "title": "Target region", "type": "string", "description": "Optional market/region context.", "default": "Global" }, "maxPages": { "title": "Max proof pages", "minimum": 1, "maximum": 20, "type": "integer", "description": "Maximum internal proof pages to fetch after the homepage.", "default": 8 }, "includeSearchLinks": { "title": "Include review/search links", "type": "boolean", "description": "Include durable G2/Capterra/search links for manual follow-up.", "default": true }, "testMode": { "title": "Test mode", "type": "boolean", "description": "Return deterministic sample output without crawling.", "default": false }, "enableSearchFallback": { "title": "Enable search fallback", "type": "boolean", "description": "If the website blocks crawling or has no obvious proof pages, search the public web for customer stories/case studies/testimonials.", "default": true }, "maxSearchResults": { "title": "Max fallback search results", "minimum": 1, "maximum": 20, "type": "integer", "description": "Maximum search-result proof candidates to keep when fallback search is used.", "default": 8 } } }, "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 } } } } } } } } } } ```