# Deduped Job Intelligence for AI Agents (`deepapi/job-intelligence-deduper`) Actor Extract, normalize, and deduplicate public job postings into clean hiring-signal records with source evidence, role classification, and confidence scores. - **URL**: https://apify.com/deepapi/job-intelligence-deduper.md - **Developed by:** [DeepAPI](https://apify.com/deepapi) (community) - **Categories:** Lead generation, AI, Agents - **Stats:** 2 total users, 0 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $3.50 / 1,000 valid jobs 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 ## Deduped Job Intelligence for AI Agents Extract, normalize, and deduplicate public job postings into clean hiring-signal records with source evidence, role classification, duplicate groups, and confidence scores. Use this Actor when you need hiring intelligence rather than another raw job feed. Provide public careers, job-board, or direct posting URLs, and the Actor returns validated dataset rows that are ready for sales workflows, recruiting research, market analysis, and AI agents. ### What this Actor does - Crawls user-supplied public careers, job-board, and direct job posting URLs. - Extracts job candidates from supported ATS/job-board pages and fallback job links. - Visits extracted job detail pages to enrich records with location, remote type, seniority, and evidence when available. - Extracts public salary or compensation ranges from structured job data and visible job detail text when available. - Normalizes job titles for cleaner grouping and analysis. - Detects common role functions, seniority signals, locations, and remote types when present. - Matches optional role keywords such as `account executive`, `customer success`, or `sales engineer`. - Groups duplicate postings with a stable `duplicateGroupId`. - Preserves source URLs and evidence for auditability. - Filters output with `maxJobs` and `minConfidence`. - Returns validated dataset rows suitable for automation and AI-agent workflows. ### Use cases - B2B sales teams using hiring as an account signal. - Lead generation agencies enriching company lists. - Recruiters monitoring active roles across target companies. - Investors and analysts tracking hiring momentum. - AI agents that need normalized hiring data rather than raw HTML or duplicate job feeds. ### Supported source types Use public source URLs such as company careers pages, public job-board pages, direct public job posting URLs, and public ATS pages. The output schema currently detects these providers when available: Greenhouse, Lever, Ashby, Workable, SmartRecruiters, Teamtailor, Breezy, and Workday. Provider coverage depends on the public page structure available at crawl time. ### Input Use public HTTP(S) `sourceUrls` as the primary input. Optional filters let you cap results, match role keywords, filter by locations, include duplicate source URLs, set clean-mode requirements, and set a minimum confidence threshold. ```json { "sourceUrls": [ "https://jobs.ashbyhq.com/notion", "https://jobs.lever.co/posthog" ], "maxJobs": 25, "roleKeywords": [ "account executive", "customer success", "sales engineer", "solutions engineer", "engineer" ], "locations": ["United States", "Remote"], "includeSourceDuplicates": true, "requireLocation": false, "requireSalary": false, "requireRoleKeywordMatch": false, "minConfidence": 0.65 } ```` Clean mode toggles can return only jobs that are ready for downstream routing: | Toggle | Effect | |---|---| | `requireLocation` | Return only jobs with a detected location. | | `requireSalary` | Return only jobs with a detected public salary range. | | `requireRoleKeywordMatch` | Return only jobs that matched at least one configured role keyword. | ### Output Each pushed dataset item is a validated, normalized job intelligence record. ```json { "companyName": "Notion", "jobTitle": "Account Executive, Commercial", "normalizedTitle": "Account Executive Commercial", "function": "sales", "location": "New York, United States", "salary": { "currency": "USD", "min": 150000, "max": 180000, "period": "year" }, "sourceUrl": "https://jobs.ashbyhq.com/notion/9526496b-5c39-456b-a454-ebec889e7149", "sourceUrls": [ "https://jobs.ashbyhq.com/notion/9526496b-5c39-456b-a454-ebec889e7149", "https://jobs.ashbyhq.com/notion/fdc2a6c0-396a-45db-b465-683bacf4201e" ], "sourceUrlsText": "https://jobs.ashbyhq.com/notion/9526496b-5c39-456b-a454-ebec889e7149 | https://jobs.ashbyhq.com/notion/fdc2a6c0-396a-45db-b465-683bacf4201e", "jobBoardProvider": "ashby", "duplicateGroupId": "notion:account-executive-commercial", "duplicateCount": 2, "roleKeywords": ["account executive"], "roleKeywordsText": "account executive", "hiringSignal": "Hiring Account Executive, Commercial", "evidence": [ { "type": "job_title", "value": "Account Executive, Commercial", "sourceUrl": "https://jobs.ashbyhq.com/notion/9526496b-5c39-456b-a454-ebec889e7149" }, { "type": "role_keyword", "value": "account executive", "sourceUrl": "https://jobs.ashbyhq.com/notion/9526496b-5c39-456b-a454-ebec889e7149" }, { "type": "salary", "value": "USD 150000-180000 per year", "sourceUrl": "https://jobs.ashbyhq.com/notion/9526496b-5c39-456b-a454-ebec889e7149" } ], "evidenceSummary": "job_title: Account Executive, Commercial (https://jobs.ashbyhq.com/notion/9526496b-5c39-456b-a454-ebec889e7149) | salary: USD 150000-180000 per year (https://jobs.ashbyhq.com/notion/9526496b-5c39-456b-a454-ebec889e7149)", "salaryText": "USD 150000-180000 per year", "confidence": 0.95, "scrapedAt": "2026-06-29T15:52:57.933Z" } ``` Spreadsheet preview: | companyName | normalizedTitle | function | location | salaryText | roleKeywordsText | duplicateCount | sourceUrlsText | |---|---|---|---|---|---|---:|---| | Notion | Account Executive Commercial | sales | New York, United States | USD 150000-180000 per year | account executive | 2 | https://jobs.ashbyhq.com/notion/9526496b... | https://jobs.ashbyhq.com/notion/fdc2a6c0... | ### Example results A local sample run against two public sources produced: ```text Source URLs: 2 Requests processed: 2 Raw jobs found: 74 Valid jobs returned: 25 Pushed jobs: 25 ``` The sample included merged duplicate sources for equivalent postings, role keyword matches, function detection, and source-backed evidence. ### Output Fields For CSV Buyers The dataset keeps rich arrays and objects for agents, but also includes flat text fields for spreadsheet exports: | Field | Meaning | |---|---| | `sourceUrlsText` | Pipe-separated source URLs represented by the deduplicated job. | | `roleKeywordsText` | Pipe-separated matched role keywords. | | `evidenceSummary` | Pipe-separated source evidence summary for review in CSV, Excel, or Sheets. | | `salaryText` | Human-readable salary range when detected. | ### Troubleshooting | Symptom | Most likely reason | What to do | |---|---|---| | No `validJob` rows | The public page did not expose job links/data matching your filters and `minConfidence`. | Lower `minConfidence`, remove restrictive `locations`, or pass the direct ATS/job-board URL. | | Jobs are missing locations or salary | The public job detail page does not expose those fields in visible text or JSON-LD. | Keep the row if the title/source evidence is enough, or filter with `locations` only when location is required. | | Fewer rows than expected | Duplicates were merged, charge limits stopped writes, or `maxJobs` capped output. | Check `OUTPUT.summary`, `duplicateCount`, and `sourceUrlsText`. | ### Limitations - Works with public pages only. - Does not log in to private job boards or social networks. - Does not collect applicant data. - Does not guarantee complete coverage for every ATS or custom careers site. - Salary, department, seniority, location, and remote type are returned only when detected from public source content. - Provider support can vary when public page markup changes. ### Local Development From the portfolio root: ```bash pnpm jobs:test pnpm jobs:typecheck pnpm jobs:sample ``` The sample script uses a separate `storage-live-sample` local storage directory and writes inspection files to `data/live-sample`. ### Pricing Unit Recommended pay-per-event unit: ```text validJob ``` The Actor will charge only when a normalized job passes validation and is pushed to the dataset. # Actor input Schema ## `sourceUrls` (type: `array`): Public careers, job-board, or direct job posting URLs to normalize and deduplicate. ## `maxJobs` (type: `integer`): Maximum number of valid normalized jobs to return. ## `roleKeywords` (type: `array`): Optional keywords used to classify and filter role relevance. ## `locations` (type: `array`): Optional location strings used to filter returned jobs and classify matching locations. ## `includeSourceDuplicates` (type: `boolean`): Include duplicate source URLs in each output record. ## `requireLocation` (type: `boolean`): Clean mode: return only jobs with a detected location. ## `requireSalary` (type: `boolean`): Clean mode: return only jobs with a detected public salary range. ## `requireRoleKeywordMatch` (type: `boolean`): Clean mode: return only jobs that matched at least one configured role keyword. ## `minConfidence` (type: `number`): Minimum confidence score required before a job is returned. ## Actor input object example ```json { "sourceUrls": [ "https://jobs.ashbyhq.com/notion", "https://jobs.lever.co/posthog" ], "maxJobs": 1000, "roleKeywords": [], "locations": [], "includeSourceDuplicates": true, "requireLocation": false, "requireSalary": false, "requireRoleKeywordMatch": false, "minConfidence": 0.65 } ``` # Actor output Schema ## `normalizedJobsUrl` (type: `string`): Dataset items returned by this Actor. Each item is a validated deduplicated job record. ## `runOutput` (type: `string`): OUTPUT key-value store record with run-level counts, filter diagnostics, source-level result counts, and charge-limit status. # 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 = { "sourceUrls": [ "https://jobs.ashbyhq.com/notion", "https://jobs.lever.co/posthog" ] }; // Run the Actor and wait for it to finish const run = await client.actor("deepapi/job-intelligence-deduper").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 = { "sourceUrls": [ "https://jobs.ashbyhq.com/notion", "https://jobs.lever.co/posthog", ] } # Run the Actor and wait for it to finish run = client.actor("deepapi/job-intelligence-deduper").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 '{ "sourceUrls": [ "https://jobs.ashbyhq.com/notion", "https://jobs.lever.co/posthog" ] }' | apify call deepapi/job-intelligence-deduper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=deepapi/job-intelligence-deduper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Deduped Job Intelligence for AI Agents", "description": "Extract, normalize, and deduplicate public job postings into clean hiring-signal records with source evidence, role classification, and confidence scores.", "version": "0.1", "x-build-id": "K2Syk98S7ODZbY3Nk" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/deepapi~job-intelligence-deduper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-deepapi-job-intelligence-deduper", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/deepapi~job-intelligence-deduper/runs": { "post": { "operationId": "runs-sync-deepapi-job-intelligence-deduper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/deepapi~job-intelligence-deduper/run-sync": { "post": { "operationId": "run-sync-deepapi-job-intelligence-deduper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "sourceUrls" ], "properties": { "sourceUrls": { "title": "Source URLs", "minItems": 1, "type": "array", "description": "Public careers, job-board, or direct job posting URLs to normalize and deduplicate.", "items": { "type": "string", "pattern": "^https?://" } }, "maxJobs": { "title": "Max Jobs", "minimum": 1, "maximum": 10000, "type": "integer", "description": "Maximum number of valid normalized jobs to return.", "default": 1000 }, "roleKeywords": { "title": "Role Keywords", "type": "array", "description": "Optional keywords used to classify and filter role relevance.", "items": { "type": "string", "minLength": 1 }, "default": [] }, "locations": { "title": "Locations", "type": "array", "description": "Optional location strings used to filter returned jobs and classify matching locations.", "items": { "type": "string", "minLength": 1 }, "default": [] }, "includeSourceDuplicates": { "title": "Include Source Duplicates", "type": "boolean", "description": "Include duplicate source URLs in each output record.", "default": true }, "requireLocation": { "title": "Require Location", "type": "boolean", "description": "Clean mode: return only jobs with a detected location.", "default": false }, "requireSalary": { "title": "Require Salary", "type": "boolean", "description": "Clean mode: return only jobs with a detected public salary range.", "default": false }, "requireRoleKeywordMatch": { "title": "Require Role Keyword Match", "type": "boolean", "description": "Clean mode: return only jobs that matched at least one configured role keyword.", "default": false }, "minConfidence": { "title": "Minimum Confidence", "minimum": 0, "maximum": 1, "type": "number", "description": "Minimum confidence score required before a job is returned.", "default": 0.65 } } }, "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 } } } } } } } } } } ```