# Email Pattern Finder - Likely Work Emails from Name + Domain (`oriented_wallpaper/email-pattern-finder`) Actor Generate the most likely work email formats (first.last@, flast@, first@...) from a full name and company domain, ranked by how common each pattern is, plus an MX domain check. Read-only B2B prospecting: builds address strings and does a DNS lookup only - never sends email or probes mailboxes. - **URL**: https://apify.com/oriented\_wallpaper/email-pattern-finder.md - **Developed by:** [Flash Scrape](https://apify.com/oriented_wallpaper) (community) - **Categories:** Lead generation, Automation, Developer tools - **Stats:** 1 total users, 0 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $1.40 / 1,000 person processeds 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 ## Email Pattern Finder — likely work emails from a name + domain Generate the **most likely professional email formats** for a person at a company. Feed it a full name and a company domain, get back ranked candidates (`first.last@`, `flast@`, `first@`…) with a confidence score and an MX domain check. Standard B2B prospecting tooling — the natural front of the **find → verify → enrich** flow (pair with the Email Verifier and Company Enricher). ### Privacy & safety Read-only. It **only builds address strings** from the names you provide and does a **DNS (MX) lookup** on the domain. It **never sends any email** and **never probes individual mailboxes**. ### What you get per person - `best_guess` — the single most likely email + a confidence score - `candidates` — all ranked guesses with the pattern that produced each - `mx_found` + `email_provider` — whether the domain accepts mail (Google Workspace / Microsoft 365 / other) ### Input ```json { "domain": "stripe.com", "names": ["Patrick Collison", "Jane Doe"], "verifyMx": true, "maxCandidatesPerPerson": 6 } ```` ### Output | name | best\_guess | confidence | email\_provider | candidates | |------|------------|------------|----------------|------------| | Patrick Collison | patrick.collison@stripe.com | 95 | Google Workspace | \[first.last, flast, …] | Export to CSV, JSON, or Excel. Then run the results through the **Bulk Email Verifier** to filter to deliverable domains. ### Pricing Pay-per-result — charged per person processed. No subscription. # Actor input Schema ## `domain` (type: `string`): The company domain emails should use, e.g. 'acme.com' or 'https://acme.com'. ## `names` (type: `array`): People to generate email guesses for, one full name per line (e.g. 'Jane Doe'). ## `verifyMx` (type: `boolean`): Look up the domain's MX records (DNS) to confirm it can receive mail and detect the email provider. Boosts confidence. No mailbox probing, no email sent. ## `maxCandidatesPerPerson` (type: `integer`): How many ranked email guesses to return per person. ## `patterns` (type: `array`): Optional: only generate these pattern ids (e.g. 'first.last', 'flast', 'first'). Leave empty for all. ## Actor input object example ```json { "domain": "stripe.com", "names": [ "Patrick Collison", "Jane Doe" ], "verifyMx": true, "maxCandidatesPerPerson": 6 } ``` # 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 = { "domain": "stripe.com", "names": [ "Patrick Collison", "Jane Doe" ] }; // Run the Actor and wait for it to finish const run = await client.actor("oriented_wallpaper/email-pattern-finder").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 = { "domain": "stripe.com", "names": [ "Patrick Collison", "Jane Doe", ], } # Run the Actor and wait for it to finish run = client.actor("oriented_wallpaper/email-pattern-finder").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 '{ "domain": "stripe.com", "names": [ "Patrick Collison", "Jane Doe" ] }' | apify call oriented_wallpaper/email-pattern-finder --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=oriented_wallpaper/email-pattern-finder", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Email Pattern Finder - Likely Work Emails from Name + Domain", "description": "Generate the most likely work email formats (first.last@, flast@, first@...) from a full name and company domain, ranked by how common each pattern is, plus an MX domain check. Read-only B2B prospecting: builds address strings and does a DNS lookup only - never sends email or probes mailboxes.", "version": "0.1", "x-build-id": "YerB9Ttr9JEFGOxVW" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/oriented_wallpaper~email-pattern-finder/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-oriented_wallpaper-email-pattern-finder", "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/oriented_wallpaper~email-pattern-finder/runs": { "post": { "operationId": "runs-sync-oriented_wallpaper-email-pattern-finder", "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/oriented_wallpaper~email-pattern-finder/run-sync": { "post": { "operationId": "run-sync-oriented_wallpaper-email-pattern-finder", "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": [ "domain", "names" ], "properties": { "domain": { "title": "Company domain", "type": "string", "description": "The company domain emails should use, e.g. 'acme.com' or 'https://acme.com'." }, "names": { "title": "Full names", "type": "array", "description": "People to generate email guesses for, one full name per line (e.g. 'Jane Doe').", "items": { "type": "string" } }, "verifyMx": { "title": "Check domain MX", "type": "boolean", "description": "Look up the domain's MX records (DNS) to confirm it can receive mail and detect the email provider. Boosts confidence. No mailbox probing, no email sent.", "default": true }, "maxCandidatesPerPerson": { "title": "Max candidates per person", "minimum": 1, "maximum": 12, "type": "integer", "description": "How many ranked email guesses to return per person.", "default": 6 }, "patterns": { "title": "Limit to specific patterns (optional)", "type": "array", "description": "Optional: only generate these pattern ids (e.g. 'first.last', 'flast', 'first'). Leave empty for all.", "items": { "type": "string" } } } }, "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 } } } } } } } } } } ```