n8n-nodes-factori

Factori People data enrichment community node for n8n, enrich person profiles at scale directly within your n8n automation workflows, without writing custom code.

By integrating Factori AI's data enrichment capabilities into n8n, teams can apply multi-dimensional filters and generate structured output, either as individual records or in bulk.

Prerequisites

• An active n8n account (cloud or self-hosted)
• Access to n8n Settings with permissions to install community nodes
• A Factori account with an API key

Installation

Step 1 — Open Community Node Settings

1. Log in to your n8n account.
2. Navigate to Settings from the left-hand sidebar.
3. Select Community Nodes from the Settings menu.

Step 2 — Install the Factori Node

1. Click Install on the Community Nodes page.
2. In the search field, enter n8n-nodes-factori.
3. Click Install and wait for the installation to complete.

Once installed, the Factori People data enrichment node will appear in the confirmation list.

Step 3 — Verify the Node in the Gallery

1. Navigate to your workflow canvas (or create a new blank canvas).
2. Click the + Add Node button or press Tab to open the node gallery.
3. Search for Factori — the node should appear in the results.

For more details, see the n8n community nodes installation guide.

Credentials

Authentication with the Factori platform is handled via an API key. You must generate this key from the Factori Console

1. Visit the Factori Console and sign up or log in.
2. Navigate to the API Keys section within your account settings.
3. Click Generate API Key and copy the key.
4. In n8n, add a new credential of type Factori API and paste your API key.

Once the API key has been generated, copy and store the key securely, once pasted into the Factori People data enrichment node in n8n, you will be all set to execute your workflows.

Operations

Person —> Enrich

You can apply one or more of the following filters to narrow down results. All filters are optional, however providing at least one filter is required to execute the node.

| Filter | Type | Description | Example | | -------------------- | ------ | ------------------------------ | --------------------------- | | Name | String | Full or partial name | Jane Smith | | Email Address | String | Professional or personal email | [email protected] | | Company / Org. | String | Company or organisation | Acme Corporation | | Job Title / Role | String | Current job title or role | Head of Engineering | | Location | String | City, state, or country | San Francisco, CA | | LinkedIn URL | String | Full LinkedIn profile URL | linkedin.com/in/janesmith |

Output Modes

• Single Output — returns one best-matching record.
• Multiple Output — returns all matching records.

Results are returned as structured JSON available for downstream nodes in your workflow.

Output Schema

Each enriched record contains the following fields:

| Field | Description | | -------------------------------- | ------------------------------------------------------ | | people_id | Unique person identifier | | first_name, last_name | Name | | age, gender, date_of_birth | Demographics | | social | LinkedIn, Twitter, Facebook URLs | | emails | Personal and work emails (with MD5, SHA1, SHA2 hashes) | | phone | Phone numbers with type flags (cell, landline, work) | | address | Full address, city, state, ZIP, lat/long, census data | | education | Education level and history (school, degrees) | | employment | Title, level, job function, skills, work experience | | company | Company name, industry, size, revenue, SIC/NAICS codes | | financial | Net worth, credit rating, mortgage info | | household | Household income, size, dwelling type, marital status | | property | Home value, build year, pool, AC | | hobby | Hobbies and activities | | buyer | Buyer categories | | interests | Interest categories |

{
	"people_id": "a1b2c3d4e5f6a7b8c9d0e1f2a3b4c5d6e7f8a9b0",
	"first_name": "jane",
	"last_name": "doe",
	"age": 32,
	"gender": "Female",
	"date_of_birth": { "year": 1993, "month": 6, "day": 21 },
	"social": {
		"linkedin_url": "https://www.linkedin.com/in/janedoe",
		"twitter_url": "twitter.com/janedoe",
		"facebook_url": "facebook.com/janedoe"
	},
	"emails": {
		"personal": [
			{
				"email_address": "[email protected]",
				"email_domain": "gmail.com",
				"email_md5": "abcdef1234567890abcdef1234567890",
				"email_sha1": "aabbccddeeff00112233445566778899aabbccdd",
				"email_sha2": "1234567890abcdef1234567890abcdef1234567890abcdef1234567890abcdef"
			}
		],
		"work": [
			{
				"email_address": "[email protected]",
				"email_domain": "acmecorp.com",
				"email_md5": "1234567890abcdef1234567890abcdef",
				"email_sha1": "ffeeddccbbaa99887766554433221100ffeeddcc",
				"email_sha2": "abcdef1234567890abcdef1234567890abcdef1234567890abcdef1234567890"
			}
		]
	},
	"phone": [
		{
			"phone_number": "4155550192",
			"is_cellphone": true,
			"is_landline": false,
			"is_workphone": false
		}
	],
	"address": {
		"full_address": "742 evergreen terrace",
		"city": "austin",
		"state": "Texas",
		"zip": "73301"
	},
	"education": {
		"education_level": "Completed College",
		"education_history": [
			{
				"school": "University of Texas at Austin",
				"degrees": ["Bachelor of Science (BS)", "Computer Science"]
			}
		]
	},
	"employment": {
		"employee_title": "product manager",
		"employee_level": "senior",
		"employee_job_function": "product",
		"recent_job_change": true,
		"skills": ["product management", "agile", "roadmapping", "data analysis"],
		"work_experience": [
			{
				"title": "Associate Product Manager",
				"company": "Amazon",
				"start_year": "2017",
				"end_year": "2019"
			},
			{ "title": "Product Manager", "company": "Stripe", "start_year": "2019", "end_year": "2022" }
		]
	},
	"company": {
		"company_name": "acme corp",
		"company_industry": "technology",
		"company_num_employees": "1001-5000",
		"company_revenue_range": "100m-500m",
		"company_website": "acmecorp.com"
	},
	"financial": {
		"income": { "net_worth": "$50000 - $99999" },
		"credit_information": { "credit_rating": "Between 700-749" }
	},
	"household": {
		"household_income": "$95000 - $104999",
		"marital_status": "Single",
		"homeowner": "Renter",
		"household_size": 2
	},
	"property": {
		"home_market_value": 320000,
		"property_build_year": 2005
	},
	"hobby": ["avid_readers", "cooking_hobby", "fitness_hobby"],
	"buyer": ["electronics_buyer", "books_buyer", "health_buyer"],
	"interests": ["interest_in_technology", "interest_in_fitness", "interest_in_travel"]
}

Troubleshooting

HTTP Error Codes

If an API request encounters an error, the Factori API returns informative error messages with corresponding status codes. Refer to the Factori Documentation for detailed error handling guidance.

Resources

• n8n community nodes documentation
• Factori Documentation
• Factori Console

License

MIT