A lot of go-to-market plumbing already runs in n8n. A form submission arrives, a CRM record gets created, a Slack message goes out. The step that usually gets skipped is the one that tells you who actually came in: the company behind the email domain, the person's role, whether the address is even valid. Adding it meant an HTTP Request node, hand-built headers, and a response you had to unpack yourself.
Data Legion now has a verified node in n8n. Search the nodes panel for Data Legion, install it, paste an API key, and enrichment becomes one more step in the workflow, with fields you can map like any other node's.
To build a workflow before spending anything, turn on Sandbox Mode in the credential. Enrich then returns sample records with every field a real record has, and uses no credits.
What the node does
The node has five operations across three resources:
- Person > Enrich finds a person by email, phone, social profile URL, or a name with one more detail such as a company or school.
- Company > Enrich finds a company by domain, name, social profile URL, or ticker.
- Utility > Clean Data, Hash Email, and Validate Data normalize contact and company fields, hash an email address three ways, and flag problems before you look anything up.
Enrich bills your Data Legion account for each record returned, under your plan, and a no-match costs nothing. The three utilities are free.
Identifiers take n8n expressions, so mapping {{ $json.email }} from a form trigger or {{ $json.website }} from a CRM node works the way it does everywhere else in n8n. Two options narrow what counts as a match: a minimum match confidence, and a list of fields a record must have, such as work_email. A record without those fields comes back as a no-match, so you don't pay for a profile your next step can't use.
Every row comes back
On a list, the case that matters is the one where nothing matches. The Data Legion API answers a no-match with a 404, and a workflow that treats that as an error either stops or quietly drops the item. On a list of five hundred leads, neither is acceptable.
The node returns one output item for every input item, always. A match carries the full person or company record, matched: true, and the match metadata (what it matched on and with what confidence). A miss comes back as { "matched": false } in the same position. Put an If node on matched to send the two down separate paths.
Real errors explain themselves. An invalid key points you to the credential, an empty balance tells you to add credits, and a rate limit suggests turning on Retry On Fail. With n8n's On Error set to continue, a failed item carries the reason and the rest of the list keeps going.
Giving it to an AI Agent
The node also works as a tool for n8n's AI Agent. Connect it to the agent's Tool input, pick Company > Enrich, and let the model fill in the domain. Ask the agent which industry a company is in, and it calls the tool, reads the record, and answers from data rather than from memory.
That's the pattern we'd suggest for any agent that answers questions about people or companies: give it a lookup it can call, and check what came back. Evaluating Agent Tool Calls covers the checking half. For assistants outside n8n, our MCP server and CLI expose the same data with the same API key.
When to call the API instead
The node covers enrichment and the utilities, one record per item. Search across the full dataset, natural-language discovery, and bulk delivery live on the Data Legion API. n8n's HTTP Request node reaches all of it with the same key in an API-Key header, and the two mix freely in one workflow.
If records should be enriched the instant they arrive in your own systems rather than in an automation tool, Building a Real-Time Enrichment Pipeline walks through that architecture.
Like every Data Legion product, the node never returns people who have opted out, and contact data is for lawful use under our Acceptable Data Use policy. The n8n integration guide has the full list of identifiers, options, and output fields. A quick way to judge the data is a handful of records you already know: run them through the node and compare. If enrichment is new to your team, What Is Data Enrichment explains what to expect from the records.