Should I use n8n Cloud or self-hosted?

For low-volume flows, n8n Cloud is convenient. Above 10,000 executions/month or with sensitive data, self-hosted (Docker/Railway) is better for cost and compliance.

Which enrichment sources are most accurate?

Apollo for company info, BuiltWith/Wappalyzer for tech stack, Clay or Hunter for contact info are the most common combinations. Cross-validating 2–3 sources is usually required.

How does MCP (Model Context Protocol) fit in here?

MCP lets an LLM connect to external sources (CRM, database, internal API) through a standard interface. When the LLM should query the CRM directly instead of going through n8n, an MCP architecture is preferred.

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AI & Automation•July 10, 2026•10 min read

n8n Lead Enrichment Automation: Step-by-Step Setup

How to combine Clay, Apollo and OpenAI inside n8n to build an automatic enrichment workflow for every new lead.

TL;DR — Summary

n8n is more flexible than Zapier, self-hostable and cost-scalable.
The enrichment flow is: webhook → Apollo/Clay enrichment → LLM-based ICP scoring → write to CRM.
Summarizing lead notes with OpenAI/Gemini and pushing them into HubSpot boosts SDR efficiency by 30%+.
The critical factor is data quality — bad enrichment burns the entire SDR team's time.

Why n8n Beats Zapier

Because n8n can be self-hosted, it wins on data sovereignty and cost. Beyond 400+ native integrations, a custom HTTP node connects to any API. Rich branching (if/switch) and JavaScript nodes make scenarios possible that Zapier cannot handle.

Architecture of the Enrichment Flow

A typical lead enrichment flow has these steps:

Webhook: a lead's form submission lands in n8n.
Apollo/Clay: company info, employee count and tech stack are pulled by email.
OpenAI: an LLM prompt summarizes lead notes and scores ICP fit (0–100).
HubSpot/Pipedrive: enriched data is written to the CRM and an owner is assigned.
Slack: high-scoring leads notify the SDR team in real time.

LLM Prompting Tips

Give the model a clean criteria list for ICP fit scoring. Instead of vague questions like 'how well does this company match our ICP?', ask for a score based on industry, employee count and tech. Use structured output (JSON schema) so downstream nodes can process the data without breaking.

#n8n#Automation#LLM#Lead Enrichment