LinkedIn Website Enrichment with n8n Architecture

A r/n8n_ai_agents thread broke down a 5-workflow n8n system that auto-enriches every inbound website lead with LinkedIn data. Seven upvotes and a clean architecture diagram. Here is the same architecture with the cost-cut version that uses one search API for the LinkedIn plus Reddit plus SERP enrichment.

The Five Workflows

1. Lead capture: webhook listens for inbound form submission.
2. SERP enrichment: scoped search for the contact on LinkedIn.
3. Reddit signal: company-domain search for recent mentions.
4. Brand SERP: domain-level SERP plus AI Overviews citations.
5. Merge and write: function node combines all data into one record, writes to HubSpot.

Why Five Workflows Beats One

The original architecture in the thread used five separate workflows instead of one big graph because n8n debugging is much faster when each workflow is small. A single 30-node graph is hard to inspect when a downstream step fails. Five 5-node workflows isolate failures.

The Cost Cut

The thread used three vendors for the enrichment surfaces (a SERP provider, a LinkedIn-data scraper, a Reddit scraper). The consolidated version uses one multi-platform search API for all three. Same data shape, one credit pool, one set of n8n credentials.

The HTTP Node Setup

Each enrichment workflow is an HTTP Request node calling Scavio with the right query shape. n8n credentials hold the API key once; every workflow references the same credential.

// HTTP node config (n8n)
// URL: https://api.scavio.dev/api/v1/google
// Method: POST
// Body Type: JSON
// Body:
{
  "query": "site:linkedin.com/in {{$json.email.split('@')[0]}}",
  "num_results": 5
}
// Headers:
//   x-api-key: {{$credentials.scavio.apiKey}}

The Merge Step

A function node combines all enrichment results into one CRM-ready record. The trick is null-safe extraction: not every prospect has a LinkedIn profile, not every domain has Reddit mentions. The merge function should handle both.

// Function node
const linkedin = $('Scavio LinkedIn').first().json.organic_results?.[0];
const reddit = $('Scavio Reddit').first().json.posts?.slice(0, 5) || [];
const brand = $('Scavio Brand SERP').first().json;

return {
  json: {
    email: $input.first().json.email,
    domain: $input.first().json.domain,
    linkedin_url: linkedin?.link,
    linkedin_title: linkedin?.title,
    reddit_mention_count: reddit.length,
    reddit_top_thread: reddit[0]?.url,
    ai_overview_citations: brand.ai_overview?.citations || []
  }
};

Where the AI Overview Citation Helps

Including AI Overview citations in the brand SERP step is the wedge most enrichment stacks miss. If the prospect's domain is cited in Google AI Overviews for a buying-intent keyword, the SDR has a real opener ("noticed your team is showing up in AI Overviews for..."). Few competitors check this signal.

The Latency Profile

Three Scavio calls in parallel returns under three seconds. Adding retries and HubSpot write keeps the whole pipeline under 10 seconds end-to-end. The user filling out the form sees the "thank you" page; the SDR sees the enriched contact in HubSpot before they finish their next sip of coffee.

The Cost Profile

Each inbound lead consumes about four credits at fast tier ($0.012). At 1,000 inbound leads per month, total enrichment cost is $12. n8n cloud at $20/mo plus this enrichment cost equals $32 for the whole pipeline. Compare to single-purpose enrichment tools at $200+/mo with the same coverage.

Where This Falls Short

Public LinkedIn-via-SERP returns the public profile snippet, not the full profile fields. For teams that need verified work history, education, and skills as structured fields, a paid people-data API layered on top is still required. Most ICP scoring works fine on the snippet alone.

Why This Beats the Single-Vendor Path

Single vendors lock the team into one schema and one billing meter. Multi-platform under one API gives Reddit signal that single-vendor enrichment never includes. Reddit signal is a meaningful intent differentiator for B2B sales teams in 2026.