This article provides a complete, practical walkthrough of the Splitout Code Automation Webhook n8n agent. It connects HTTP Request, Webhook across approximately 1 node(s). Expect a Intermediate setup in 15-45 minutes. One‑time purchase: €29.

What This Agent Does

This agent orchestrates a reliable automation between HTTP Request, Webhook, handling triggers, data enrichment, and delivery with guardrails for errors and rate limits.

It streamlines multi‑step processes that would otherwise require manual exports, spreadsheet cleanup, and repeated API requests. By centralizing logic in n8n, it reduces context switching, lowers error rates, and ensures consistent results across teams.

Typical outcomes include faster lead handoffs, automated notifications, accurate data synchronization, and better visibility via execution logs and optional Slack/Email alerts.

How It Works

The workflow uses standard n8n building blocks like Webhook or Schedule triggers, HTTP Request for API calls, and control nodes (IF, Merge, Set) to validate inputs, branch on conditions, and format outputs. Retries and timeouts improve resilience, while credentials keep secrets safe.

Third‑Party Integrations

• HTTP Request
• Webhook

Import and Use in n8n

1. Open n8n and create a new workflow or collection.
2. Choose Import from File or Paste JSON.
3. Paste the JSON below, then click Import.
4. Show n8n JSON

   Title:  
   Automating LinkedIn Lead Enrichment with n8n: A Complete Workflow for Data-Driven Prospecting
   
   Meta Description:  
   Leverage this advanced n8n workflow to automate LinkedIn lead generation and enrichment using Apollo, OpenAI, RapidAPI, and Google Sheets. Discover how to streamline B2B lead research and build rich prospect profiles at scale.
   
   Keywords:  
   n8n LinkedIn lead automation, Apollo lead generation, LinkedIn enrichment, OpenAI LinkedIn summaries, RapidAPI LinkedIn data, Google Sheets CRM automation, LinkedIn scraping workflow, AI cold outreach, lead scoring automation, B2B prospecting automation
   
   Third-Party APIs Used:
   
   1. Apollo.io API – For lead searching and contact data enrichment
   2. OpenAI (GPT-3.5 Turbo) – To extract and summarize profile information and LinkedIn posts
   3. RapidAPI – For querying LinkedIn public data like posts and profile summaries
   4. Mails.so API – For email validation and deliverability checking
   5. Google Sheets API – For storing, updating, and triggering lead and enrichment data
   
   Article:
   
   In today’s hypercompetitive B2B environment, efficiently identifying, qualifying, and personalizing outreach to leads is not just a luxury—it’s a necessity. Manual research on LinkedIn and CRM updating can take hours for every 10–20 leads, which slows down sales and marketing teams. To address this, an advanced no-code automation solution built using n8n offers a holistic workflow that completely automates LinkedIn lead generation and enrichment using several powerful tools and third-party APIs.
   
   In this article, we'll deconstruct a complex yet scalable n8n workflow designed to combine lead generation from Apollo.io, data enrichment with RapidAPI and OpenAI, contact email validation, and storage in Google Sheets. Whether you're a growth hacker, sales ops expert, or automation enthusiast, this workflow creates a scalable foundation for smart prospecting.
   
   🛠️ How the Workflow Works
   
   Step 1: Capture Form Inputs  
   The workflow kicks off with an n8n Form Trigger node that collects critical parameters—Job Title, Location, and Number of Leads—from the user. This allows for dynamic and targeted searches based on specific prospecting needs.
   
   Step 2: Generate Leads via Apollo.io API  
   Once the inputs are captured, Apollo’s "mixed_people/search" endpoint is called with the parameters. It returns a list of potential leads, including their ID, name, job title, and LinkedIn URL.
   
   Step 3: Store Raw Leads in Google Sheets  
   The returned list of leads is split and passed through a clean-up process. Each record is enriched with additional metadata placeholders (statuses) and then appended to a central “Apollo AI Leads” Google Sheet. This sheet acts as the operational CRM for enrichment workflows.
   
   Step 4: Extract LinkedIn Usernames  
   To interact with LinkedIn profile APIs later, the workflow uses OpenAI (GPT-3.5-Turbo) to strip the LinkedIn URLs down to usernames. These cleaned usernames are then updated back into Google Sheets.
   
   Step 5: Reveal Contact Email Address  
   For each lead with a pending contacts scrape status, the workflow uses Apollo’s API to fetch potential emails and phone numbers. Email addresses are passed to the Mails.so API for verification. Only deliverable or high-confidence addresses are saved; undeliverable ones are marked as invalid with an option to re-queue them for retries after 4 weeks using a scheduler.
   
   Step 6: Profile Summary Enrichment  
   Here’s where AI adds some serious power. Using RapidAPI's LinkedIn data endpoints (“get-profile” and “get-profile-posts”), the workflow pulls employment history, education, languages, and recent posts. This data is then formatted and passed through OpenAI, where a personalized profile summary is generated. These summaries are ideal for crafting personalized cold outreach emails.
   
   Step 7: Post Summary & Sentiment Analysis  
   Similarly, the most recent LinkedIn posts are condensed into a narrative using OpenAI. This post analysis gives marketing and sales teams insight into what the lead currently cares about—perfect for crafting timely introductions.
   
   Step 8: Update and Monitor Statuses  
   Every lead moves through multiple data states (pending, failed, completed). Workflow branches track these states and retry failed scrapes periodically. This ensures leads don’t go missing due to transient API errors or rate limits.
   
   Step 9: Final Enriched Profiles Sync  
   Once a lead has completed email extraction, profile enrichment, and post summarization, it is flagged as “completely enriched.” These leads are then appended to a dedicated “Enriched Leads Database” Google Sheet, ready for CRM import or outreach campaigns.
   
   💡 Why This Workflow Stands Out
   
   - Fully No-Code: You don’t need to write a line of server-side code to build this.
   - Modular & Scalable: Each phase (email, posts, profile, usernames) can be scheduled, split, and retried.
   - Human-Like AI Summaries: GPT-3.5 writes cold-email-ready summaries.
   - Real-Time Sync: Google Sheets is used both as the trigger and record-keeper.
   - Error Handling: Failed email validations or profile scrapes are queued with automatic retry logic.
   
   📈 Real-World Use Cases
   
   - B2B SaaS companies automating LinkedIn outreach
   - Lead-gen agencies enhancing Apollo contact data
   - Growth marketers building enriched lead lists for cold campaigns
   - Talent acquisition teams identifying potential hires
   - ABM teams scoring prospects based on engagement signals and job role
   
   🔐 Security & Compliance Note  
   This workflow fetches publicly available information and uses secure APIs with rate limits and API key protection. However, users should consult legal and compliance teams before automated LinkedIn scraping, especially at scale.
   
   In summary, this n8n-powered automation workflow turns what used to be a tedious, manual enrichment task into a seamless, intelligent, and scalable pipeline—taking data from form to a clean, AI-enriched final database in minutes.
   
   Ready to try it? With n8n’s flexibility and the power of OpenAI and Apollo.io, your lead generation process just got a major upgrade.
   
   — End —

5. Set credentials for each API node (keys, OAuth) in Credentials.
6. Run a test via Execute Workflow. Inspect Run Data, then adjust parameters.
7. Enable the workflow to run on schedule, webhook, or triggers as configured.

Tips: keep secrets in credentials, add retries and timeouts on HTTP nodes, implement error notifications, and paginate large API fetches.

Validation: use IF/Code nodes to sanitize inputs and guard against empty payloads.

Why Automate This with AI Agents

AI‑assisted automations offload repetitive, error‑prone tasks to a predictable workflow. Instead of manual copy‑paste and ad‑hoc scripts, your team gets a governed pipeline with versioned state, auditability, and observable runs.

n8n’s node graph makes data flow transparent while AI‑powered enrichment (classification, extraction, summarization) boosts throughput and consistency. Teams reclaim time, reduce operational costs, and standardize best practices without sacrificing flexibility.

Compared to one‑off integrations, an AI agent is easier to extend: swap APIs, add filters, or bolt on notifications without rewriting everything. You get reliability, control, and a faster path from idea to production.

Best Practices

• Credentials: restrict scopes and rotate tokens regularly.
• Resilience: configure retries, timeouts, and backoff for API nodes.
• Data Quality: validate inputs; normalize fields early to reduce downstream branching.
• Performance: batch records and paginate for large datasets.
• Observability: add failure alerts (Email/Slack) and persistent logs for auditing.
• Security: avoid sensitive data in logs; use environment variables and n8n credentials.

FAQs

Can I swap integrations later? Yes. Replace or add nodes and re‑map fields without rebuilding the whole flow.

How do I monitor failures? Use Execution logs and add notifications on the Error Trigger path.

Does it scale? Use queues, batching, and sub‑workflows to split responsibilities and control load.

Is my data safe? Keep secrets in Credentials, restrict token scopes, and review access logs.