This article provides a complete, practical walkthrough of the Wait Splitout Process Scheduled 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 Lead Enrichment at Scale with n8n and Dropcontact
   
   Meta Description:  
   Explore a powerful n8n workflow that automates asynchronous bulk requests to Dropcontact for lead enrichment. Learn how to query, transform, and update CRM data at 1500+ requests per hour—entirely code-free.
   
   Keywords:  
   n8n workflow automation, Dropcontact integration, lead enrichment automation, PostgreSQL CRM data update, batch API requests, B2B lead generation, async API batching, n8n Dropcontact use case, automate contact enrichment, data pipeline automation
   
   Third-Party APIs Used:
   
   - Dropcontact API (https://api.dropcontact.io): Used for asynchronous batch submission and retrieval of enriched contact data.
   - Slack API (via n8n Slack node): Sends automated alerts in case of issues (e.g., insufficient credits).
   
   Article:
   
   In the fast-paced world of sales and lead generation, enriched and up-to-date contact information is the fuel that drives engagement and growth. Manual research and enrichment of records is impractical at scale. Enter workflow automation, specifically this efficient and scalable n8n-based integration that streamlines enrichment of B2B profiles using Dropcontact—at a pace of 1500+ requests per hour.
   
   Let’s break down how this automation works under the hood.
   
   🧠 Objective: Asynchronous Bulk Enrichment
   
   The main goal of this workflow is to verify and enrich missing email data for contacts with the title “Bestuurder” (Dutch for “Director”) within a CRM, excluding generic email domains like Gmail and Outlook. Using the Dropcontact API, this automation smartly batches data into sets of 250, submits it asynchronously, waits for processing, then updates the enriched records back into a PostgreSQL database—all without developer intervention.
   
   🔄 Step-by-Step Workflow Breakdown
   
   1. Schedule Trigger
   
   The automation initiates with the n8n Schedule Trigger, allowing it to run at regular intervals (e.g., hourly, daily depending on configuration), ensuring that newly added or updated records remain enriched consistently.
   
   2. Querying Profiles from PostgreSQL
   
   The Postgres node ("PROFILES QUERY") performs an SQL query to extract up to 1000 records missing verified emails—strictly filtering out personal domains to ensure only business addresses are processed.
   
   3. Batching and Aggregation
   
   Next, the data is split into manageable chunks of 250 using the SplitInBatches node ("Loop Over Items2"). This conforms to Dropcontact’s API limits while keeping throughput high.
   
   The Aggregate node consolidates the necessary fields like first_name, last_name, domain, and full_name for each profile. This ensures the data adheres to Dropcontact’s API schema.
   
   4. Python-Powered Transformation
   
   Perhaps the most interesting part is the "DATA TRANSFORMATION" code node that runs Python within n8n. Here, raw data from aggregation is restructured into Dropcontact’s expected JSON format:
   - Each record in the batch becomes a dictionary with first_name, last_name, website (domain), and full_name as custom_fields.
   - The output JSON is structured to be sent directly to Dropcontact.
   
   5. Sending Requests to Dropcontact (Batch API)
   
   The constructed batch is then POSTed to Dropcontact’s /batch endpoint by the “BULK DROPCONTACT REQUESTS” node, authenticated via an access token.
   
   Because Dropcontact operates asynchronously, the workflow pauses for 600 seconds using the "Wait2" node. This wait time allows the enrichment engine to process submitted records.
   
   6. Retrieve Enriched Data
   
   After the wait, a GET request is fired to Dropcontact’s /batch/{request_id} endpoint (via the "BULK DROPCONTACT DOWNLOAD" node) to pull processed data.
   
   A conditional Slack alert ("Slack" node) is sent if an issue arises—possibly triggered by failed responses, API credit exhaustion, or invalid request structures.
   
   7. Data Disaggregation & Update
   
   The response, which bundles all enriched records in a data array, is split into individual records using the “Split Out” node.
   
   Each enriched contact is then used to update the corresponding record in the PostgreSQL database ("Postgres" node). Matching is done via the full_name field, and fields like email, phone, qualification, and a flag (dropcontact_found) are updated accordingly.
   
   🧩 Highlights & Benefits
   
   - 🔁 Fully Automated Loop: From querying to enrichment to database update.
   - 🚀 Batch Asynchronous API Handling: Efficiently meets Dropcontact rate limits for high-volume data.
   - ✨ Clean Code Transformation: Built-in Python node cleanly structures the payload.
   - 📣 Slack Notifications: Monitors faults like API credit shortages.
   - 🎯 CRM-Ready Filtering: SQL query ensures only business-relevant contacts are processed—no consumer domains.
   
   ⚙️ Scalability
   
   With a batch size of 250 and a limit of 1000 records per trigger run, the system is poised to process at least 1500 leads/hour effortlessly. Easily adjustable batch sizes or interval triggers can crank this up further.
   
   📌 Final Thoughts
   
   This n8n workflow is a perfect example of low-code automation empowering sales ops and marketing teams. It leverages best practices—data batching, asynchronous API use, and error handling—within a visual interface that’s easy to maintain.
   
   By automating contact enrichment at scale, it ensures your CRM stays both accurate and actionable, so your team can focus on pipeline-building rather than data hygiene tasks.
   
   Ready to build something similar? The beauty of n8n is that it’s adaptable—plug in different APIs, databases, and workflows depending on your use case.
   
   Let your leads be enriched, automatically.
   
   —  
   Are you using n8n and looking for more templates or workflow designs? Visit the n8n docs or community to discover how fellow automation enthusiasts are transforming their operations—one node at a time.

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.