This article provides a complete, practical walkthrough of the Filter Manual 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 Company Research with n8n, Google Sheets, and the ProspectLens API
   
   Meta Description:
   Discover how to automate your company research workflow using n8n, Google Sheets, and the ProspectLens API. Streamline data enrichment and save hours of manual effort with this powerful low-code automation.
   
   Keywords:
   company data enrichment, n8n workflow, Google Sheets automation, ProspectLens API, B2B prospecting, API-based automation, no-code business intelligence, automated company research, funding data, web traffic stats
   
   Third-Party APIs Used:
   - ProspectLens API (via APIroad)
   - Google Sheets API
   
   Article:
   
   Automating Company Research with n8n, Google Sheets, and the ProspectLens API
   
   In modern sales and marketing operations, good data is no longer optional—it’s essential. Whether you're building lead lists, preparing for outreach, or validating inbound prospects, having accurate company data like funding, web traffic, or business descriptions can determine the success of your campaigns. However, collecting this data manually is inefficient and error-prone.
   
   Enter automation. Using n8n, a powerful open-source workflow automation tool, you can build a streamlined pipeline that enriches company data with real-time insights from external sources and stores it in a collaborative workspace like Google Sheets.
   
   This article walks through a real-world n8n workflow designed to automate company research using Google Sheets and the ProspectLens API, hosted on APIroad. If you’re a startup founder, SDR, marketer, or operations engineer looking to save time and increase accuracy—this is for you.
   
   🎯 What This Workflow Does
   
   The workflow, aptly named "ProspectLens Company Research," performs the following key tasks:
   
   1. Fetches company domains from a Google Sheet.
   2. Filters out companies that have already been processed.
   3. Sends each new domain to the ProspectLens API.
   4. Extracts relevant company data like funding, location, traffic, and description.
   5. Writes the enriched data back into the same Google Sheet.
   6. Marks each processed row with a timestamp to prevent duplication.
   
   🔧 Third-Party Tools and APIs
   
   This automated setup integrates two main platforms:
   
   - Google Sheets API: Used for fetching and writing data from a shared spreadsheet.
   - ProspectLens API (via APIroad): This API enriches a domain with detailed company insights drawn from web and financial data.
   
   🧠 Workflow Breakdown
   
   Let’s unpack how the workflow functions inside n8n:
   
   1. Manual Trigger
   
   The process begins with a "Manual Trigger" node—perfect for testing or running the workflow on-demand. This ensures you can control when data is processed, making it easy to test workflows without relying on schedules.
   
   2. Get All Rows from Google Sheets
   
   Next, the workflow pulls all data from a designated Google Sheet. This typically includes company domains that need researching. You can duplicate the template spreadsheet [here](https://docs.google.com/spreadsheets/d/1S2S18hvfBoFsUgRYPyizH6uv7WwI218frvOqu2bV3wk/edit).
   
   3. Filter Unprocessed Rows
   
   A “Filter” node checks each row to see if the processed_at field is empty—meaning the company hasn’t been researched yet. Only unprocessed domains move forward to conserve API usage and avoid redundant work.
   
   4. Loop Over Items in Batches
   
   To control concurrency and avoid rate-limiting, a “SplitInBatches” node processes one domain at a time. This creates a safer, more stable experience when sending HTTP requests to external APIs.
   
   5. Enrich Company Data via ProspectLens API
   
   An HTTP Request node queries the ProspectLens API for each company’s domain. The API returns a wealth of information, including:
   
   - Company name
   - Website domain
   - Short business description
   - Web traffic data (from SEMrush)
   - Funding round type and amount
   - Headquarter location
   - Year founded
   
   Each response is parsed and prepared for data entry in the next step.
   
   6. Save Enriched Data to Google Sheets
   
   The updated data is mapped to specific columns in the original sheet. It also adds a processed_at timestamp to track work completion. Additional metadata like the entire JSON response can be stored for debugging or auditing purposes.
   
   📊 What Makes This Workflow Great
   
   - Reusability: You can reuse it for new batches of companies by simply adding rows to the same sheet.
   - Scalability: The loop and retry logic make this stable at scale.
   - Transparency: Information is kept visible in Google Sheets, allowing non-technical teams to access and review it.
   - No-Code Friendly: While n8n lets you inject JavaScript, its visual UI makes the logic approachable even for business users.
   
   🔐 Credentials & Setup
   
   To use this workflow, you’ll need:
   - An API key for ProspectLens (available via [APIroad](https://apiroad.net/marketplace/apis/prospectlens))
   - Access to a Google account and configured Google Sheets OAuth credentials in n8n
   
   You’ll also want to make a copy of the Google Sheet template provided in the note node of the workflow.
   
   🚀 Final Thoughts
   
   If you're tired of manually researching companies for your CRM, sales outreach, or market analysis, this n8n workflow can transform your process. With just a few clicks, you can enrich spreadsheet-based lead lists using real-time data from ProspectLens—all without writing a single full-fledged application.
   
   Whether you’re working as an individual contributor or managing a scaling revops function, automating repetitive research tasks like these is not just smart—it’s essential for staying competitive.
   
   Now go ahead, test the workflow, and let automation do the heavy lifting.
   
   —
   
   Looking to build your own? Clone the essential components, plug in your API keys, and start mastering automation with n8n!

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.