This article provides a complete, practical walkthrough of the Splitout Filter Process 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 Startup Funding Intelligence with AI and n8n: A Workflow Deep Dive
   
   Meta Description:
   Discover how this advanced n8n automation uses AI models like Claude, Perplexity, and Jina to monitor TechCrunch and VentureBeat for startup funding news, extract structured data, and store insights in Airtable for instant analysis.
   
   Keywords:
   startup intelligence, funding news automation, n8n workflow, TechCrunch API, VentureBeat API, Perplexity AI, Claude 3.5 Sonnet, Airtable automation, structured data extraction, angel investing automation, venture capital research, Jina Deep Search, AI business analysis
   
   Article:
   
   Tracking startup funding rounds is a key priority for investors, analysts, and innovation teams. But manually monitoring tech news, scraping articles, and compiling structured data is tedious and inefficient. Enter automation.
   
   In this article, we’ll explore a highly sophisticated no-code workflow built on n8n that automates the full pipeline of funding news extraction. By integrating real-time web scraping, AI-assisted data structuring, and external research tools, this workflow detects and analyzes new funding rounds from major tech media outlets—then seamlessly uploads the data into Airtable for easy exploration and further action.
   
   Let’s walk through the workflow step by step.
   
   Step 1 – Detect Fresh Funding News from Trusted Outlets  
   The automation begins by fetching XML-based news sitemaps from TechCrunch and VentureBeat. These feeds are parsed using n8n’s XML nodes to transform them into structured JSON data.
   
   From there, the workflow uses filters to isolate articles that likely involve funding events—by checking for keywords like “raise” in the article title or URL.
   
   Step 2 – Scrape and Parse the Full Funding Article  
   Once a relevant article is identified, its full URL is collected and the HTML is downloaded via an HTTP request. Two dedicated HTML parsers (one for TechCrunch, one for VentureBeat) extract the content and article title using CSS selectors.
   
   Step 3 – Extract Structured Data with AI  
   The article’s title and body are passed to an AI-powered LangChain information extractor. Using a prompt engineered for funding event details, the AI identifies and extracts:
   
   - Company name
   - Funding round (e.g., Series A)
   - Amount raised
   - Lead and participating investors
   - Market/industry
   - Company valuation
   - Press release URL
   
   This structured data is then merged across sources, so both VentureBeat and TechCrunch mentions of the same event are unified.
   
   Step 4 – Enrich Company Data Using AI Search  
   Just knowing a funding amount isn’t enough. The workflow now initiates a second layer of AI enrichment using multiple LLMs.
   
   First, it uses Perplexity’s Llama 3.1 Sonar model to search online for the company’s official website. Since LLaMA models often struggle with structured responses, a two-step fallback is implemented: Perplexity generates rough info, then Claude 3.5 Haiku parses and corrects the final website URL via structured output prompts.
   
   Step 5 – Store Initial Results in Airtable  
   Cleaned data—including company name, funding size, round type, and URL—are saved to a “Funding Rounds” base in Airtable. This instantly creates a usable database of new startup activity for analysts or investors.
   
   Step 6 – Trigger Deep AI Research  
   A new sub-workflow is then triggered via n8n’s “Execute Workflow” node. This initiates a detailed company background investigation via the Perplexity Deep Research API.
   
   A custom-crafted prompt gathers a wide range of strategic insights, including:
   
   - Founding story and executive details
   - Previous funding information
   - Business model and target market
   - Competitors and positioning
   - Market growth, revenue estimations, and future plans
   
   Claude 3.5 Sonnet is then used to extract structured insights from this Perplexity-generated long-form report. An auto-fixing parser ensures schema validity and handles any output inconsistencies.
   
   If needed, the workflow can optionally switch to Jina AI’s Deep Search API for added research quality or fallback scenarios.
   
   Step 7 – Final Structured Data Storage  
   Once the company intelligence has been fully enriched and structured, the data is written to a second Airtable base: “Company Deep Research.” Each company entry includes everything from valuation models to founding team members, investor history, HQ location, and even a list of data sources and web links.
   
   Third-Party APIs and Services Used:
   
   1. TechCrunch News Sitemap – RSS feed scraped via HTTP
   2. VentureBeat News Sitemap – RSS feed scraped via HTTP
   3. Airtable API – Data storage for structured records
   4. Perplexity AI (OpenRouter) – Deep web-based research and company info
   5. Claude 3.5 Sonnet & Haiku (Anthropic) – Large language models for parsing and correction
   6. LangChain AI – Used for information extraction and prompt chaining
   7. Jina Deep Search – Optional AI-driven research backend
   
   Why This Matters  
   This workflow isn’t just automation—it’s intelligence infrastructure. It transforms vast, messy sources like tech news articles into a structured, searchable strategic asset. Manually achieving the same task would take hours. With n8n and these powerful integrations, updates are just a trigger away.
   
   For startup scouts, researchers, investors, or media analysts, automations like these unlock timely insights at scale—and turn information overload into a competitive edge.
   
   Final Thoughts  
   Automated research workflows are increasingly vital in tracking fast-moving markets. With this sophisticated n8n pipeline, anyone can create their own startup radar powered by the latest AI tools.
   
   Whether you’re mapping market trends or trying to spot the next unicorn, AI-powered no-code tools like this are becoming essential components of modern strategy.
   
   Ready to try it yourself? With n8n, it's never been easier to build your own business intelligence engine.
   
   —
   
   Want to build this yourself? Explore all the steps inside n8n’s visual editor and customize them for your own use case.

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.