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Startup Funding Research Automation with Claude, Perplexity AI, and Airtable

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Created by: Julian Kaiser || jksr

Julian Kaiser

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Last update 3 months ago

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Startup Funding Research Automation with Claude, Perplexity AI, and Airtable

How it works

This intelligent workflow automatically discovers and analyzes recently funded startups by:

  1. Monitoring multiple news sources (TechCrunch and VentureBeat) for funding announcements
  2. Using AI to extract key funding details (company name, amount raised, investors)
  3. Conducting automated deep research on each company through perplexity deep research or jina deep search.
  4. Organizing all findings into a structured Airtable database for easy access and analysis

Set up steps (10-15 minutes)

  1. Connect your news feed sources (TechCrunch and VentureBeat). Could be extended. These were easy to scrape and this data can be expensive.
  2. Set up your AI service credentials (Claude and Perplexity or jina which has generous free tier)
  3. Connect your Airtable account and create a base with appropriate fields (can be imported from my base) or see structure below.
    Airtable Base

Structure Funding Round Base

Field Name Data Type Description
website_url String URL of the company website
company_name String Name of the company
funding_round String The funding stage or round (e.g., Series A, Seed, etc.)
funding_amount Number The amount of funding received
lead_investor String The primary investor leading the funding round
market String The market or industry sector the company operates in
participating_investors String List of other investors participating in the funding round
press_release_url String URL to the press release about the funding
evaluation Number The company's valuation

Structure Company Deep Research Base

Field Name Data Type Description
website_url String URL of the company website
company_name String Name of the company
funding_round String The funding stage or round (e.g., Series A, Seed, etc.)
funding_amount Number The amount of funding received
currency String Currency of the funding amount
announcement_date String Date when the funding was announced
lead_investor String The primary investor leading the funding round
participating_investors String List of other investors participating in the funding round
industry String The industry sectors the company operates in
company_description String Description of the company's business
hq_location String Company headquarters location
founding_year Number Year the company was founded
founder_names String Names of the company founders
ceo_name String Name of the company CEO
employee_count Number Number of employees at the company
total_funding Number Total funding amount received to date
total_funding_currency String Currency of total funding
funding_purpose String Purpose or use of the funding
business_model String Company's business model
valuation Object Company valuation information
previous_rounds Object Information about previous funding rounds
source_urls String Source URLs for the funding information
original_report String Original report text about the funding
market String The market the company operates in
press_release_url String URL to the press release about the funding
evaluation Number The company's valuation

Notes

I found that by using perplexity via open router, we lose access to the sources, as they are not stored in the same location as the report itself so I opted to use perplexity API via HTTP node.

For using perplexity and or jina you have to configure header auth as described in Header Auth - n8n Docs

What you can learn

  • How to scrape data using sitemaps
  • How to extract strucutred data from unstructured text
  • How to execute parts of the workflow as subworkflow
  • How to use deep research in a practical scenario
  • How to define more complex JSON schemas