Startup Funding Research Automation with Claude, Perplexity AI, and Airtable
How it works
This intelligent workflow automatically discovers and analyzes recently funded startups by:
- Monitoring multiple news sources (TechCrunch and VentureBeat) for funding announcements
- Using AI to extract key funding details (company name, amount raised, investors)
- Conducting automated deep research on each company through perplexity deep research or jina deep search.
- Organizing all findings into a structured Airtable database for easy access and analysis
Set up steps (10-15 minutes)
- Connect your news feed sources (TechCrunch and VentureBeat). Could be extended. These were easy to scrape and this data can be expensive.
- Set up your AI service credentials (Claude and Perplexity or jina which has generous free tier)
- 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