Automated Company Funding Research

Target Audience

• Entrepreneurs and Startups: Those seeking funding can use this workflow to gather information about potential investors and funding opportunities.
• Investors: Individuals or firms looking for insights on companies that have recently raised funds, including detailed analyses of their business models and market positions.
• Researchers and Analysts: Professionals needing comprehensive data on funding rounds and company performance for market research or competitive analysis.
• Journalists and Content Creators: Writers looking for reliable sources and structured information about recent funding events in the tech industry.

Problem Solved

This workflow addresses the challenge of obtaining structured, comprehensive information about companies that have recently announced funding rounds. It automates the process of collecting data from multiple sources, parsing it, and organizing it into a format that is easy to analyze and understand. By integrating various APIs and data parsing techniques, it eliminates the manual effort required to research and compile this information, saving users significant time and effort.

Workflow Steps

1. Manual Trigger: The workflow begins when a user clicks the 'Test workflow' button to initiate the process.
2. Fetch Latest Articles: It sends HTTP requests to TechCrunch and VentureBeat to retrieve the latest articles related to funding rounds.
3. Parse XML Data: The retrieved XML data is parsed to extract article URLs.
4. Split Articles: The workflow splits the articles into individual items for further processing.
5. Filter Articles: It filters articles based on specific keywords (e.g., 'raise') to retain only relevant content.
6. Extract Article Content: For each relevant article, it fetches the full HTML content and parses it to extract the title and text.
7. Structured Data Extraction: The extracted text is processed to identify key information such as company name, funding round, funding amount, and lead investors using advanced language models.
8. Research Company Website: It uses a language model to find the official website of the company mentioned in the articles.
9. Merge Data: All extracted data is merged into a unified dataset for comprehensive analysis.
10. Store Data in Airtable: Finally, the structured information is saved to Airtable, allowing for easy access and further analysis.