Business Situation
- A US-based private equity firm wanted to enhance the quality and quantity of shortlisted investable targets to gain a competitive edge by expediting the screening process and reducing the time to decision.
- The client aimed to achieve this through a web crawler powered by artificial intelligence (AI) that would gather and analyze potential target companies and ensure seamless access to internal systems and third-party application programming interfaces (APIs), creating a central repository of the target universe.
SGA Approach
Internal Data Sources: Determined internal databases, warehouses, and external data sources, including third-party tools and software systems, for comprehensive and robust data gathering.
External Data Sources: Aggregated publicly available data from government databases, industry reports, academic publications, and market research studies.
AI-Powered Crawler Development:
- Developed a scalable web crawler to handle large volumes of data from diverse sources.
- Integrated AI and machine learning (ML) to dynamically navigate and extract relevant information.
- Created algorithms to parse various web formats and used natural language processing (NLP) for contextual data extraction.
- Implemented keyword matching and customizable lists for targeted data gathering.
Key Takeaways
- Efficiency: Saved over 50% of screening research time.
- Better Quality of Screened Targets: Increased volume of relevant, investable targets by 40% compared to the traditional process.
- Enhanced Business Impact: Improved effectiveness of the front-end team by enabling greater focus on complex analysis and due diligence.
