Business Situation
- A global private equity firm was seeking to improve the quality and timeliness of its investment decisions by efficiently determining the investment attractiveness of identified targets.
- The client aimed to develop a system that efficiently and automatically extracts key investment details from confidential information memorandums (CIMs), presentations, and teasers to condense them into investment memos using generative artificial intelligence (GenAI) tools.
SGA Approach
- AI-Enabled Research: Utilized state-of-the-art text extraction tools to accurately parse and extract information from PDFs, images, and Word documents.
- Data Structuring: Organized data into nodes and relationships using a graph database, facilitating efficient and meaningful information retrieval.
- Data Analysis: Integrated cutting-edge AI models from Hugging Face for detailed text analysis, including named entity recognition and response generation.
- Vector Embeddings: Measured semantic similarities across documents using vector embedding techniques.
- Data Accuracy and Relevance: Leveraged a large language model (LLM) based on retrieval-augmented generation (RAG) for context-rich text generation.
- Tech-Powered Content Creation: Automated the conversion of extracted information into customized, concise PowerPoint presentations and detailed memos tailored to user specifications using an in-house GenAI tool.
Key Takeaways
- Investment Lifecycle Optimization: Reduction in the time spent creating investment memos by 60% compared to a fully manual process.
- Enhanced Deal Volume: Assessing and analyzing 40% more targets helped enhance the probability of consistent growth across all assets under management (AUM).
