BUSINESS SITUATION
A global PE firm was seeking to automate the data analysis from a large pool of documents spanning across various work products.
ENGAGEMENT
- We developed a system that efficiently extracts key investment details from CIMs, presentations, and teasers, condensing the information into concise 4-page memos and 4-5 slide PowerPoint presentations
- Our solution facilitates rapid and focused analysis of extensive documents.
SGA APPROACH
- Document parsing: Utilized our state-of-the-art text extraction tools to accurately parse and extract information from PDF, images and Word documents
- Data structuring: Organized data into nodes and relationships using 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 employing vector embedding techniques
- Data accuracy and relevance: Leveraged RAG-based LLM, Retrieval-Augmented Generation for context-rich text generation
- Creating presentations & memos: Automated conversion of extracted information into customized, concise PowerPoint presentations and detailed memos tailored to user specifications
- Content addition: Included sections on company overview, SWOT, key risks, etc.
Streamlining key insight extraction from deal documents with Gen AI
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BENEFITS & OUTCOME
- Optimization: Documents of varying lengths and complexities handled with consistent performance
- Enhanced user interaction: Summarized documents into dynamic PowerPoint presentations with intuitive interactivity
KEY TAKEAWAYS
- Reduced the time and effort required to analyze and summarize extensive documents, enabling faster decision-making in investment scenarios
- Maintained high level of relevance and quality for content generated through AI extraction tools
- Solution adaptable to specific user queries and needs, providing tailored summaries
