Streamlined RFI Response Workflow for a Fintech Company

Our innovative web application streamlines the process of responding to Requests for Information (RFIs) by leveraging a Knowledge-based Question Answering (QA) System. Utilizing the wealth of knowledge contained within previous RFIs, our system embeds this information into a vector database. When a new RFI is submitted, our QA system retrieves similar QA pairs from the knowledge base, generating accurate and relevant answers efficiently.

Key features include:

• Automatically fills RFIs with optimal answers rapidly.
• Seamless integration of Large Language Models (LLMs) for natural language understanding and generation.
• Utilizes RAG methodology for generating comprehensive and context-rich responses.
• Implements various RAG approaches including Naïve RAG, Window Sentence RAG, and Hierarchical RAG to cater to specific data requirements.
• The system was deployed on AWS EC2 servers

Results:

• Reduces RFI screening and filling time from months to a fraction, ensuring timely project execution and adherence to timelines.
• Minimizes errors in information extraction and response generation, thereby improving project quality and client satisfaction while reducing operational costs and boosting productivity.