Building a Scalable Document Querying Chatbot: Corbin Capital
Business Problem
About Corbin Capital
Corbin Capital Partners is a woman-led investment firm specializing in alternative assets like hedge funds and credit investments. They manage client money through various methods and prioritize client satisfaction. As of April 1, 2024, they manage $9.1 billion in assets.
Solution
GoML’s consulting team sat down with different portfolio managers to understand various inputs & features for each portfolio, as well as the data backing them:
•A GPT – 4 Turbo powered agent ingested all the portfolio documents (structured, unstructured, images, video & audio transcriptions)
•A RAG based engine extracted all the data from these multiple sources and allowed the portfolio managers to interact with this data realtime, to gain inisights, build clear comparisons, understand the portfolio structure, perform complex mathematical functions and get detailed portfolio reports
User Authentication and Authorization
The system ensures that only authenticated and authorized users can access the document repository, maintaining strict security protocols and compliance with regulatory standards. This controlled access helps protect sensitive information and provides an audit trail for monitoring and review.
Natural Language Processing (NLP)
The chatbot employs advanced NLP techniques, enabling users to interact with it using natural language queries. This feature allows users to type questions or statements in everyday language, making the system highly intuitive and accessible.
Efficient Information Retrieval
Once a user submits a query, the chatbot processes it, retrieving the most relevant information from the indexed and processed documents. This rapid retrieval process significantly reduces employee’s time searching for information, thus enhancing productivity.
User Interaction Logging
The system logs all user interactions and feedback, which are then analyzed to refine and improve the chatbot’s performance continually. This ongoing learning process ensures that the chatbot becomes more accurate and efficient over time, adapting to users’ evolving needs.
Scalable and Automated Processes
The chatbot’s implementation automates many of the manual processes previously required for document searching. Its scalable architecture allows it to handle a growing volume of queries and documents without compromising performance, thereby increasing overall operational efficiency.
