Building a Scalable Document Querying Chatbot: Corbin Capital

Business Problem

Corbin Capital has implemented a Document Querying Chatbot to facilitate efficient and accurate document querying within its repository. This advanced solution leverages state-of-the-art NLP models and is designed to provide quick access to relevant information, significantly enhancing productivity and decision-making processes. Managing and retrieving data from vast document repositories can be time-consuming and prone to errors in large organizations like Corbin Capital.
Employees often need help finding specific information quickly, leading to delays in decision-making and reduced operational efficiency. The existing manual search processes could have been faster and more efficient, making retrieving precise information from many documents more accessible. The system could also have been more user-friendly, resulting in low adoption rates. Ensuring data security and regulatory compliance during information retrieval was another critical challenge, along with the high resource allocation required for training and manual searches.

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.

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Solution

Corbin Capital addressed these challenges by implementing a Document Querying Chatbot, an AI-driven solution designed to streamline information retrieval. The chatbot provides several key features to enhance the user experience and improve operational efficiency.

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.

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Architecture

User Interaction: Users interact with the system through a React.js user interface, submitting queries and accessing information securely via SSL certificates installed on a Linux VM.
Data Ingestion and Processing: Data from sources like GitHub and other repositories are ingested, processed by a Vision Parser, and stored as raw and processed files.
AI-Powered Services: The application utilizes Azure AI services (OpenAI Vision, Turbo, Embed, and AI Search) for advanced data processing, analysis, and efficient querying.
Data Storage and Management: Azure Cosmos DB stores and retrieves structured data, while an App for SharePoint integrates for enhanced document management and collaboration.
Security and Compliance: User authentication and authorization ensure secure access, with user interactions and feedback logged for continuous improvement and auditing.
Scalability and Efficiency: The architecture supports scalable and automated processes, managed by a scheduler to ensure timely task execution and efficient resource utilization.
Continuous Improvement: Logged user interactions and feedback are analyzed to refine and enhance the chatbot’s performance over time, ensuring it adapts to evolving user needs.

Outcomes

0Faster Answers, Better Decisions

Chatbot provides quick access to info, streamlining workflows and empowering informed choices.

0Shared Knowledge, Stronger Teams

Breaks down information silos, fostering knowledge sharing and collaboration across teams.

0Reduced Errors, Increased Accuracy

Eliminates human error in document searches, leading to more reliable data and accurate decisions.

Technology Stack