Overview

This project addressed the challenge of efficiently analysing complex contract data for quant funds.

Problems

The primary problem this project addresses is the challenge of efficiently analysing complex contract data for quant funds. With large volumes of data, it is difficult to extract actionable insights and identify important patterns. This project aims to automate the process of analysing contract data by using an LLM to generate SQL queries, process the data, and present insights in a clear, natural language format.

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Solution

FlowFoundry's solution involves training a Large Language Model (LLM) to understand contract data structures and financial terminology relevant to quant funds. The LLM is capable of generating SQL queries to extract data from contracts, which it then analyzes to produce meaningful results. These results are presented in natural language, making them accessible to non-technical stakeholders. The system also has the capability to create advanced structures such as stratification tables, allowing users to get a deep understanding of the data and gain strategic insights.

Key Aspects of the Project

1. Data Extraction and Preparation We gather contract data from various sources and preprocess it for analysis. This includes cleaning and normalizing the data to ensure consistency, allowing the LLM to better understand the structures and relationships within the dataset.
2. Model Training for SQL Generation The LLM is trained to generate accurate SQL queries based on user prompts. By understanding the specific needs of a quant fund, the model can craft complex queries to extract valuable data, enabling precise and insightful analysis.
3. Natural Language Reporting Once the data is analysed, the LLM summarises the results in natural language, providing easy-to-understand descriptions of key findings. This feature is designed to make complex data analysis accessible to decision-makers without requiring deep technical expertise.
4. Advanced Data Structuring The system can generate advanced data structures, such as stratification tables, to provide a detailed view of different segments of the contract data. These structures help in identifying patterns and trends, supporting better decision-making and strategy formulation.

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