30% Faster Data Processing with LLM-Powered Financial Data Retrieval

LLM functionality was enhanced beyond basic text processing, enabling faster financial data retrieval, precise insights, and improved decision-making.

30%

Faster document processing: Achieved through parallelized data pipelines.

40%

Faster chatbot responses: Improved query response times.

25%

Better data accuracy: Enhanced metadata extraction boosted retrieval precision.

IndustryBFSI
Company typeEnterprise
CountryUnited States
Services usedTuring Intelligence
30% Faster Data Processing Transforming Financial Data Retrieval with Advanced LLM Solutions

About the client

A leading global investment management firm specializing in financial research and market intelligence. The company leverages AI-driven analytics and data processing solutions to provide high-quality insights for investment decision-making.

The problem

The client faced significant challenges in handling financial research data, leading to inefficiencies in retrieval and analysis. Key issues included:

  • Slow data ingestion pipelines: Inefficient handling of large, unstructured datasets like PDFs caused processing delays.
  • LLM integration issues: The existing system struggled to integrate large language models, resulting in slow and inaccurate query responses.
  • Lack of metadata and search optimization: Limited metadata generation and inefficient search keys made accurate and fast data retrieval difficult.

These inefficiencies disrupted productivity, slowed decision-making, and made it challenging to meet the demand for precise data retrieval in high-paced financial environments.

The solution

Turing implemented a comprehensive optimization of the client’s financial data retrieval system, leveraging advanced LLM integration and pipeline enhancements.

  • Optimized data ingestion pipelines: Introduced parallel processing for large PDFs, significantly improving document ingestion speeds.
  • Metadata enhancement: Developed custom components to extract and generate rich metadata, improving search accuracy and retrieval precision.
  • Real-time processing: Enabled real-time document processing, enhancing query response times for the client’s chatbot interface.
  • LLM integration: Leveraged Python, Langchain, Azure OpenAI, and Unstructured.io to streamline data flow and improve chatbot interactions.

The result

  • 30% faster document processing: Optimized data pipelines improved processing efficiency.
  • 40% faster chatbot responses: Improved query handling led to better user experience and response accuracy.
  • 25% better data accuracy: Advanced metadata extraction significantly boosted retrieval precision.

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