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 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.

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.

• 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.