A global financial services firm that offers online trading, investment, wealth management and asset management services for institutional, private and retail clients.

The client’s chatbot, essential for delivering real-time financial insights to traders, faced performance challenges that impacted efficiency and user experience. Key issues included:

• Slow response times: Chatbot queries took 6-8 seconds, leading to frustration among users.
• Inefficient system design: Redundant backend processes and poor caching created performance bottlenecks.
• Credibility risks: Delays in chatbot responses lowered user trust and system reliability.

These inefficiencies disrupted trading operations, making real-time data access unreliable and inconsistent.

Turing implemented a multi-faceted optimization strategy to enhance chatbot performance, reduce response times, and improve scalability while ensuring real-time financial insights for traders.

Code optimization:

• Refactored backend processes to eliminate redundancy and improve execution speed.
• Parallelized database operations and optimized query handling to reduce response latency.

Advanced caching strategies:

• Integrated Azure Tables to store frequently accessed data, reducing retrieval time.
• Implemented data preloading techniques to improve chatbot responsiveness.

Optimized deployments:

• Integrated Azure-based GPT-3 and GPT-4 models for query intent extraction and context augmentation.
• Used Azure DevOps for CI/CD pipelines, ensuring seamless updates without system downtime.

Efficient resource management:

• Introduced token management strategies to optimize API usage and reduce operational costs.
• Implemented scalability enhancements to support higher concurrent user loads without performance degradation.

Database and infrastructure improvements:

• Integrated Azure Cosmos DB for scalable data storage, ensuring faster session data retrieval.
• Used SQL databases for metadata storage, optimizing query resolution and response generation.

• 40% faster response times: Optimized processing reduced chatbot query times from 6-8 seconds to 3-5 seconds.
• 30% higher user satisfaction: Improved chatbot efficiency enhanced trader experience and usability.
• 20% server load reduction: Optimized caching strategies enhanced system efficiency.
• 30% cost savings: Reduced token access costs for GPT-3 and GPT-4, lowering operational expenses.
• Higher scalability: The system now supports 50% more concurrent users, enabling better performance under high loads.