40% Faster Response Times: Optimizing Chatbots for Real-Time Insights

Optimized chatbot infrastructure improved real-time financial data retrieval, reducing delays, enhancing query execution, and ensuring faster, more reliable insights for traders.

40%

Faster responses: Chatbot response times improved from 6-8 seconds to 3-5 seconds.

30%

Higher user satisfaction: Notable among traders needing real-time insights.

50%

More scalability: System now handles 50% more concurrent users.

IndustryBFSI
Company typeEnterprise
CountryGlobal
Services usedTuring Intelligence
40% Faster Response Times Optimizing Chatbot Efficiency for Real-Time Financial Insights OG

About the client

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

The problem

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.

The solution

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.

The result

  • 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.

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