Real-Time Adaptive Support Console

A flexible system that processes streaming information in real-time, categorizes it, and delivers contextually relevant guidance based on enterprise knowledge bases.

Problem Statement

Businesses often face challenges in processing real-time information streams, such as customer service interactions, efficiently. There's a need for systems that can instantly understand the context of the interaction, protect sensitive data like PII, retrieve relevant information from internal knowledge bases, and provide immediate, actionable guidance to personnel. This enhances efficiency, ensures compliance, and improves the quality of service or decision-making. This project demonstrates a solution addressing this need, adaptable beyond customer service.

Diagrams & Visuals

Note: The diagram illustrates the flow from data ingestion through PII masking, classification, knowledge retrieval, and finally, guideline generation presented in the UI.

Demo Video

Note: The video demonstrates the system processing a simulated customer call, showing the real-time masking, classification, document retrieval, and generated agent guidance.

Results & Reflections

This project successfully demonstrates a robust system for real-time information processing and augmentation. Key takeaways and potential results include:

• Potential Efficiency Gains: Real-time guidance could significantly reduce agent response/resolution times in a customer service scenario.
• Improved Compliance: Automated PII masking strengthens data privacy measures and aids compliance efforts.
• Enhanced Consistency: Standardized knowledge retrieval and guideline generation promote consistent service quality.
• Modular Design: The separation of concerns (UI, services, graph logic) allows for easier adaptation to different data streams and business domains (e.g., log analysis, meeting summaries).
• Latency Management: The project highlights the critical importance of managing latency in real-time AI systems, using techniques like efficient chunking and model selection.
• Hybrid Approach: Combining rule-based methods (Regex for specific PII patterns) with model-based approaches (spaCy for NER, LLMs for classification/generation) provides robustness.
• Cost Optimization: The ability to configure different LLM providers allows for balancing performance needs with operational costs.