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85% Accuracy, 80% User Satisfaction Boost

Knowledge Retrieval for Efficient Customer Support and Technical Services

Goal

Enhance knowledge retrieval using AI to enable better customer support and deliver precise answers from a diverse, evolving knowledge base.

Solution

Leveraged NLP & RAG to develop a dynamic knowledge retrieval system interpreting information from various sources for accurate, context-specific answers.

Result

Reduced average information retrieval time from over 20 minutes to 5 minutes, improved response accuracy to 85%, increased user satisfaction by 80%.

Business Introduction

The client was a customer support and managed IT services provider across various industries, including technology, healthcare, and finance. With a vast customer base spanning multiple regions, they faced a constant influx of diverse inquiries from end-users, clients, and partners. Delivering prompt and accurate responses was crucial to maintaining high levels of customer satisfaction and fostering long-term relationships. However, their knowledge base was extensive, dynamic, and stored in various formats across multiple locations, posing significant challenges in efficient knowledge retrieval.

Dynamic, Distributed Knowledge Retrieval

Knowledge base was highly dynamic varied across users and departments
Information stored in multiple formats and locations, challenging efficient retrieval and processing
Maintaining accuracy and relevance as the knowledge base evolved

AI-Powered Dynamic Knowledge Retrieval System

Sophisticated AI systems integrate with various data sources like files, databases, websites & cloud storage.

NLP & heuristics to interpret and extract relevant information

Set up Retrieval Augmented Generation pipeline and set priority queuing of documents.

Used OpenAI GPT 4 Model for generation and Milvus for vector storage

User-friendly interface for querying and receiving precise, context-specific answers

Human review flagging for uncertain responses, enabling continuous improvement

Sophisticated AI systems integrate with various data sources like files, databases, websites & cloud storage.
NLP & heuristics to interpret and extract relevant information
Set up Retrieval Augmented Generation pipeline and set priority queuing of documents.
Used OpenAI GPT 4 Model for generation and Milvus for vector storage
User-friendly interface for querying and receiving precise, context-specific answers
Human review flagging for uncertain responses, enabling continuous improvement

Multiformat Integration, Adaptive Learning, User-Friendly Interface

Integration with diverse data sources and formats
Intelligent information extraction and interpretation by using a mix of document chunking techniques to generate better vector embeddings
Context-aware answer retrieval and delivery
Role-based access control for both features and data.
Custom Prompt Injection configurable by admin
Ability to retrieve source knowledge files for all user queries

Measurable Results and Impact

Reduced average information retrieval time from 20 minutes to under 5 minutes (75% reduction)
Increased user satisfaction by 80%

Key Achievements

Developed AI-powered dynamic knowledge management system
Integrated multiformat data sources for comprehensive knowledge coverage
Delivered precise, context-aware answers through intelligent retrieval
Improved operational efficiency, accuracy, and customer satisfaction

Conclusion

Through our AI-driven dynamic knowledge retrieval system, we revolutionized the client’s knowledge retrieval process, enabling faster, more accurate, and user-friendly information delivery. By integrating diverse data sources, leveraging advanced AI techniques, and implementing adaptive learning, the system could dynamically interpret and extract relevant knowledge, providing precise, context-specific answers. The result was a significant reduction in retrieval time, improved response accuracy, and increased user satisfaction, ultimately enhancing the client’s operational efficiency and customer experience.

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