RAG for Customer Service Management

CData provides a wide range of data connectivity drivers, connectors across different platforms, including ADO.Net, ODBC, JDBC, and more. Each driver comes with extensive documentation covering installation, configuration, troubleshooting, and advanced usage.

One of the key challenges for CData’s customer support team is navigating this vast documentation repository efficiently. Support agents need to fully understand the documentation to assist customers effectively, which requires significant training time and continuous knowledge updates as the documentation evolves.

To address this, Nester Labs developed a Retrieval-Augmented Generation (RAG) system that enables support teams and users to instantly retrieve relevant information from the documentation. By leveraging AI-driven search and contextual understanding, the solution reduces dependency on manual searching, minimizes training efforts, and improves response accuracy, leading to enhanced customer experience and operational efficiency.

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Objective

Develop a Proof of Concept (PoC) for a Retrieval-Augmented Generation (RAG) system to enhance user interaction with CData’s driver documentation across multiple editions (ADO.Net, ODBC, JDBC, etc.).

Challenges

• Processing and structuring raw HTML documentation for effective retrieval.
• Ensuring the system can accurately answer edition-specific queries.
• Scalability considerations for future deployment.

Solution

Nester Labs designed and implemented a RAG-based AI system that:

1. Data Processing – Built an ingestion pipeline to extract, transform, and semantically chunk documentation before storing it in a vector database with metadata for efficient retrieval.
2. Query Handling – Developed a REST API that leverages OpenAPI models to return precise answers based on indexed documentation.
3. Deployment & Evaluation – Packaged the system in Docker containers and deployed it on Azure, iterating on chunking and vectorization strategies for performance tuning.

Results

• Successfully indexed CData’s documentation into a structured format for intelligent retrieval.
• Developed a scalable system architecture, enabling future enhancements.
• Delivered a PoC with clear documentation, ensuring CData can extend and maintain the system independently.

Key Deliverables

• Fully functional RAG system with ingestion, querying, and retrieval capabilities.
• Infrastructure setup on Azure, including NoSQL storage and vector databases.
• Comprehensive documentation and handover of artifacts to CData for future scalability.

Conclusion

This engagement validated the feasibility of using AI-driven search and retrieval for technical documentation, setting the foundation for enhanced knowledge access in enterprise environments.

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