AI-based Billing Assistant
ThirdEye Data deployed an intelligent billing assistant chatbot powered by LLMs and a robust data intelligence platform, designed to simplify and streamline billing query resolutions. Leveraging Databricks Vector Search and Microsoft’s Foundation Model endpoint, the solution delivers highly contextual, accurate responses in real time using Retrieval-Augmented Generation (RAG) and the DBRX Instruct model. It provides finance and support teams with a conversational interface to interact with internal billing knowledge, improving response accuracy, speed, and operational efficiency.
THE CUSTOMER
BUSINESS GOALS OR CHALLENGES
Business Goals or Challenges
- Faster Query Resolution: Streamline internal billing support by enabling instant answers to repetitive or complex billing questions.
- Centralized Knowledge Access: Create a unified, searchable internal knowledge base from unstructured and semi-structured documents.
- Intelligent Automation: Use generative AI to improve response accuracy and reduce human effort in resolving billing queries.
- Scalable and Secure Deployment: Implement a managed, production-grade solution that could scale with business needs while ensuring data integrity.
Understanding the Challenges:
- Inadequate Traditional Search: Basic keyword matching lacked the ability to understand query intent or context.
- Unstructured and Scattered Data: Unstructured Billing Data: Billing policies, historical data, and resolution logs existed across disconnected documents and formats.
- Slow Response Times: Support teams were spending significant time manually looking up information, leading to delays and inefficiencies.
- Lack of Context in Automation: Existing bots lacked the ability to understand query context or interpret data across multiple documents.
- Low Confidence in Responses: Previous attempts with basic bots yielded generic answers, reducing trust in automation.
- Scalability Constraints: Prior solutions couldn’t scale to handle increasing load or ensure real-time performance.
Prerequisites and Preconditions:
To develop a robust billing assistant, a few foundational elements were essential:
- Clean and Structured Documents: Internal billing documentation was pre-processed, cleaned, and enriched for knowledge base readiness.
- Databricks Environment: The customer operated on the Databricks Lakehouse platform, facilitating integration with vector search and LLMs.
- Microsoft Foundation Model Endpoint: Enabled the use of advanced generative models via a secure and scalable managed interface.
- Document Embedding Strategy: Required a high-performing vectorization pipeline for embedding and storing content.
- Secure Access Controls: Ensured only authorized teams could access or query billing data through the assistant.
THE SOLUTION
ThirdEye Data built and deployed a real-time AI-based billing assistant using a Retrieval-Augmented Generation (RAG) approach, seamlessly integrated into the customer’s Databricks environment.
Solution Highlights:
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Document Preparation and Knowledge Base Creation: Pre-processed and indexed all relevant internal documents, building a reliable base for the assistant to draw insights from.
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Databricks Vector Search: Used to create embeddings and efficiently search semantically similar documents for each query.
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Real-time RAG-based Chatbot: Built using a Retrieval-Augmented Generation pipeline that dynamically fetches context for each user query.
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Microsoft Foundation Model Endpoint: Powered responses using the DBRX Instruct model hosted on a secure, scalable endpoint.
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LLM-Powered Conversations: Delivered rich, human-like interactions that adapt based on query complexity and historical document patterns.
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Fully Managed Deployment: Implemented using Databricks’ managed services, ensuring smooth scalability, high availability, and operational simplicity.
VALUE CREATED
The AI-based billing assistant significantly improved billing operations by enhancing speed, accuracy, and team productivity:
- Rapid Query Resolution: Reduced average response time to billing queries by over 45%, helping teams resolve issues in real time.
- Increased Self-Service Adoption: Enabled employees to get answers without escalating to finance teams, leading to a 2.5x rise in self-service usage.
- Improved Response Accuracy: Context-rich answers powered by RAG and vector search increased trust and reliability in chatbot outputs.
- Operational Efficiency: Freed up billing and support personnel from repetitive queries, leading to a 31% improvement in team efficiency.
- Seamless Scalability: Serverless, fully managed setup ensured the solution could scale with growing document volumes and query loads.
- Enhanced Knowledge Retention: The internal knowledge base became a living system, continuously expanding with each document indexed.
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