Streamlining client support: Azure and OpenAI transforming the insurance industry

The knowledge access challenge

Over time, the company accumulated thousands of internal documents covering policies, products, procedures, and customer guidance. While this information was essential to daily operations, it was stored across multiple formats and locations. When clients asked questions, support teams often had to search through lengthy documents to find accurate answers.

This process worked when inquiry volumes were low. As demand increased, it became harder to keep response times short. Clients expected quick and reliable answers, while support teams struggled with repetitive searches that added little value to their work. A simple question about policy coverage could require checking multiple documents, comparing versions, and verifying which information applied to specific situations. The problem wasn’t lack of information but the difficulty of accessing it efficiently when it mattered most.

Building a centralized support workflow

To address this challenge, the company worked with Data Science Dojo to create an application that centralized access to its internal knowledge. The goal was to reduce the time spent searching for information and make it easier for both staff and clients to find accurate answers.

The solution focused on indexing company documents such as policies, procedures, product manuals, and support articles using Azure AI Search. Once indexed, this content could be queried using natural language, allowing users to ask questions and receive relevant answers drawn directly from existing documentation. Instead of remembering which document contained which information, support staff could simply ask questions the way clients asked them.

The system was designed to surface the most relevant information quickly, presenting answers with references to source documents for verification. This approach maintained accuracy while dramatically reducing search time. Rather than replacing existing support processes, the system complemented them by handling routine information retrieval. This allowed support teams to rely on a consistent source of truth while maintaining control over more complex client interactions. Support staff could now spend their expertise on interpreting policies for unique situations rather than hunting for basic information.

Improving day-to-day support operations

After implementation, the application became part of daily support workflows. Time previously spent navigating documents was reduced by approximately 70%, enabling faster responses to common questions. Support teams were able to handle a higher volume of inquiries without increasing workload, while clients experienced clearer and more timely communication.

The centralized knowledge base also improved consistency. Answers were grounded in official documentation, reducing the risk of miscommunication and ensuring that clients received reliable information regardless of how or when they reached out for support. New team members could get up to speed faster, relying on the system to guide them to accurate information while they built their own expertise. The company saw measurable improvements in client satisfaction scores and a noticeable reduction in escalations caused by unclear or incomplete initial responses.