Global Risk Management Insurance Firm
Building a GenAI Chatbot for Faster Document Retrieval
What if support teams could deliver faster, more accurate service, and better customer experiences, all while reducing queue times?
Challenge + Opportunity
A global risk management insurance firm specializing in personal devices and consumer electronics faced a growing operational challenge: managing and retrieving support documentation for an ever-expanding catalog of thousands of devices. With more than one million support documents—ranging from OEM manuals to how-to guides—customer service representatives (CSRs) spent an excessive amount of time manually searching multiple repositories to locate relevant materials. This inefficiency led to longer response times, higher support costs, and an inconsistent customer experience.
The firm recognized the need to modernize its support operations and saw an opportunity to leverage AI to streamline document retrieval and enhance productivity. The goals were to reduce the time CSRs spend on document retrieval and prepare for a future rollout to end users that would deflect basic support queries from live channels.
Integrating Document Sources With AI-Powered Search Tools
We designed and delivered a GenAI-powered chatbot built on Azure OpenAI and Azure AI Search. We began with a targeted implementation covering 25 device types, refining the experience and validating the architecture before scaling to a production-ready solution indexing more than 10,000 devices.
The chatbot is engineered to understand natural language queries, retrieve support documents from both Salesforce-curated repositories and the firm’s proprietary system, and deliver context-aware responses tailored to each device type. Logic was applied to prioritize internal documentation, while allowing for fallback to external resources like OEM websites and YouTube when needed.
We used Azure Data Factory and custom Python scripts to develop a robust back-end pipeline to vectorize and ingest new content into a scalable Azure AI Search vector database. This ensures content remains searchable, accurate, and up to date.
To further enhance the experience, we built a structured orchestration layer that enables filtering capabilities for user queries (e.g., retrieving only video content when requested) and memory-aware response generation using prompt engineering techniques.
The solution was deployed with CI/CD pipelines, integrated with Cosmos DB for conversation logging, and aligns with our client’s strict governance and compliance standards.
This AI-driven transformation enables the client to scale support operations strategically, with a focus on the future, extending the solution to end users and reducing live channel volume through routine query deflection.
Results
Improving Support Operations With a Scalable AI Solution
The chatbot will be used by CSRs to more efficiently support customers and improve the customer experience. It is expected to significantly reduce the time spent searching for troubleshooting materials, enabling CSRs to resolve issues faster. This will decrease queue times and ensure more consistent and accurate responses across the support team. Additionally, it will allow CSRs to focus on more complex, high-value interactions.
This AI-driven transformation is intended to position the client to scale support operations more strategically, with the future goal of extending the solution to end users and unlocking even greater value by deflecting routine queries from live channels.
