Case study
Underwriter's Assistant Chatbot
An agentic natural-language interface that lets underwriters query cases, policies, and guidelines in under 30 seconds.
Problem
Underwriters had to switch between multiple tools and databases for every query. Keyword search could not understand natural language or relationships between cases, policies, and rules.
They needed one interface for complex multi-step questions with grounded answers, without manual query chaining.
Solution
We built an agentic chatbot with LangGraph that orchestrates retrieval from MilvusDB and SQL. The agent interprets questions, retrieves context, and chains multi-step tasks in one conversation.
Query refinement and task breakdown happen inside the graph, so underwriters get complete answers instead of running several manual queries.
Outcomes
Underwriters analyze cases in natural language from a single interface.
Multi-tool work that once took minutes now completes in under 30 seconds.
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