Unleashing agentic AI and retrieval-augmented generation for insurance

The amount of time insurance teams spend searching for, evaluating and authenticating information is staggering. From historical policy documents, notes and images to customer service logs, claims files and regulatory requirements, such tedious work strains the resources of overextended teams in customer service, sales, claims and underwriting.

What if there were a better way? Retrieval-augmented generation (RAG) combines two AI capabilities (retrieval and generation) to help insurers find the answers they need faster and with more reliability.

RAG pairs semantic search with large language models (LLMs) to retrieve relevant information from unstructured data. Acting like a digital coworker, it provides context-aware GenAI responses based on approved, preselected sources – often internal and proprietary. Then it delivers citation-backed, source-grounded responses with results that are:

• Trustworthy and explainable.
• Accurate.
• Faster and less costly than traditional, more manual methods.

Whether developing financial insights, investigating claims or delivering customer service, the RAG approach uses timely, trustworthy business information to help insurers save time, gain clarity and build confidence in the decisions they make every day.