What is semantic search in AI Agents? A Guide for product managers in insurance

Semantic search is a way for AI agents to find information by meaning, not just by matching exact words. It lets an agent understand that “water damage claim,” “burst pipe claim,” and “leak in the ceiling” may refer to the same customer need.

How It Works

Traditional search looks for keyword overlap. If a claims note says “escape of water” and the user asks for “pipe burst,” keyword search may miss it unless those exact words appear.

Semantic search uses embeddings, which are numeric representations of text that capture meaning. Think of it like a very good claims adjuster who has seen thousands of cases and can recognize that different descriptions point to the same underlying event.

A simple analogy: imagine sorting mail in an insurance office.

• •Keyword search is like sorting letters only by exact street name.
• •Semantic search is like a receptionist who understands context, abbreviations, and common phrasing, so they can still route “123 Main St Apt 4B” even if someone wrote “Unit 4B, 123 Main.”

In an AI agent, semantic search usually works like this:

• •A document, policy clause, FAQ, or claim note is converted into an embedding.
• •A user question is also converted into an embedding.
• •The system compares the vectors and retrieves the most similar content.
• •The agent uses that retrieved context to answer or take action.

For product managers, the key point is this: semantic search is what makes an AI agent useful when users do not phrase things exactly the way your knowledge base does.

Why It Matters

• •

  Customers do not use your internal language.
  A policy document may say “accidental damage from escape of water,” while customers say “my ceiling leaked.” Semantic search bridges that gap.

• •

  It improves self-service resolution.
  Agents can retrieve the right FAQ, policy clause, or claims workflow even when the wording varies. That reduces handoffs to human support.

• •

  It makes AI agents more reliable in messy real-world inputs.
  Insurance questions often come in with typos, partial details, or informal language. Semantic search handles that better than exact-match lookup.

• •

  It supports better decision-making inside workflows.
  An underwriting assistant can pull relevant guidelines based on intent, not just terms. That helps teams move faster without forcing users to memorize policy terminology.

Real Example

Let’s say you are building an AI claims assistant for home insurance.

A customer types:

“My upstairs bathroom pipe burst overnight and damaged the ceiling below. Is this covered?”

A keyword-based system might look for “pipe burst” and miss documents labeled:

• •escape of water
• •plumbing failure
• •sudden accidental discharge
• •internal water damage

A semantic search layer changes that. The agent retrieves:

• •the relevant policy section on accidental water damage
• •the claims intake checklist for burst pipes
• •prior similar claim examples
• •any exclusions around maintenance neglect or gradual leakage

The agent then responds with something like:

“Based on your description, this appears related to sudden water damage from a burst pipe. I’ve pulled the relevant coverage section and claims steps. Coverage may depend on whether the leak was sudden versus gradual.”

That is the practical value: the agent does not need perfect wording from the customer. It needs to understand intent well enough to fetch the right source material.

For product managers, this matters because semantic search affects:

Related Concepts

• •

  Embeddings
  The vector representation used to compare meaning across text.

• •

  Vector database
  The storage layer that holds embeddings and returns similar items fast.

• •

  Retrieval-Augmented Generation (RAG)
  A pattern where an LLM first retrieves relevant content through semantic search before generating an answer.

• •

  Hybrid search
  Combines keyword search and semantic search so you get both precision and meaning-based recall.

• •

  Re-ranking
  A second pass that improves retrieval quality by scoring the top results more carefully before they reach the agent.

Keep learning

• •The complete AI Agents Roadmap — my full 8-step breakdown
• •Free: The AI Agent Starter Kit — PDF checklist + starter code
• •Work with me — I build AI for banks and insurance companies

By Cyprian Aarons, AI Consultant at Topiax.