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

Embeddings are numerical representations of text, images, or other data that capture meaning in a form AI systems can compare mathematically. In AI agents, embeddings let the agent find items that are semantically similar even when the words are different.

How It Works

Think of embeddings like a map of meaning.

If you put “car insurance claim,” “auto accident report,” and “vehicle damage request” on that map, they land near each other because they mean related things. If you put “homeowners policy renewal” somewhere else, it sits in a different part of the map.

For a product manager in insurance, the useful mental model is this:

• •Words are not stored as plain text alone.
• •Each item gets turned into a vector, which is just a list of numbers.
• •Similar meanings produce vectors that sit close together.
• •The AI agent uses those distances to decide what content is relevant.

A simple analogy: imagine sorting documents in a filing cabinet not by exact labels, but by “what they’re about.” A customer might say:

• •“My car was hit in a parking lot”
• •“I need to report bumper damage”
• •“Someone scraped my vehicle”

A keyword system may treat these as different phrases. An embedding-based system knows they belong to the same intent cluster.

Under the hood, this usually works like:

1. •The agent receives a question or task.
2. •The text is converted into an embedding vector.
3. •That vector is compared against stored embeddings for documents, FAQs, policy clauses, claim notes, or prior cases.
4. •The nearest matches are returned to the agent.
5. •The agent uses those matches to answer, route, summarize, or take action.

This is why embeddings matter so much for retrieval-augmented generation, semantic search, and intent matching. They give agents memory with context instead of memory with exact words.

Why It Matters

For product managers in insurance, embeddings are not just an engineering detail. They change what your AI agent can do reliably.

• •

  Better search across messy insurance language

  Customers and adjusters rarely use the same wording. Embeddings help the agent match “water leak under sink” with “plumbing damage” or “escape of water.”

• •

  Improved self-service and deflection

  A policyholder asking about “how long I have to submit receipts after theft” should get the right claims guidance even if they don’t use formal policy terms.

• •

  More accurate routing

  Embeddings help classify whether a request belongs to claims, underwriting, billing, fraud review, or complaint handling without relying on rigid keywords.

• •

  Smarter knowledge retrieval

  Agents can pull the right clause from long policy documents, internal playbooks, and SOPs instead of hallucinating from general model knowledge.

For PMs, the business value is straightforward: fewer dead-end searches, faster resolution times, better containment rates in support channels, and less manual triage by operations teams.

Real Example

Let’s say you’re building an AI claims assistant for auto insurance.

A customer uploads this message:

“I was rear-ended at a traffic light and my trunk won’t close properly.”

The agent needs to decide what to do next. Without embeddings, it may depend on exact keywords like “collision,” “accident,” or “rear-end.” That breaks when customers describe events in natural language.

With embeddings:

• •The customer message is converted into a vector.
• •The system compares it against vectors for:
  • •claims intake scripts
  • •FNOL categories
  • •repair estimate FAQs
  • •towing eligibility rules
  • •rental car coverage guidance
• •It finds that this message is semantically close to:
  • •collision claim intake
  • •vehicle damage assessment
  • •roadside assistance eligibility

The agent then responds with something like:

• •confirm date and location of loss
• •ask whether police report exists
• •check if vehicle is drivable
• •offer towing instructions if needed

That means the assistant does not need exact phrasing from the user. It understands intent through meaning similarity.

From a product standpoint, this reduces friction in first notice of loss flows. It also improves consistency because every customer gets routed through the same knowledge base rather than depending on how they phrase their problem.

Related Concepts

• •

  Vector database
  Stores embeddings so the agent can search for nearest matches quickly at scale.

• •

  Semantic search
  Search based on meaning rather than exact keyword overlap.

• •

  RAG (Retrieval-Augmented Generation)
  Uses embeddings to fetch relevant context before the model generates an answer.

• •

  Intent classification
  Assigns user requests to categories like claims, billing, underwriting, or service.

• •

  Similarity scoring
  Measures how close two embeddings are so the system can rank results.

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.