AI Embeddings

What Are AI Embeddings?

Embeddings are the way AI systems understand meaning. When an AI agent needs to find the right answer to a customer question, it doesn't search for exact keyword matches — it converts both the question and all available knowledge into numerical vectors (arrays of numbers) that represent their semantic meaning. Questions and answers with similar meanings end up close together in this vector space, enabling the agent to find relevant information even when the customer uses completely different words than the documentation.

For example, a customer asking "how do I get my money back?" and documentation titled "Refund and Returns Policy" use different words but have similar embeddings because they share the same underlying meaning.

How Embeddings Work in Customer Service

When a customer service AI system is set up, all knowledge base articles, product documentation, and support content are converted into embeddings and stored in a vector database. When a customer sends a message, that message is also converted into an embedding. The system then performs a similarity search — finding the stored content whose embeddings are closest to the customer's query embedding.

This is the foundation of Retrieval-Augmented Generation (RAG) — the most common architecture for grounding AI responses in factual content. The quality of the embeddings directly determines the quality of the retrieval, which in turn determines the accuracy of the AI agent's responses.

Why Embedding Quality Matters

Poor embeddings lead to poor retrieval, which leads to poor answers. If the embedding model doesn't understand that "cancel my account" and "close my subscription" mean similar things, the AI agent will fail to find the right policy document and either hallucinate an answer or escalate unnecessarily.

Technical context: Modern embedding models produce vectors with 768 to 3,072 dimensions, capturing nuanced semantic relationships. Domain-specific embeddings trained on customer service data can outperform general-purpose embeddings by 10-25% on support-specific retrieval tasks.

The Maven Advantage: Knowledge Graph Beyond Embeddings

While embeddings power basic semantic search, Maven AGI goes further with a structured knowledge graph that captures not just semantic similarity but the relationships between concepts, policies, products, and workflows. This means Maven can understand that a question about "returning a damaged item" connects to the returns policy, the warranty terms, the shipping policy, and the customer's specific order history — a level of contextual understanding that flat embedding search alone can't achieve.

Maven proof point: Enumerate achieved a 91% resolution rate by leveraging Maven's knowledge graph to connect a complex web of property data, tenant records, and maintenance workflows — relationships that simple embedding-based search would miss.

Frequently Asked Questions

What's the difference between embeddings and keywords?

Keywords match exact text strings. Embeddings match meaning. A keyword search for "refund" won't find a document about "returning your purchase for credit." An embedding search will, because both concepts have similar semantic meaning in vector space.

Do embeddings need to be updated when content changes?

Yes. When knowledge base content is added, modified, or removed, the corresponding embeddings must be regenerated to reflect the changes. Enterprise AI platforms automate this process so that content updates are immediately reflected in the agent's retrieval capabilities.

Can embeddings work across languages?

Modern multilingual embedding models can represent text from different languages in the same vector space. This means a customer asking a question in Spanish can match against documentation written in English, enabling multilingual support without duplicating content across languages.

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