What are Embeddings?
Embeddings are numeric representations of meaning used for similarity search.
Technical detail
An embedding converts text, image, or other content into vectors that capture semantic relationships. Items with similar meaning are closer together in vector space, even if exact words differ. This enables semantic search, clustering, and recommendations. Embeddings are a core building block for retrieval pipelines.
Why it matters
- Finds relevant content beyond exact keyword matches.
- Improves retrieval quality for AI assistants and copilots.
- Supports deduping and content organization at scale.
- Helps map intent to better workflow actions.
Example
Two product descriptions use different wording but describe the same item type. Embedding search still groups them closely, allowing a merchandising team to detect duplicates and align catalog content.
How Retailbridge relates
Retailbridge uses embeddings to improve matching across catalog, content, and customer workflows. Better retrieval means fewer misses and clearer next actions for teams. Results are evaluated with weekly outcome metrics instead of guesswork.