Embeddings

A practical example

Example: a content-recommendation tool embeds every blog post and each visitor's query, then surfaces posts whose vectors sit nearest to the query's vector, catching synonyms a keyword filter would miss.

What to evaluate before investing

• Ask which embedding model the vendor uses and whether you can swap or re-embed content if you change models later.
• Test semantic matching with your own synonyms and jargon — does the system link your industry terms to the intended records?
• Ask how embeddings are kept current when content or CRM records are edited, and whether re-embedding is automatic.

Limitations and tradeoffs

Tradeoff: embeddings capture similarity of meaning, not factual correctness or recency, so a semantically close match can still be outdated or wrong. Model choice also matters — vectors from different models are not comparable.