Do I need embeddings to dedupe records across systems?

Not always.

You usually don’t need embeddings if:

• Records have a shared unique ID across systems.
• You can match on exact fields like email, phone, SKU, invoice number, etc.
• The data is fairly clean and standardized.
• You’re doing straightforward entity resolution with deterministic rules.

Embeddings can help if:

• Records are messy or inconsistent: different spellings, formats, abbreviations, missing fields.
• You need to match based on semantic similarity rather than exact values.
• You’re deduping things like:
  • product descriptions
  • customer notes
  • company names
  • addresses with lots of variation
• You want a candidate-generation step before stricter matching.

Common pattern

A lot of dedupe systems use a hybrid approach:

1. Exact/blocking rules to narrow candidates
2. Embeddings or fuzzy similarity to find likely matches
3. Rules or ML classifier to decide final merge

Rule of thumb

• If your duplicates differ mainly by format or typos, start with standardization + fuzzy matching.
• If they differ by meaningful text variation, embeddings may be useful.
• If you have stable identifiers, embeddings are probably unnecessary.

If you want, I can suggest the best dedupe approach based on the kinds of records you have.