Imagine a U.S. catering company. A customer name from a web form looks identical to the name in an older export, but exact comparison fails. This is a hypothetical example. Inspect the character representation before asking artificial intelligence to rewrite the name.

An accented letter can sometimes be represented as one character, or as a letter followed by a combining accent. Normalizing both representations to the same canonical form can resolve this difference. That does not make every similar-looking name equivalent.

Keep the original name, a comparison key, and a stable customer identifier separately. Preserve the original for display, generate the key under documented rules, and link orders through the identifier. Apply the same rule on both sides. A match is a candidate, not permission to merge namesakes.

Do not assume that more transformation is better. Compatibility normalization also folds some formatting distinctions. Names, product codes, and contract text need their own policies, rather than one universal cleaning button.

Normalization is not accent removal, translation, or identity verification. Similar-looking characters can remain different. Even if you separately offer forgiving search, show candidates and uncertainty. Search results do not grant permission to change or merge accounts.

Prepare three acceptance groups. Two representations of the same accented character should match. Accented and unaccented names should not become equal merely through canonical normalization. Identically named customers with different identifiers must stay separate. Check that originals survive. This is a proposed plan, not completed testing of your systems.

Have the owner approve each field’s comparison policy. Version the rule, retain originals, and list collisions. Start with suggestions only. Success means finding intended matches without merging customers who should remain distinct.