That was roughly how I looked at it when I started working on the naming side of Nurturepedia.
They don't always type the exact spelling. They search by meaning. They try different spellings. They mix cultural preferences. They want names from a particular origin or religion. Sometimes they know the sound they want but have no idea how the name is spelled.
At that point, "searching a database of names" becomes a much more interesting engineering problem.
This article walks through some of the technical and UX lessons I've learned while building a better baby-name discovery experience. Search starts with the data model
{ name: "Amélie", normalizedName: "amelie", gender: "girl", meanings: ["work", "industrious"], origins: ["French"], religions: ["Christianity", "Neutral"], alternateSpellings: ["Amelie"], countries: ["France", "Canada", "United States"] }
That separation becomes particularly useful when your dataset contains names with accents, diacritics, alternate spellings, or characters from different writing systems. Unicode can quietly break a search experience
JavaScript developers eventually run into an annoying fact: two strings can look identical but contain different Unicode representations.
For example, a character such as é can be represented using a single code point or as a base character followed by a combining accent.
JavaScript's String.prototype.normalize() exists specifically to deal with these different Unicode representations.
const normalized = value .normalize("NFKD") .replace(/\p{Diacritic}/gu, "") .toLowerCase();
MDN documents the distinction between canonical and compatibility normalization, and compatibility normalization can be useful for search-oriented processing in appropriate situations.
The important caveat is that I would never replace the original display value with the normalized value.
That small architectural decision prevents a lot of problems later. Exact matching isn't enough
This is where search becomes a relevance problem rather than a simple database lookup.
Exact name match ↓ Exact normalized match ↓ Prefix match ↓ Alternate spelling ↓ Meaning match ↓ Broader relevance
The exact scoring strategy depends on the application, but the principle is important:
A search engine should understand what the user probably meant, not just what text happens to exist in the database.
MongoDB Search provides tools specifically for relevance-oriented search, including autocomplete, compound queries, filtering, faceting, and scoring.