Band names that fit the genre, checked against the acts we know about.
A metal name and a twee-pop name differ structurally, not just in vocabulary. One is frequently not in English and rarely takes a definite article; the other reaches deliberately for the small and the domestic. This names bands by the actual conventions of the scene, tells you what each name is doing and how it would look on a shirt, and checks every result against a corpus of real acts before you fall in love with one.
Each example replays a saved run, so you can read a whole batch and its corpus check without signing in or spending anything.
Check a name you already have free, no sign-in
The check is not tied to names this app produced. Type any name — one you thought of in the shower, one from another generator — and it will tell you whether it collides with an act in our corpus. Nothing is sent anywhere and nothing is charged.
Your shortlists
How the check works, and what it cannot do
A generator that hands you a name and says nothing else is asking you to take it on faith. A band name is the wrong thing to take on faith: if you name a band, print a hundred shirts and then discover the name belongs to someone else, you have a trademark problem rather than an awkward conversation. So every name is checked, in your browser, before you see it.
Four tiers, in descending confidence
An exact match is the same string. A normalised match is the same name once case, punctuation, diacritics, an ampersand and a leading “The” are set aside — so a name and that name without its definite article are correctly treated as one name, and an initialism written with full stops matches the same initialism written without them. A variant is the same name with a plural or a spelling change. A near match is a name one or two characters away, a name containing a real act's whole name, or a name built on a word that essentially only one real act uses.
Why the near tier is deliberately hard to trigger
An over-strict checker is worse than no checker. If a rule flags every two-word name that contains an ordinary noun, you learn within one batch that the warnings are noise, you start skipping them, and then you skip the one that was real. So a shared word only counts as evidence if it is genuinely distinctive — a coinage or a proper noun, not ordinary English. That distinction is drawn using an external dictionary rather than this app's own corpus, because a corpus of a few thousand acts makes perfectly ordinary words look rare, and tuning against it would have measured the same skew twice.
What it actually catches, measured
Two corpora of real acts and two sets of invented names were commissioned from independent agents who never saw this code. One pair was used for tuning; the other was scored once, at the end, and is reported here.
On the held-out set of real acts, the check flags 54.4% overall — but that single number hides everything useful, because it depends entirely on how well known the act is. It catches 100% of the most famous acts, 80% of very well known ones, 62% of acts known to people who follow music, and 38% of genuinely obscure ones. On the held-out set of invented names it raised a false alarm on 0.7% — three names out of 418, every one of them the softer “close to” warning rather than a claim that the name was taken.
This is a corpus-membership test, not an originality test. A clear result means the name is not in our list. It does not mean the name is available. The list holds a few thousand well-known acts; there are hundreds of thousands of bands, and the list is Latin-script only. Before you commit to a name, search a trademark register and search a streaming service. Those two searches take a minute and this check does not replace them.
Where the misses concentrate, and why
The false negatives are not spread evenly, and the reason is structural. While the bundled examples were being written, the writer kept reaching for a name and then recognising it as an act that already exists — a funerary abstraction, a small-society name, an honorific followed by a short name. Eighteen confirmed acts found that way are now in the corpus.
The lesson generalises. The devices are not too weak; they are too well aimed. A naming device encodes what a scene's names actually do, so a good device points at the same small region of name-space the scene's real bands already occupy — often at the very band that established the convention. The better the device, the likelier its best output is taken. Misses therefore concentrate exactly where this app is working well: plausible, idiomatic, scene-correct names belonging to acts too small for a few-thousand-entry list. That is the argument for searching properly especially when a name comes back clear and sounds perfect.
Why the genre matters more than the word list
Twenty-two genre families are modelled as inventories of naming devices — rules of formation, each tagged with the semantic field it draws on and the structural shape it produces. There are no example band names anywhere in the prompt, deliberately: a sample name is not an illustration, it is an instruction, and its shape comes back in every output. The inventory is handed over as a vocabulary for describing finished work, never as a checklist to work through, and every inventory holds more devices than the largest batch you can ask for — so “use each one once” is impossible by arithmetic rather than by instruction.