Austin, Texas
You have done this before. You saw someone play to a half empty room, told everyone you knew, and watched the rest of the world arrive two years later. You were right, and nothing anywhere remembers that you were. StageCred is where that gets written down. One scan or tap at a show, and the night you believed in an artist becomes a permanent record with your name on it.
01 — Why it matters
This is not a shortage of talent. It is a shortage of proof. Every number below is public, or simple math on a public number, and they show the same gap from three sides.
Music apps recommend songs by looking at what you already played, and what people with taste like yours already played. Every suggestion is built out of the past. That works well for a song a million people know. It cannot work for a song nobody has heard yet, because there is nothing to look at. No plays, no history, no reason for the app to put it in front of anyone. So it puts it in front of nobody. It is really hard for an algorithm to know what would surprise you.
Music has never really spread that way anyway. It spreads person to person. Someone hears a song, tells a friend, drags that friend to the next show. But that only happens if there is a way for the first person to reach the second, and right now there is none. Whoever hears it first has no way to pass it on, so the song dies in the room it started in. Write that first night down and the path exists. The song keeps moving, and the artist and the people who were always going to love them find each other years sooner.
Today one person decides. A scout at the back of the room, a booker, whoever owns the playlist. An artist's next year turns on whether that one person was there, and awake.
A hundred other people were in that room, and they decided too. Their verdict is earlier, wider, and far harder to fake than any single opinion. Nobody writes it down. By morning it exists only in their heads.
So we write it down. Recognition granted by the room instead of by a gatekeeper, held by the people who were in it, and traceable to what each of them actually did: showed up, came back, brought someone. With their consent, and they can take their name off it whenever they want.
02 — Why now
AI decides more and more of what you hear next, and all it has to go on is what people already played. Now it is also learning from words and music that other machines made. It has started feeding on itself. We produce the opposite: a record of real people in real rooms, written at the moment they decide they love something, before any app has pushed it at anyone.
Who backed which artist, how early, how often, from which room.
That record does not exist anywhere today, and we will never sell it. What it can do is recommend: the next show for a fan, the next booking for a venue, the next artist worth a label’s time. The record is real the moment it is written, and it keeps growing whether or not anyone ever pays for what it recommends.
03 — Claim your place
This is not a waitlist. A waitlist gives you nothing until somebody lets you in. You get your number in line, the bonus that comes with it, and your own QR code to share, all straight away. Your place in line never moves.
You were brought here by someone who is already in. They get credit when you join.
We ask for one thing because that is all anyone will give a stranger in a dark room. No password to invent, nothing to download, nothing to verify before it counts.
Screenshot it, print it, stick it on a laptop, put it on the back of a shirt. Anyone who scans it lands here, and when they join you are credited automatically.
04 — The detail
The short version is above. The long version holds the working mechanics, the whole scoring model in public, and an honest column for what we have not built yet.
Scan or tap, write it down, score it, share it. The whole product in four steps.
Read itThe full model, published rather than hinted at, with the real numbers.
Read itBuilt, being built, and still a guess, kept in separate columns.
Read itWhat you get for being early, and how a QR on a sticker pays you back.
Read itThree sides of the same room, and what each one gets out of it.
Read itThe case, the next test, what protects us, and what would make us wrong.
Read itPlain answers to the questions people ask most, from pricing to your data.
Read it