Gotobeat
Technology and data OS for the live music industry. We built the prediction and agent layer behind artist and venue demand forecasting.
Gotobeat could see every ticket sale in its network and still could not tell a promoter whether a show would sell.
Four years of gig data — artists, venues, capacities, ticket curves, cities, support acts — sat in Postgres and got used for reporting. Booking decisions were still made from instinct and a spreadsheet, and a mistake meant a half-empty 400-capacity room or an artist turned away from a venue that would have sold out.
Internally the same knowledge problem repeated. Answering "how did this artist perform in Manchester last spring?" meant asking the two people who remembered, and promoters were sending those questions to a shared WhatsApp number at all hours. Nobody was answering them at 11pm on a Friday, which is exactly when promoters ask.
What we built, and why in that order
“We stopped arguing about whether a show would sell and started arguing about the number. That's a much better argument to be having.”
Forecasting is now part of how Gotobeat books, not a report it reads afterwards.
Prediction Agent output appears next to every show under consideration, and the team has started using low-confidence forecasts as a signal in itself — a wide band means the artist-city pairing is genuinely untested and gets treated as a risk, not a guess. Hermes handles the overnight and weekend enquiry volume that previously went unanswered until Monday.
The platform has since expanded into more cities on the same model architecture, retrained quarterly as new ticket curves land. Gotobeat runs and retrains the models in-house now; we hand over after each expansion review.
Have something that needs to ship?
Scope agreed in writing, architecture first, weekly increments you can review. Start with a 30-minute call or send the brief.