Book smarter shows. Forecast sales. Fill the room.

rchive AI turns scattered music data into clear answers: Who should I book? How many tickets will sell? At what price? When do we break even?

We analyze artist demand, local signals, and your historical data to predict ticket sales by day, recommend pricing, and surface the exact levers that move your show from risky to profitable.

  • Independent promoters & talent buyers who need signal before committing offers.

  • Venue teams planning calendars, holds, and staffing.

  • Managers & agents testing markets and routing smarter.

  • College/DIY organizers who want confidence on first bookings.

Who it’s for

  • Guesswork & “vibes-only” booking: No more flying blind off monthly listeners.

  • Fragmented data: Followers here, streams there, ticket history in a spreadsheet somewhere.

  • Pricing anxiety: GA vs. tiers vs. VIP… what maximizes revenue and velocity?

  • Promo blindspots: You spend on ads—what actually moved the curve?

The problems we solve

How it works

  1. Connect data
    Pulls public artist/activity data (social growth, streams, YouTube views, search trends), market signals (calendar competition, holidays, campus cycles), weather + local factors, and your past show results.

  2. Model demand
    Our time-series engine forecasts daily tickets sold from announce to show date, then stress-tests scenarios (price changes, supports, promo spend, venue size).

  3. Plan & execute
    Get a green/yellow/red go/no-go, recommended offer range & capacity, tiered pricing, and a marketing timeline with spend guidance tied to ROI.

  4. Track & adapt
    Live dashboards compare expected vs. actual; alerts flag under/over-performance so you can tweak price, add support, or bump ad sets early.