An automated music marketing system — a business-in-a-box for digital content operations. Built by a team of AI agents, run by one human operator, organized around whatever single metric defines success for each client.
JITRBeats treats a music project the way a product company treats a release: cadence is the unit of progress. A single ships every 8 to 12 weeks, every checklist item finishes, every promotion lane fires. The system exists so that cadence stays predictable while a one-person operator coordinates the artist, the agents, and the platforms.
The checklist below is the canonical list of items every single must clear before it goes live.
One central clip selector feeds every platform. Priorities are set once in the weights config and propagate everywhere; each platform tunes its own cadence on top. The feed reads human because no two posts share the same shape — captions, hashtag counts, and timing all vary on every post. Daily volume scales up or down per client — peak cadence is around 70 clips per day; quieter cadences match a smaller catalog or a different stage of the release cycle.
Every clip carries a deterministic filename encoding its pool, a stable 12-character content hash, and its current effectiveness score. Engagement re-ranks clips nightly; age-based rotation rules keep evergreen material alive while quietly retiring stale clips. Posters never see a database — they read meaning straight off the filename. The same approach scales across client libraries of any size.
Spotlight watches the music-production pipeline (idea → demo → mix → premaster → master → release) and reports the bottleneck stage in plain English: which step is taking longer than the schedule needs, and what the take-away is — e.g. the mix-iteration step is N× over schedule — finish what's already in flight, don't start more. The artist sees the diagnostic in customer-voice; the operator sees the underlying numbers.
JITRBeats doesn't try to sell to strangers. It runs sequential tests — does this creative hold attention? Does an attention-holding viewer click through? Does a click-through become an email subscriber? Does an email subscriber become a Patreon supporter? Each wave has a target, and only ad spend that's clearing the prior gate moves to the next.
A North Star is the single metric that frames every decision the system makes — what to post, when to release, where to spend ad budget, what to feature in the monthly newsletter. For a recording artist, it might be Patreon supporters at a price-per-month sufficient to fund continuous production. For a different client, it could be paid streams, ticket sales, a syndication contract, or a brand partnership. JITRBeats doesn't prescribe the number; it ties the machine's outputs to whatever number you pick.
Once a North Star is set, the marketing waves become a measurable funnel from stranger to superfan. The implication math below is the worked example for a 5K-Patreon goal at $8/month — every other goal has its own funnel, but the shape is the same.
Two dashboards regenerate every two hours (operator + artist), the weekly investor email summarizes what shipped, and any red alert opens a durable ticket that survives until it's worked or auto-resolved. A self-healing responder takes the first run at known outage classes before a human has to.
JITR Records bills its artists as a single billing face: contractors and vendors bill JITR, JITR re-bills the client. Internal cost tracking estimates labor hours from filesystem activity. Monthly narratives summarize what the artist created and what JITR did for them.
JITRBeats is built and run by a team of specialized AI chats — each one owns specific programs and subsystems. The human operator (Jeff) plays the role of architect / AI manager: setting direction, holding architectural continuity across long efforts, and reviewing what each chat ships. Dev chats build applications; marketing chats produce copy and ad creatives; finance chats handle accounting and billing; client-support chats keep the artist informed. New chats start with extensive onboarding text so they understand the system before they touch it.
The four big themes below describe the next year of JITRBeats. Selected open items appear underneath — investors and prospective clients can see what's actively in the queue without us publishing the full internal backlog.
JITRBeats has been a long-running experiment in building real production software with AI agents as the development team. Here's what's true after a year of doing it.