limbo/Beat says its quality control agent helps distributors review releases for potential fraud before tracks reach streaming platforms. That is one review window. Tracks already in a back catalogue may still be earning while they are screened for artificial intelligence (AI) music. An independent label needs a separate process for each: decide what can ship, then decide what to investigate without disrupting the rest of the catalogue.
Check uploads before delivery
A new upload has a natural stopping point. The distributor can review it before sending the release to stores, ask for more information, or hold delivery while it resolves a concern. Pre-delivery fraud review is the function limbo/Beat describes for its quality control agent. The useful decision for a label is whether its distributor has a defined point in the upload process where a human can resolve a flagged release.
That decision has consequences beyond the upload being checked. Rising upload volumes and a copyright lawsuit have brought scrutiny to the way a distributor’s catalogue can affect the artists using it. Quality control therefore belongs in the delivery process, where a questionable upload can be examined before it joins the store catalogue. A label submitting a release should keep its ownership and release information ready for review so that an inquiry has a clear path to an answer.
AI screening adds another reason to make the decision before delivery. AI music can impose costs on the distributor that ships it through royalty dilution, store takedowns, demonetization and lost listener trust. Those outcomes are different: a takedown affects availability, while demonetization affects earnings. A label should ask what action follows each kind of concern, rather than treating a passed upload as a promise that every store will keep paying for it.
Review the live catalogue on its own schedule
A back catalogue cannot be handled as a stack of unreleased files. Screening guidance for existing catalogues calls for scanning beside delivery through an application programming interface (API), sorting results by score and triaging them in bands. That keeps the investigation moving while releases already in stores continue their normal delivery and earnings cycle.
For a label, the practical unit of work is a review queue. Record which release needs attention, what prompted the review, who is responsible for resolving it and what decision was made. Work through higher priority cases first. If the same team handles both queues, give it distinct instructions: the upload queue asks whether to send a release; the live catalogue queue asks whether an existing release needs further investigation or a store action.
This separation also makes a distributor’s response easier to assess. A service that checks new uploads has addressed the entry point. It has not thereby examined the catalogue it already delivered. The back catalogue workflow exists because pausing delivery is not a workable way to check tracks that are already earning. Labels with older releases should ask how those releases enter review and how they will learn the outcome.
Turn scores into decisions
A detector’s output is a prompt for review, not the decision itself. Distributor guidance for AI flags calls for two thresholds, actions for each resulting band that comply with store policy, a defensible record and an advisory role for the detector. That gives staff a repeatable route from a score to an action while keeping the final decision with the distributor.
Tool choice does not replace that policy. Seven AI music detection tools, including SEAM, Ircam Amplify, Hive, Modulate, Pex, Sightengine and Qosmo, differ in what they detect, how users access them and their published costs. A label evaluating a distributor should therefore ask what the chosen tool checks and what happens after it returns a score. The answer matters more to a release than the existence of a detector on an intake form.
Fraud review and AI screening should also have separate reasons recorded for their decisions. limbo/Beat describes potential fraud as the target of its pre-platform review, while the AI policy playbook addresses how to handle a track flagged as AI. A flag in either process needs a stated question and a documented outcome. Without that record, a label cannot tell which issue led to a hold or a later store action.
Set a catalogue review rule
A label can make the two queues operational with a short rule for every release:
- Before delivery: identify who answers a distributor’s fraud or AI inquiry and what release information that person can provide.
- After delivery: put existing releases into a background review schedule, with a named owner for cases that need attention.
- At a flag: record the issue, review the result against the distributor’s policy and keep the reason for the final decision.
- At a store action: record whether availability or earnings changed, then reconcile that outcome with the catalogue and royalty records.
The governing distinction is the release’s status. A new upload can wait for an answer before delivery. An earning release needs an investigation that runs alongside delivery until a specific action is justified. Background scanning and banded triage provide the live-catalogue route; two thresholds and a recorded, store-compliant decision provide the action rule. Those are concrete questions a label can put to a distributor before the next upload and apply to its existing catalogue now.
