Rights, royalties, and the AI that has to get them right.

Rights management, royalty calculation, and creator-facing tools for music organizations with their own tech teams and standing budgets, where a matching error is a payment error. Ongoing partnership with BMI.

BMIOngoing partnership
18 yrsShipping enterprise platforms
Rights mgmtComplex licensing
RoyaltyCalculation systems

What we build for music & entertainment

Enspirit designs, builds, and adds AI to music rights and royalty software for companies that run their own product teams. Music and entertainment technology is a domain where the business logic is the hard part. Royalty splits across publishers, sub-publishers, and territories. Rights that change ownership mid-quarter. Catalog data from dozens of sources that needs to reconcile daily. The UI is important, but the engine underneath is where the real engineering happens.

Rights management

Track ownership, licensing terms, and territory-specific rights across complex catalog structures. Handle transfers, splits, and disputes.

Royalty systems

Calculate and distribute royalties across publishers, writers, and territories. Audit trails, dispute resolution, and statement generation.

Creator portals

Songwriter and artist-facing tools for catalog management, earnings visibility, and rights registration.

Data reconciliation

Ingest and reconcile catalog data from multiple sources. Match works, resolve conflicts, and maintain a single source of truth.

The interface is a trust product

A songwriter logging in to check their statement doesn't want to learn the calculation engine. They want to know what they earned, from which uses, in which territories, and why this quarter looks different from the last one. If the answer to that takes three clicks and a phone call, the platform has failed them, regardless of how correct the math is underneath.

Creator portals are where most rights organizations either earn loyalty or quietly lose it. The complexity of the back office is real. Royalty splits, territory rules, sub-publishing chains, statement adjustments. None of that should be visible in the user experience unless someone explicitly asks. Earnings need to be readable at a glance. Drill-downs need to answer the next question, not require a glossary. Notifications about catalog changes or new earnings need to be specific enough to be useful and quiet enough not to be ignored.

Building this kind of interface is harder than it looks. It needs designers who understand the domain well enough to know which complexity matters to the user and which can be hidden, and engineers willing to build the data layer to support that distinction. We've shipped creator-facing experiences where the back-office complexity is real and the interface still feels manageable.

Where the experience needs to go next

Statements are still mostly retrospective. A creator finds out what happened last quarter after the fact. The next version is a portal that surfaces patterns in real time: a track that's gaining traction, a territory where usage is climbing, a sync opportunity that fits the catalog. The data already exists in most rights organizations. Turning it into something a creator can act on is a design and product question more than an engineering one.

AI helps here in specific ways. Matching usage reports to registered works at scale has been a machine learning problem for a while, and better matching means rights holders get paid more accurately and more quickly. Rights clearance for sync, film, and commercial use is another place AI can compress weeks of manual research into something usable. We see the bigger creator-side opportunity in catalog discovery: a portal that tells a songwriter where their catalog has untapped commercial potential, not just where it's already earning.

The foundational infrastructure for rights and royalty work matters and we build it. But the interface layer is where most creators and rights holders actually meet the platform. That's where the next generation of music and entertainment products will be won or lost.

Active partnership

BMI: rights and royalty platform engineering

Ongoing partnership on rights management and royalty distribution systems for one of the largest performing rights organizations.

See related work

Common questions

Royalty platforms require a data model that can represent multi-party ownership splits, mechanical vs. performance vs. sync rights, and territory-specific licensing rules, all of which vary by deal type and jurisdiction. We build flexible attribution engines that process usage data from DSPs, PROs, and direct licensing sources and calculate payouts according to each agreement's specific terms, including advances, recoup thresholds, and minimum guarantees. The output is a transparent, auditable ledger that rights holders and label administrators can interrogate without raising a support ticket.
Usage-to-work matching, clearance research, and statement explanation are the workflows where AI pays back first, and where an error is a payment error. We add AI inside the systems you already run, one workflow at a time, measured against your own baseline.
Embedded. Same repo, same standups, three to twelve months and beyond, with a monthly fee that grows or shrinks with the roadmap. The first four weeks run on one workstream, fixed fee.
Yes. We architect platforms with separate experience layers sharing a common rights and catalog data layer, so a music supervisor can browse and license tracks through a B2B portal while fans stream the same catalog through a consumer app, all governed by the same underlying rights data. This avoids catalog duplication and rights conflicts while letting you serve entirely different user types with purpose-built interfaces and workflows. Permissions, pricing models, and usage terms are enforced at the API layer so neither experience can surface content that isn't cleared for its context.
Music & Entertainment

Running a rights or royalty platform and want AI that gets the payments right?

Four weeks of Enspirit Delivery OS on one workstream. Fixed fee, cycle-time guarantee, baseline taken from your own Jira and Git history.

Start with four weeks

Related services: AI product development, Product engineering, AI Integration.