Detect AI-generated musical works before they impact institutional resources.
Against the international pattern of documented AI fraud. Aligned with copyright frameworks across operating jurisdictions. Applied at repertoire ingestion, before impact on institutional administration.
Built for collecting societies
OPERATIONAL CONTROL
Two operational checkpoints across the repertoire lifecycle.
01 INTAKE
Detection at repertoire ingestion.
The technical analysis intervenes when the work enters the administered catalog, before distribution begins.
02 SETTLEMENT
Verification prior to royalty settlement.
The technical analysis identifies registrations requiring institutional review before resources are redistributed.
Both checkpoints operate under the established institutional criterion — the system provides the technical capability.
Operational capability, documented cases, and institutional framework.
The technical layer transforms institutional policy into operational capability.
The system analyzes catalogs at repertoire ingestion, distinguishing AI-exclusive content, partially AI content, and fully human content, with complete traceability. The analysis integrates audio signals when available and additionally operates on institutional metadata of the administered repertoire (ISWC and other standardized work identifiers) — operational capability does not require complete catalog audio availability. The collective management organization exercises distribution according to its established institutional criterion — the system provides the technical layer, it does not replace institutional authority. The operational capability also applies to distributors and record labels facing the same challenge at catalog ingestion.
Collective management organizations have established policies on AI content. Without technical capability to enforce those policies at scale, the policies operate on paper — not in practice. Every AI track that enters the repertoire without detection redirects royalty collection toward registrations the organization explicitly does not represent, simultaneously compromising the integrity of the institutional mission, member trust, and accumulated legal exposure.
Documented cases reveal the operational and reputational cost of absent systematic detection: the Michael Smith case (USD ~10M scheme indicted by the U.S. Department of Justice in 2024) and the mass Spotify removal (~75K AI tracks removed in 2024).
SGAE 2025 study and Goldmedia 2024 study project 27-28% reductions in royalty distribution by 2028 in the European markets measured.
Recent European case law has begun to address this trajectory: in GEMA v. OpenAI (Landgericht München, November 2025) — a first-instance ruling within the German national jurisdiction, subject to appellate review — memorization during AI training was found to constitute reproduction under copyright law. The decision marks one element of an emerging European jurisprudential trajectory rather than a settled pan-European precedent.
Detection cost represents a fraction of the economic value preserved under institutional control.
For a collective management organization processing thousands of tracks monthly, each percentage point of undetected AI content translates directly into royalty collection that escapes institutional criterion — simultaneously compromising member trust, accumulated legal exposure, and operational reputation. The relevant operational evaluation does not focus on the cost of detection, but on the estimable institutional risk of sustained non-detection.
Copyright frameworks across operating jurisdictions — from European Union member states to the United Kingdom and the United States — constitute the institutional substrate on which technical detection capability at repertoire ingestion operates. For collective management organizations, distributors, record labels, and IP counsel administering or protecting repertoire across cross-border catalogs, the transformation of institutional policy into systematic enforcement capability at repertoire ingestion preserves institutional criterion over catalog administration.
Why INBEstudio
Single-function detection, multi-source verification, and forensic-grade reasoning.
A single function: detection
Your catalog enters the technical analysis, gets processed, and the report is returned. That is the full lifecycle of the track in our system — the analysis is the beginning and the end of the process. The conclusion you receive is exactly the output of the technical analysis, with no commercial reuse of the processed catalog.
Multi-source verification — no single-vendor dependency
When a verification source is temporarily offline (e.g. a public catalog rate-limited or down), the system discovers equivalent verification across other independent public catalogs and maintains high-confidence verdicts without customer intervention. The report explicitly shows which sources confirmed verification in each analysis. Empirically validated under public-catalog rate-limit conditions.
The historical catalog is not subject to incorrect classification
When the artist presents verifiable presence in public catalogs, the tool cross-references the audio signature against that identity prior to flagging the track, which reduces the probability of false positives over established repertoire. Cross-verification at ingestion preserves the integrity of institutional records under the criterion established by the collective management organization.
Audio and lyrics automated cross-check — partial-AI productions detected
The system cross-references vocal identification with lyrical composition without requiring manual lyrics submission. Compositions where some elements are synthetic and others are human are classified in separate layers. The differentiation between hybrid and all-AI productions can inform case-by-case evaluations that your legal team conducts under the regulations applicable in your jurisdiction.
Auditable methodological framework
Documented and reproducible. Compliance officers can request the methodological documentation and verify the criteria.
Validated at Scale
Performance metrics from laboratory testing across diverse audio content. Real-world results may vary depending on audio quality, genre, and production characteristics.
Detection accuracy across 17,000+ files analyzed in laboratory testing. Results may vary depending on track quality, file format, and prior processing applied.
Audio, lyrics and artist verification cross-referenced in a single analysis. When one verification source is temporarily offline, the system discovers equivalent verification in other independent public catalogs.
Cryptographic timestamps on every forensic report — chain-of-custody documentation.
How It Works
Seamless integration with your existing systems
Connection to the institutional catalog
Via web portal, REST API, or batch processing. No prior migration nor complex technical integration is required.
Automated detection of AI content
The system analyzes each track in an average time under 2 minutes, cross-referencing audio, lyrics, and artist verification in the same analysis. For extensive catalogs, batch processing in parallel.
Flagging only of tracks that require human attention
The system signals tracks with uncertainty — divergent signals, ambiguous cases, partially AI productions, among other cases — for review by the CMO team. Tracks with clear classification process without manual intervention.
Forensic analysis in cases of doubt
For disputes or regulatory submissions, a complete forensic report with cryptographic chain-of-custody is generated. RSA-SHA256 digital signatures and RFC 3161 timestamps.
Forensic-Grade Documentation
Every analysis can generate a comprehensive forensic report designed for chain-of-custody documentation.
Digital signatures (RSA-SHA256)
CryptographicRFC 3161 timestamps
TimestampedSpectral analysis graphs
Visual EvidenceMulti-layer analysis
Cross-ValidatedOnline report access
TransparentBuilt for Collecting Societies
Solutions designed for your specific workflows
Analyze at the Point of Registration
Help identify potential AI-generated content before distribution begins.
COMPLIANCE
Institutional privacy preserved.
Files are deleted upon analysis completion. Processed content is not used for model training or dataset enrichment.
Ready to Get Started?
Schedule a demo or request a pilot program