RAG for Music Rights and Catalogs

What Problem Does It Solve?

Rights information is scattered. A single song can involve several writers, publishers, administrators and recordings, each tied to a different agreement. Key terms sit in contracts signed years or decades ago, often as scanned PDFs. Catalog metadata varies across distributors, digital music services and collection societies.

So simple questions take real effort. A licensing coordinator checking whether a sync request needs artist approval may read an entire agreement and its amendments. A royalty analyst chasing an unmatched statement line may search three systems by hand. A sync team may know a clip exists but not where it lives.

A RAG assistant does not replace that expertise. It finds the relevant clause, record or clip faster and shows its source, so staff can verify instead of hunt. Rights answers are informational summaries. Your legal counsel confirms anything that matters.

What Questions Can It Answer?

Names and identifiers in this table are generic placeholders, not real works or companies.

Which Content Should It Search?

• Contracts and amendmentsPublishing, recording, administration, sub-publishing, management and license agreements, including side letters that change earlier terms.
• Extracted terms registerTerm dates, territories, approval rights and reversion triggers pulled from contracts, each linked back to its source clause.
• Song and recording metadataTitles, alternate titles, writers and performers. It also includes ISRC codes for recordings and ISWC codes for compositions.
• Split sheets and registrationsOwnership shares and registration records that show who controls each work.
• Royalty statementsStatement lines from distributors and collection societies, especially unmatched income.
• Sync and license historyPast quotes, approvals and granted licenses, which show precedent and existing restrictions.
• Media assetsAudio, video, lyrics, cue sheets and transcripts, with tags such as mood, tempo and themes.

How Does Hybrid RAG Work Here?

Hybrid RAG runs two searches at once. Keyword search finds exact strings, such as a song title, a writer’s legal name or an ISRC. Vector search matches by meaning, using an embedding, a list of numbers that captures what a passage says. The results are merged so the strongest passages from both reach the model.

1. Contracts, metadata, split sheets and statements are loaded into a search index. Each passage keeps its source, clause number and access level.
2. Scanned older contracts go through optical character recognition (OCR), software that turns page images into text, before indexing.
3. A staff member asks a question, such as whether a sync license needs approval for a named song.
4. The system checks the person’s role and limits the search to content they are allowed to see.
5. Keyword search matches exact titles, names and identifiers, while vector search finds clauses with similar meaning but different wording.
6. The two ranked lists are merged, and a re-ranker, a model that compares the question with each passage, puts the most relevant first.
7. The LLM answers only from those passages and cites each clause or catalog record it used.
8. The staff member opens the cited source to verify, and legal counsel reviews anything that affects a deal.

We start music clients on this pattern because their questions rarely use one kind of language. “Placeholder Song” and a writer’s name must match exactly, while “can we use it in an ad” must find a clause about advertising and commercial uses. Meaning-based search alone blurs similar identifiers, and keyword search alone misses paraphrased clauses. See RAG architectures explained for how hybrid RAG compares with the other patterns.

When Should You Add Other RAG Patterns?

Pattern: Graph RAG for Rights Chains

The signal is questions that no single document answers. An analyst asks who ultimately collects on a work after a catalog sale and a new administration deal. Or unmatched royalties keep piling up because nobody can trace which agreement covers a recording in a given territory.

Graph RAG builds a knowledge graph, a map of entities and how they connect. Here the entities are works, recordings, writers, publishers, territories and agreements. Staff can follow a chain from a recording to its composition, its writers, their publishers and the agreements that set each share. Answers still cite the source records. The cost is real. Building the graph takes many LLM calls or careful data engineering. Extraction errors can create false links. The graph must also be updated as deals change. Permissions also need care, because a summary can mix details from deals some users cannot see.

Pattern: Multimodal RAG for Audio and Video

The signal is that answers live in the media itself. Sync teams search by feel, lyric content or a moment in a video, and text metadata does not capture it. Staff end up scrubbing through files by hand.

Multimodal RAG makes audio and video searchable. Speech recognition creates timestamped transcripts, and lyrics are indexed alongside them. Models also tag tracks by mood, tempo and themes. Results link to the file and the exact timestamp, so a sync team can review the right clip quickly. Processing long video and large audio catalogs costs time and compute. Automated tags can also be wrong or vague, so catalog managers need to review them. Pitch decisions stay with your team.

Where Do People Stay in Control?

• Legal counsel on rightsThe assistant summarizes clauses, and your attorneys confirm interpretations before anyone grants a license or changes a payment.
• Business affairs on dealsStaff decide whether to approve, quote or decline a request, using the cited source as one input.
• Royalty analysts on matchesAnalysts review suggested links between statement lines and catalog records before any claim or correction.
• Catalog managers on metadataPeople approve changes to titles, splits, tags and identifiers, since the assistant only reads these systems.
• Artists and their teams on creative choicesThe assistant supports business and operations work, not songwriting, production or creative direction.
• Consent on voice and likenessNo synthetic voice or likeness is created without documented permission from the people involved.

What Security and Compliance Controls Matter?

• Role-based accessConfidential deal terms, advances and royalty rates appear only for authorized staff, with search filtered before the model sees any text.
• Controlled environmentUnreleased music, video, contracts and financial data stay in your controlled cloud environment.
• No public model trainingYour content and data are never used to train public models.
• Documented data sourcesEvery source feeding the assistant is recorded, and you confirm you have the rights to use that content.
• Audit logsQuestions, retrieved sources and answers are logged, so sensitive lookups can be reviewed later.
• Source-first answersEvery rights answer links to the clause or record, and the assistant says so when sources do not cover a question.

We do not provide legal advice. Rights, consent and copyright questions belong with your legal counsel, and we design within their guidance. Our AI governance, security and compliance team helps put policies and controls in place.

How Do You Measure Whether It Works?

• Retrieval accuracyHow often the correct clause or record appears among the results for test questions.
• Exact-match hit rateHow reliably searches by title, writer name, ISRC or ISWC return the right item.
• Citation accuracyWhether each cited clause truly supports the statement it backs.
• FaithfulnessWhether answers stay within the retrieved text without added terms or dates.
• Honest gapsHow often the assistant correctly says the sources do not answer a question.
• Time to verified answerHow long staff take to reach an answer they have checked against the source.
• Expert review resultsThe share of sampled answers that business affairs staff and counsel rate as correct.
• Unmatched income resolvedHow many unmatched statement lines analysts clear with the assistant’s help.

How Do You Roll It Out?

1. 1

   Pick One Team and Question Set

   Choose a focused starting point, such as licensing approvals or royalty research. Collect real questions from that team, with answers agreed by subject-matter experts. This evaluation set becomes the yardstick for every later change.

2. 2

   Prepare Contracts and Metadata

   Gather the agreements, amendments and catalog records for a sample of your catalog. Run OCR on scanned contracts and flag inconsistent titles, names and identifiers. Cleaning this data is often part of the first project.

3. 3

   Build Hybrid Search With Permissions

   Set up keyword and vector search over the prepared content. Apply role-based access from the start so confidential deal terms stay restricted. Test exact identifier lookups and paraphrased clause questions separately.

4. 4

   Pilot With Expert Review

   Give the assistant to a small group of staff. Business affairs staff and counsel review sampled answers and citations. Log failures and use them to fix content, search settings or instructions.

5. 5

   Expand and Add Patterns

   Connect more catalog segments and teams once results hold up. Add graph RAG or multimodal search only when evaluation shows rights chains or media questions are the main gap. Measure after each change.

Frequently Asked Questions

Put Your Contracts and Catalog to Work

Tell us which rights and catalog questions slow your team down and where the answers live. We will recommend a practical starting design and help you test it on real data. Explore our RAG architecture reference or book a free AI consultation.