Project: Rights & Metadata System for Transmedia IP

Hook: Why every backend engineer should build a Rights & Metadata System for Transmedia IP

Tracking who created what, where a license applies, and whether a character can be adapted into a mobile game is a common nightmare in studios and agencies. If you're a backend engineer looking for a portfolio piece that mirrors real-world product requirements, build a prototype rights & metadata system for transmedia IP: it forces you to design robust data models, APIs, audit trails, and provenance that hiring teams value.

Quick summary — what you’ll deliver and why it matters in 2026

Build an API-backed service that models creative contributions, tracks licenses and distribution rights across media types (comics → video → game), stores immutable provenance, evaluates licensing windows, and answers availability queries. This project aligns with 2026 trends: rise of modular IP and transmedia studios (see recent signings like The Orangery in early 2026), growth of short-form vertical video platforms, and increased demand for traceable AI-attributed content.

“Transmedia IP is not a single format — it’s a rights graph. Your backend should make that graph queryable, auditable, and defensible.”

What you’ll learn (project goals)

• Design a normalized rights & metadata database schema (and a graph variant).
• Implement REST/GraphQL APIs to manage works, licenses, and contributors.
• Model licensing windows, exclusivity, territory, and percentage splits.
• Produce an immutable audit and provenance log (W3C PROV-inspired) and tie it to events.
• Create sample queries to determine availability and chain-of-title.
• Package the project as a strong portfolio piece: OpenAPI, Postman, demo dataset, CI, and README.

2026 trends to bake into your prototype

Design with current realities in mind:

• Transmedia business models: Studios and IP incubators are expanding cross-format deals (Variety, Jan 2026). Your model must support multi-format licensing and sublicensing.
• AI content and attribution: AI-assisted creation requires attribution metadata and provenance metadata fields to prove who contributed what and when.
• Micro-licensing & fast windows: Platforms increasingly launch short, overlapping windows for vertical content, and your system should answer availability for quick-turn adaptations.
• Interoperability & standards: Use W3C PROV concepts for provenance and common metadata fields so the system can integrate with DAMs and CMSs.

Core domain model — entities and relationships

At the heart of the system are a few entities. Model them early and clearly:

• IP (intellectual property): the umbrella property (e.g., a comic universe).
• Work: a specific creative artifact (comic issue #1, video short, game build).
• Asset: media files, scripts, concept art linked to Works.
• Party: creators, studios, agencies, distributors.
• Contribution: a record tying a Party to a Work with role, timestamp, and evidence.
• License/Right: a contract record granting permissions, with window, territory, platforms, exclusivity, and revenue share.
• ProvenanceRecord: immutable evidence of a change or assertion (signed statement, hash, CDC event).

Sample SQL schema (simplified)

-- Core tables (Postgres)
CREATE TABLE ip (
  id uuid PRIMARY KEY,
  title text NOT NULL,
  created_at timestamptz DEFAULT now()
);

CREATE TABLE work (
  id uuid PRIMARY KEY,
  ip_id uuid REFERENCES ip(id),
  title text,
  media_type text, -- comic|video|game
  release_date date
);

CREATE TABLE party (
  id uuid PRIMARY KEY,
  name text,
  type text -- creator|studio|publisher
);

CREATE TABLE contribution (
  id uuid PRIMARY KEY,
  work_id uuid REFERENCES work(id),
  party_id uuid REFERENCES party(id),
  role text,
  evidence jsonb,
  created_at timestamptz DEFAULT now()
);

CREATE TABLE license (
  id uuid PRIMARY KEY,
  ip_id uuid REFERENCES ip(id),
  licensor_id uuid REFERENCES party(id),
  licensee_id uuid REFERENCES party(id),
  territory jsonb, -- list of ISO countries or regions
  platforms jsonb, -- platforms or media types
  start_date date,
  end_date date,
  exclusivity boolean DEFAULT false,
  revenue_split jsonb,
  metadata jsonb
);

CREATE TABLE provenance_record (
  id uuid PRIMARY KEY,
  entity_type text,
  entity_id uuid,
  action text,
  actor_id uuid REFERENCES party(id),
  payload jsonb,
  recorded_at timestamptz DEFAULT now(),
  hash text -- optional content hash for immutability
);

Choosing storage: relational, graph, or hybrid?

Rights systems are a graph problem: parties, works, and licenses form a connected network. But transactional integrity matters — payments, windows, and contracts need ACID rules.

• Start with Postgres (relational) for transactions, audit logging, and JSONB for flexible metadata. Use FK constraints for integrity.
• Add a graph layer (Neo4j, Amazon Neptune, or Postgres with pg_graph) for chain-of-title and rights-walk queries that answer: “Who holds film adaptation rights for character X?”
• Consider an append-only ledger (Event Store, Kafka, or blockchain anchoring) for immutable provenance and external verification.

API design — make the rights graph queryable

Create RESTful or GraphQL endpoints that map to your domain. Keep APIs small, testable, and documented with OpenAPI.

Essential endpoints

• POST /ip — create IP
• POST /works — create or link Work
• POST /parties — register creators/studios
• POST /contributions — record creative contributions (with evidence)
• POST /licenses — grant a License (validates overlaps)
• GET /availability?ipId=&character=&start=&end=&territory=
• GET /chain-of-title?workId=
• GET /provenance?entityType=&entityId=

Sample JSON payload — create a license

{
  "ipId": "8a6f...",
  "licensorId": "3b5c...",
  "licenseeId": "9d2a...",
  "territory": ["US","GB","EU"],
  "platforms": ["video", "game"],
  "startDate": "2026-04-01",
  "endDate": "2027-12-31",
  "exclusivity": true,
  "revenueSplit": {"licensor": 0.6, "licensee": 0.4}
}

Implementing audit & provenance

Provenance proves assertions about the origin and transformation of assets. In 2026, with AI tools assisting creation, provenance is essential for attribution and legal clarity.

• Use an append-only provenance_record table or an event stream (Kafka/EventStore) to capture every state-changing action.
• Record who performed the action, timestamps, the payload, and an optional content hash (SHA-256) of the evidence.
• Anchor periodic Merkle roots to public blockchains (optional) for tamper-evidence and external verification.
• Adopt W3C PROV concepts (agent, entity, activity) to make provenance portable.

Licensing windows, exclusivity, and availability queries

Modeling windows is simple conceptually but tricky in practice. You must detect overlapping licenses and prioritize exclusivity.

Rules engine — core checks

1. When creating a license, check for existing licenses with overlapping territory & platforms where exclusivity is true. Reject or flag conflicts.
2. Support sublicensing chains: follow the chain-of-title and ensure the licensee is permitted to sublicense.
3. Implement a timeline query to answer: which party has the right to distribute on a given platform in a territory between dates?

Example SQL to find conflicting licenses (simplified)

SELECT * FROM license
WHERE ip_id = :ipId
  AND (start_date <= :proposed_end AND end_date >= :proposed_start)
  AND (territory && :proposed_territory) -- Postgres overlap operator for arrays
  AND (platforms && :proposed_platforms)
  AND exclusivity = true;

Chain-of-title and graph queries

To answer “Who can authorize a game adaptation?” you need to walk the graph from IP to parties via license edges. In Neo4j, a sample cypher query might be:

MATCH (ip:IP {id: $ipId})-[:LICENSED]->(l:License)-[:TO]->(p:Party)
WHERE $date >= l.startDate AND $date <= l.endDate
RETURN p, l

Provenance for AI-assisted contributions

In 2026, studios use AI to generate concept art and scripts. Track these as contributions with fields:

• tool: model ID, model version
• prompt: redacted or hashed for privacy
• human_reviewed: boolean
• evidence_hash: SHA-256 of final asset

This metadata allows legal teams and partners to attribute work and enforce policy.

Security, privacy, and compliance

• Authentication & Authorization: Use OAuth2/JWT, implement RBAC, and scope tokens by IP/Work when needed.
• Data protection: Encrypt PII at rest and in transit; store contracts and sensitive documents in a secure vault.
• GDPR & regional rules: Model consent and retention rules for creators; keep a purge and anonymization workflow.
• Auditability: All changes to license records produce a provenance_record entry; keep an immutable audit trail for disputes.

Observability and testing

Prove reliability with:

• Unit tests for validation logic (overlaps, exclusivity).
• Integration tests for API flows (create license → query availability).
• Property tests for time-window logic.
• Instrumentation: request tracing, audit log metrics, alerting for conflicting license attempts.

Integrations & automation

Real projects integrate metadata from many sources:

• Ingest metadata from DAMs/CDNs and map to your asset model.
• Webhooks for downstream systems (licensing approvals kick off payments or content locks).
• Connect to contract repositories (e.g., DocuSign) and store signed contract hashes in provenance_record.

Portfolio presentation — make this project stand out

This is where hiring teams judge clarity and impact. Package your prototype as a product:

• README: problem statement, architecture diagram, sequence flows, and deployment instructions.
• Sample dataset: fictional transmedia IP (e.g., a comic “Orbit Hex”, its animated short, and a mobile game) with realistic licenses showing conflicts, sublicenses, and AI contributions.
• OpenAPI / Postman collection: live demo calls showing availability and chain-of-title queries.
• Automated tests: CI/CD pipeline that runs unit & integration tests on every push.
• Demo video (2-4 min): screen-share flow: create IP → grant license → query availability → show provenance audit.
• Architecture diagram: show relational DB + graph layer + event stream + optional blockchain anchoring.
• Write-up: include a short section on trade-offs you made and what you would build next.

Concrete example: From comic to game — an availability check

Scenario: A studio wants to license a character from a comic for an EU mobile game from 2026-04-01 to 2027-12-31. How does your system respond?

1. Query existing licenses for the IP that overlap the proposed dates and territories (SQL shown earlier).
2. If an exclusive license exists covering EU and games for that window, return a conflict with the license ID and chain-of-title evidence.
3. If no exclusivity, check sublicensing rights of rightsholder — follow the chain-of-title nodes to find whether the licensee can grant game distribution rights.
4. Record the proposed license as a provisional license with a provenance_record entry for audit, then move to contract signing workflows.

Advanced strategies & future-proofing (2026+)

• Decentralized identifiers (DIDs) & verifiable credentials: Use DIDs for parties to make assertions verifiable across organizations.
• Smart contracts for automated payouts: For revenue split enforcement, integrate payment rails with on-chain settlement for transparent payouts (optional).
• AI-assisted ingestion: Auto-tag assets and extract contributor names using trained NER models — but record human verification in provenance.
• Rights reconciliation: Implement batch reconciliation jobs that sweep for expired windows and flag orphaned assets for takedown or relicensing.

Why this project maps to job requirements

Companies hiring backend engineers need people who can:

• Design resilient schemas and data flows for complex domains.
• Implement secure, well-documented APIs that integrate across systems.
• Ensure traceability and auditability for legal and business teams.
• Make engineering trade-offs and clearly justify them.

Your prototype demonstrates all of these. It also shows domain understanding of transmedia licensing — a differentiator in interviews.

Actionable roadmap — build this in 4 sprints

1. Sprint 1 (1–2 weeks): Schema, basic CRUD APIs for IP, Work, Party, Contribution. Seed sample dataset.
2. Sprint 2 (1–2 weeks): License modeling, availability endpoint, basic overlap checks, unit tests.
3. Sprint 3 (1–2 weeks): Provenance stream/event store, chain-of-title queries (graph proof-of-concept), OpenAPI docs.
4. Sprint 4 (1 week): Packaging: demo video, README, CI, Postman collection, deploy to a cloud demo instance.

Actionable takeaways

• Start with a clear domain model — IP, Work, Party, License, Provenance.
• Use Postgres for transactional integrity and JSONB for metadata; add a graph DB for chain-of-title queries.
• Record every change in an append-only provenance store and consider external anchoring for tamper-evidence.
• Model license windows and exclusivity early — they're the hardest business rule to get right.
• Ship small, document everything, and create a compelling demo that answers real business questions.

Final note: tie the project to real-world signals

Studio deals and new platforms in early 2026 (for example, The Orangery signing with major agencies and the expansion of AI-powered vertical platforms) make this work especially relevant. Employers are looking for engineers who can manage metadata, licensing complexity, and provenance end-to-end.

Call to action

Ready to build this prototype? Start your repo today: scaffold the schema above, seed a fictional transmedia IP, and publish a 3-minute demo. Share the repo link and demo on our community forum for feedback — or join the Challenges.pro backend track to follow a guided challenge that maps directly to hiring requirements.

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