Case Study

Mosiky

AI-assisted music platform direction with radio channels, scalable delivery, and distribution readiness. We structured Mosiky to support original catalog growth, radio streaming, and a modular roadmap toward a richer on-demand experience.

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Radio channels CDN delivery patterns Catalog strategy

Project snapshot

A scalable foundation for radio today, with a clear path to on-demand later.

Radio first

Internet radio channels and a stable player experience used to seed users and test engagement.

Original catalog

Persona-based release strategy designed for consistency, identity, and scalable distribution operations.

Scalable delivery

Storage and CDN patterns for audio and video across web and native apps with cost-aware scaling.

Overview

Mosiky combines a structured original-catalog strategy with radio channels and a scalable content delivery layer designed for web and native apps. The direction is modular by design, so early traction is supported without blocking the roadmap toward an Apple Music-like on-demand experience.

Radio streaming channels and player foundation
Storage and CDN patterns for audio and video assets
Persona-based catalog direction for consistent releases
Roadmap for subscriptions, ads, resume playback, and discovery

Key outcomes

Clear platform direction that supports rapid content growth
Separation between licensed streams, when applicable, and originals
Modular foundation to evolve into a richer on-demand experience

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Common challenges

Scaling content delivery without reliability issues
Keeping catalog growth structured and brand-consistent
Supporting multiple apps and surfaces with shared infrastructure
Designing monetization without locking into the wrong stack early

What we delivered

Architecture direction for scalable audio and video delivery
Catalog strategy aligned to personas and distribution readiness
Roadmap for subscription tiers, ad tiers, and playback continuity
Admin workflow direction for upload automation and publishing
Operational framing for analytics, retention, and growth loops

Design principles

Modular backend shared across brands where it makes sense
Cost-aware scaling for storage, CDN delivery, and operations
Clear separation of content operations from playback user experience
Phased enhancements that preserve early traction
Distribution readiness with clean release and metadata workflows

Execution approach

Stabilize radio channel delivery and analytics baseline
Operationalize original catalog publishing and release cadence
Introduce on-demand features incrementally: library, favorites, resume playback
Layer in subscription tiers and ad-supported experiences as usage grows
Expand discovery with search, recommendations, and social preview support

Where AI fits

AI is governed and used to increase throughput and quality, not to remove control. The focus is consistent output, metadata quality, and scalable operations.

AI-assisted content workflows and variants with guardrails
Metadata enrichment for discovery and catalog organization
Localization and translation where needed
Operational summaries for trends and performance signals

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