Case Study
Tension Radio: 24/7 Automated AI Radio Platform
Our team built a fully automated radio platform that generates, schedules, and streams content around the clock. The system was engineered for hands-free operation, production reliability, and clear operational controls. This case study covers the architecture decisions, implementation process, and measurable outcomes.
Client background
Tension Radio operates a digital radio brand with a mandate for continuous programming. The organization needed a platform that could deliver 24/7 audio without a live production team, while preserving a consistent on-air sound and predictable scheduling.
Problem definition
Manual scheduling and content production could not scale to continuous operation. Existing tools lacked orchestration, automated failover, and quality controls. The platform needed to generate content, assemble programming blocks, and maintain a stable live stream without human intervention while preserving a predictable on-air sound.
Technical approach
We designed an event-driven pipeline that separates content generation, audio rendering, scheduling, and streaming. Each stage is independently observable and recoverable. A queue-based orchestration layer coordinates work, enforces timing guarantees, and prioritizes late-stage items to protect the live stream.
- Content generation and scripting workflows with versioned prompts and templates
- Text-to-speech rendering with audio normalization and loudness targets
- Schedule generation that balances freshness, compliance, and continuity
- Stream assembly with automated transitions and dead-air protection
- Operational dashboards for stream health, queue depth, and content status
System scope
- Continuous 24/7 audio output with program-level scheduling
- AI orchestration for narrative generation, segment pacing, and announcements
- Automated audio assembly, mixing, and asset management
- Fallback playlists and automated safe-mode operations
- Observability and alerting tied to listener impact
Architecture decisions
We separated the control plane from the media plane to reduce coupling and improve resilience. The control plane manages metadata, scheduling, and workflow state. The media plane handles audio processing and live streaming.
- Stateless orchestration workers with durable job state
- Object storage for audio assets and deterministic rebuilds
- Redundant streaming nodes with automated failover and health checks
- Idempotent processing steps for safe retries and replays
- Explicit boundaries for content approval and overrides
Implementation process
The project was delivered in slices to validate reliability early. We started with an automated pipeline for short programming blocks, then expanded coverage to full-day scheduling. Each release added monitoring, quality checks, and recovery mechanisms.
- Discovery and workflow mapping with operational constraints documented
- Prototype pipeline to validate AI orchestration and audio quality
- Scheduling engine with deterministic fallbacks
- Streaming infrastructure and automated health management
- Load testing and cutover with rollback paths
Team and timeline
Our team included a technical lead, backend engineers, ML/AI engineering, and SRE support. Delivery was structured over a 12-week timeline with staged releases and production hardening.
Challenges and mitigation
The primary risks were continuity, cost control, and audio consistency. We addressed these with pre-generation buffers, strict timing validations, and monitoring tied to listener impact.
- Stream continuity protected by automated filler blocks and failover playlists
- Audio consistency maintained through normalization and template-driven mixing
- Cost variability controlled with caching, batching, and queue priorities
- Latency managed through pre-rendering and staged publishing
Measurable outcomes
The system was instrumented with production telemetry and service level objectives. Outcomes were tracked through dashboards and alerting.
- 24/7 programming coverage without manual scheduling during steady-state operation
- Streaming availability operated against a 99.9 percent target with automated failover
- End-to-end content pipeline latency maintained under 4 minutes in steady state
- Mean recovery time under 10 minutes for encoder or stream failures
- Automation coverage consistently above 90 percent of schedule events
FAQ
What made the Tension Radio platform reliable at 24/7 scale?
We separated control and media planes, added queue-based orchestration, and implemented automated failover to keep the stream alive under variable load.
How did Pro Logica manage latency and content freshness?
We used pre-generation buffers, deterministic scheduling, and prioritized late-stage items to maintain freshness without risking dead air.
What outcomes did the client see after launch?
The system achieved continuous programming coverage, a 99.9% availability target, and sub-4-minute end-to-end content pipeline latency.
Related capabilities
This work connects directly to AI Systems & Automation, Scalable Web Applications, and Internal Tools and Platforms.