Building a Scalable Audio Processing Platform for AI-Powered Adaptive Music

Customer Overview

Tyger is an AI-powered music technology company developing adaptive audio experiences for gaming. Its platform analyzes music tracks, generates metadata, and derives adaptive playback layers in real time in response to gameplay events. Tyger adapts music to player actions and helps create more immersive gaming experiences while maintaining seamless transitions between musical segments.

Challenge

Tyger’s platform relies on processing large volumes of audio content through a series of CPU- and GPU-intensive workflows. As the company prepared for future growth and significantly larger licensed catalogs, it needed an architecture capable of supporting that volume while maintaining reliability, operational simplicity, and cost efficiency.

The existing approach involved multiple processing components and orchestration layers that introduced operational overhead for what was fundamentally a linear processing workflow. Tyger required a streamlined architecture that could support both large-scale batch processing and near-real-time audio processing while providing visibility into job status, failures, and system health.

Key requirements included:

• Supporting CPU- and GPU-based audio processing workloads
• Delivering processed output within a low-latency processing window
• Enabling both batch and near-real-time processing workflows
• Simplifying orchestration and operational management
• Optimizing storage costs for large audio libraries
• Providing a scalable foundation for future growth

Solution

TrackIt worked with Tyger’s engineering team to design a serverless, event-driven audio processing architecture on AWS to support Tyger’s adaptive music workflows while simplifying operations and providing a foundation for future scale.

Automated Content Ingestion 

Content arrives from licensed catalog partners. A scheduled task running on AWS Fargate collects each delivery through managed, least-privilege credentials. Each delivery is then reorganized into a consistent structure in Amazon S3, with the metadata manifest published only once its audio files are safely in place.

Event-Driven Processing 

Given the linear nature of the pipeline, TrackIt deliberately built the workflow without a central orchestrator. File events from Amazon S3 flow through Amazon EventBridge to purpose-specific Amazon SQS queues, each consumed by a single-purpose AWS Lambda function.

Removing the orchestration layer eliminated operational overhead that the workflow did not need. Queues are paired with dead-letter queues so that malformed messages are set aside for inspection rather than retried indefinitely.

Metadata Processing and Track Registration 

Each delivery is accompanied by a DDEX manifest that describes its metadata and structure. An AWS Lambda function parses the manifest, supporting the DDEX versions currently in use across the industry. As a result, additional delivery formats can be onboarded without redesigning the pipeline.

The function records each release and its tracks in Amazon DynamoDB, notifies Tyger’s backend that the release has arrived, and passes each track to the next stage. A second Lambda function then dispatches each track to the two parallel processing paths.

Parallel Audio Processing 

Audio processing includes a GPU-intensive stage handled by AWS Batch. A dispatcher function groups tracks and submits them for processing.

AWS Batch uses Spot GPU capacity whenever available, automatically falling back to on-demand instances when Spot capacity is unavailable, across a pool of GPU instance types.

The fleet scales down to zero between deliveries, minimizing GPU costs by running compute resources only while jobs are in flight. The processing model itself is Tyger’s proprietary technology, deployed into the pipeline as a container image.

Audio analysis and metadata generation run in parallel on Amazon ECS (Elastic Container Service) with AWS Fargate. The service reads directly from its queue and scales its capacity against the size of the backlog, expanding under load and contracting to a minimal footprint when the queue clears. Both paths write their results to Amazon S3 and their status back to Amazon DynamoDB, giving Tyger a single place to check the state of any track.

Infrastructure, Security, and Observability

The entire platform is defined as code using the AWS Cloud Development Kit, with automated security checks that run every time the infrastructure changes, and a fully isolated environment per stage. 

Compute runs on private networks and reaches AWS services without traversing the public internet. Every component emits logs to Amazon CloudWatch in a consistent structured format, ensuring that a single query can follow one track across every stage of the pipeline. Databases run with point-in-time recovery enabled.

Results

TrackIt delivered a scalable AWS-based processing foundation designed to support Tyger's adaptive music platform as it expands its audio catalog and processing requirements.

The solution provided:

• A serverless architecture aligned with Tyger's linear pipeline per track requirement
• Automated, event-driven coordination across CPU and GPU processing stages
• Centralized job tracking and metadata management
• Built-in monitoring, alerting, and failure recovery capabilities
• Scale-to-zero GPU compute, with Spot capacity used first and automatic fallback to on-demand
• A scalable foundation designed to support future growth and larger processing volumes
• Support for industry-standard DDEX delivery, allowing additional catalog sources to be onboarded quickly