Pipelines for Scalable AI Media Generation

CHALLENGE

A growing technology company set out to build an AI-powered system capable of generating and assembling custom media assets at scale. The vision was to create a seamless process where data inputs could dynamically trigger content generation, resulting in consistent, high-quality outputs suitable for large-scale delivery.

The core technical problems:

Quality variance: Recordings produced under different conditions resulted in inconsistent quality, requiring additional cleanup and normalization to ensure professional output.
Production inefficiency: Manual post-processing was needed to refine quality and assemble final media assets, slowing delivery and making the workflow difficult to scale.
System design uncertainty: AI generation tools can vary widely in quality and performance, creating the need for an adaptable architecture that can integrate evolving technologies without vendor lock-in.

The central question was how to engineer a system that is reliable, cost-effective, and adaptable in a rapidly changing AI landscape.

Implementation Details

Six Feet Up’s engineering team collaborated with the client through a series of focused prototype sprints designed to reduce technical risk and validate architectural direction. Each iteration tested core assumptions, gathered performance data, and clarified how the system should evolve.

Establishing the Workflow

The first phase focused on building a minimal viable pipeline to demonstrate that end-to-end automation was achievable.

The prototype automated two foundational components:

Data-driven generation: CSV-based data inputs triggered AI synthesis processes and automatically paired generated files with existing media assets.
Automated cleanup: Pipelines handled normalization, click removal, and silence trimming, reducing manual post-production effort and improving overall consistency.

Scaling AI Processing and Assembly

With the core workflow validated, the team expanded testing to evaluate how AI content generation and automated assembly could scale together.

Engineers benchmarked text-to-speech APIs such as ElevenLabs and Cartesia, evaluating each for audio realism, latency, scalability, and cost.

Automated orchestration built on AWS Lambda and S3 managed the assembly of generated assets with existing content, eliminating manual editing. The design supported horizontal scaling, allowing large batches to be processed in parallel while remaining modular enough for easy integration of future AI systems.

Clarifying Architectural Direction

Instead of pursuing a single, end-state solution, the team used iterative prototyping to validate assumptions and expose real-world constraints. Each cycle clarified which AI tools were production-ready, where automation added the most value, and how to scale efficiently without overcomplicating the design.

The process reaffirmed core engineering principles: test assumptions early, maintain flexibility to avoid lock-in, and design for iteration so each prototype informs the next architectural decision. By grounding decisions in performance data, the team avoided over-engineering and built confidence in the path forward.

RESULTS

Within weeks, the team validated that large-scale AI-driven media generation was technically feasible with the right architecture. Additionally, automated pipelines replaced manual editing, improving efficiency and delivering consistent, production-quality output.

Key insights:

Bottleneck identification: Data normalization, not generation or assembly, emerged as the primary performance constraint. This discovery prevented wasted effort and focused optimization on the most impactful stage of the pipeline.
Integration clarity: Mapping data flows revealed where caching, batching, and parallel processing delivered the largest performance gains.
Validated experimentation: Structured prototyping accelerated clarity, showing how short validation cycles and data-driven iteration can uncover architectural truths and reduce risk before full-scale development.

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