# Inside the Spatial9 Pipeline: From Upload to Publish-Ready File

> Follow the Spatial9 pipeline from upload to publish-ready file: source separation, GravityLLM spatial arrangement, AI agents, listening and delivery formats.

Web page: https://spatial9.ai/blog/academy-spatial9-pipeline
Author: Spatial9 Team
Published: 2026-10-04. Updated: 2026-10-04.

## Key takeaways
- The Spatial9 production lane has seven stages: input, analysis and source separation, GravityLLM arrangement, AI agent rendering, quality listening, delivery and publish.
- Stems extracted from a stereo master are estimates that can contain bleed and artifacts, so original stems give the most precise placement when available.
- Depending on workflow and plan, Spatial9 delivers ADM, Eclipsa Audio (IAMF), binaural and Ambisonics files.
- Labels and catalogs can batch-submit jobs through APIs, with webhooks sending an automatic message back when a job finishes.

In this lesson: You will follow a track through every stage of the Spatial9 pipeline, from the file you upload to the deliverable you publish. You will learn when to start from a stereo master and when to use stems, how the training side feeds the model, and how catalogs scale with APIs and webhooks.

Every great production has a signal flow. In a recording studio, you learn to trace a sound from the microphone through the preamp, the console, the processing and the converters to the final file. When something sounds wrong, signal flow is how you find out why.

AI-assisted immersive production has a signal flow too. It is just made of different stages. Once you can trace it, the system stops being a black box. You know what each stage does, what it needs from you, and where to listen when a result is not right.

That understanding is a professional superpower. Engineers who understand the pipeline give better briefs, prepare better sources and spot problems faster. In this lesson, we open the hood.


## What you will learn

- The seven stages of the production lane, from input to publish.
- The trade-offs between starting from a stereo master and starting from stems.
- How the training lane, shown in the Architecture illustration, improves GravityLLM over time.
- Where human listening fits in, and why the revise loop matters.
- How APIs and webhooks let labels and catalogs process work at scale.

The rest of this lesson is free with a Spatial9 account: https://spatial9.ai/blog/academy-spatial9-pipeline