EdTech company, content production

Content Production as a Pipeline, Not Manual Work

An EdTech company produced learning content manually at every step. We automated the mechanical parts.

IndustryEducation
ServicesAI Agents, n8n Automation

Situation

Producing learning content ran step by step by hand, from draft through structuring to formatting. Volume grew, but the number of people who could handle this process did not grow at the same pace. Every step was necessary, but most of them were mechanical, not creative.

Task

Parts of the production chain were to be automated without giving up human quality control. The task was to build a pipeline with n8n and LLM steps that takes over the mechanical part and brings people back in at the right point.

Implementation

  • n8n workflow

    Orchestrates drafting, structuring, quality checks, and formatting as defined steps.

  • LLM steps

    Handle drafting and structuring against clearly defined specifications per step.

  • Sign-off gate

    A human reviews before publication. The pipeline does not replace this step.

  • Error monitoring

    Exceptions and failed steps become visible, not silently skipped.

  • Versioned workflow definition

    The pipeline is code, not a configuration nobody can trace anymore.

Approach

First, the existing manual process was broken down into its individual steps. For each step, we decided whether it was mechanical and checkable enough to automate, or whether it needed real subject-matter judgment and stayed with the content team.

The pipeline was built step by step and aligned with the content team, which defined the quality bar the automated steps had to meet before going live.

Result

The pipeline runs as production infrastructure, not a prototype. The content team spends its time on quality and editorial decisions, not on the mechanical intermediate steps that used to slow down every piece.

Transferable lessons

  • Automate what is mechanical

    The middle of the production chain suits LLM steps; the judgment calls at the edges do not.

  • Sign-off stays with a human

    A quality gate before publication is not optional.

  • A pipeline needs monitoring

    Without error monitoring, automation becomes a blind spot.

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