Content Engine
A production content pipeline I built for a high-volume content publishing operation in a fast-moving news niche. It ingests sources, drafts with LLMs, routes everything through human review, publishes, and auto-distributes to X, LinkedIn, and Facebook, running daily.
Discuss this projectRuns every day in production, automated end to end with a human approval gate.
Publishing at the speed of the news
In a fast-moving news niche, the team had to publish a high volume of content every single day, and be early, not just accurate. Sources had to be watched constantly, drafts written and re-written, posts scheduled, and the same stories pushed out across multiple social channels by hand.
That meant long days of repetitive work, a constant risk of missing a story, and no realistic way to scale output without simply hiring more people. The bottleneck wasn’t ideas; it was the manual machinery between a source and a published, distributed post.
How it works
I built Content Engine as an end-to-end pipeline that runs on a schedule, every day.
1. Ingest: It continuously pulls from the team’s sources, deduplicates, and normalizes everything into a single queue of candidate items.
2. Draft (LLM): Large language models turn raw material into formatted drafts, guided by prompts and guardrails tuned to the team’s voice and format.
3. Human review: Nothing ships unattended. A person approves, edits, or rejects each draft; rejected items loop back rather than going out.
4. Publish: Approved content is published to the primary destination automatically.
5. Auto-distribute: The same approved item is fanned out to X, LinkedIn, and Facebook, formatted per channel.
Human in the loop, by design
Automation handles the volume; people keep the standards. The review step is the heart of the system: every piece passes a human gate before publication, so the team keeps full editorial control while the machinery does the repetitive lifting.
The pipeline suggests, formats, and queues; it never publishes something a person hasn’t signed off on. That’s what makes it safe to run at high volume in a niche where being wrong in public is expensive.
Stack
Python · LLM drafting with prompt caching (Anthropic Claude / OpenAI) · Perplexity API for fact-sourcing · human review queue · automated publishing via CMS API · OG-image generation · structured data and SEO schema · per-channel formatting with scheduled distribution · cron-based orchestration.
Results
• Runs daily in production: the pipeline has operated on a daily schedule since 2025, end to end, without manual babysitting.
• One approval, every channel: a single editorial decision publishes the piece and fans it out to X, LinkedIn, and Facebook, formatted per channel.
• Manual machinery removed: sourcing, drafting, formatting, scheduling, and cross-posting are automated; the only human step left is editorial judgment.
The operation's traffic and volume figures belong to the product, so I keep them off this page. I'm happy to walk through them in a call.
Project outcomes
• Scale without headcount: A small team publishes at a volume that previously would have required hiring, because the repetitive work is automated end to end.
• Consistent voice: LLM drafting plus human review keeps every post on-format and on-brand.
• One source, every channel: Approving an item once distributes it everywhere, so nothing is re-typed or forgotten.
• Editorial control retained: The human gate means the team never trades quality or judgment for speed.