Multi-Agent AI Content Pipeline — Built & Deployed
Designed and built a complete multi-agent AI content system — a 7-stage pipeline with brief interpreter, research collector, outline architect, draft writer, voice harmonizer, and QA reviewer. Fully functional Streamlit app with dual workflow pipelines, brand voice pre-flight, and human oversight gates.
The Challenge
A 21-client digital marketing agency needed to scale content production without sacrificing quality or brand consistency. Manual workflows couldn't keep pace — writers were producing content without research backing, brand voice was inconsistent, and there was no quality gate before publication.
The Solution
Designed and coded a complete multi-agent AI content system in Python with a Streamlit frontend. The pipeline runs content through seven sequential stages: brief interpretation, automated research collection, outline architecture, draft generation, voice harmonization, and QA review — each with human oversight checkpoints.
The system supports dual workflows (human-only and AI-enhanced paths), includes brand voice pre-flight checks pulled from client-specific guides, and features prompt version control so every output is reproducible. Delivered with full technical specification documentation across 7 documents covering the MVP, full systems map, editorial review gates, and pre-editorial processing layer.
What I Built
- 7-stage multi-agent pipeline (Python)
- Streamlit UI with brief selection & stage skipping
- Brand voice pre-flight system
- Human oversight checkpoints at each stage
- Dual pipeline architecture (human-only + AI-enhanced)
- Full technical spec documentation (7 documents)
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