What was needed
Reduce repetitive content-production work for multiple brands while preserving their individual voice and editorial approval.
Technical project management · Applied AI
Designed and delivered a multi-brand AI content production tool with configurable voice and tone, 12 stages, quality controls, and human review.
Process design · AI orchestration · quality controls · human review · delivery outcomes.
What was needed
Reduce repetitive content-production work for multiple brands while preserving their individual voice and editorial approval.
What I did
Built a 12-stage tool using configurable brand profiles, specialized prompts, recoverable jobs, quality checks, and Google Docs handoff.
What was achieved
Production dropped from roughly two days to minutes plus editorial review. The CEO reported a 50% reduction in content team size and nearly six figures in annualized profit improvement.
Project documentation
Each artifact opens as a responsive HTML document with the project logic, controls, and measured outcomes. No external repository or download is required.
Problem, scope, delivery boundaries, and success measures.
Read documentStages, orchestration, handoffs, and recovery paths.
Read documentReview gates, source checks, and human approval points.
Read documentJobs, articles, failure-rate improvement, and cycle time.
Read documentWhat changed, what stayed controlled, and what comes next.
Read documentAdditional delivery evidence: 549 articles across 882 jobs; measured failure rate reduced from 25–40% to 4–8%. The platform was designed for at least 50 brand profiles, but that limit was not verified in production. Annualized financial impact is the CEO’s estimate, not audited realized profit.
\n View public GitHub documentation \n\nNames, credentials, private records, internal URLs, production identifiers, and sensitive implementation details have been removed or generalized. The public case preserves the delivery thinking, controls, and measurable outcomes.