Scripts reviewed in days,not two weeks.
Two pipelines learn from real audience behavior and evaluate new scripts. AI advises, editors decide.
Two weeks of writer round tables for every script.
The queue was the product bottleneck. Expert judgment lived in a room, while produced scripts, subtitles, and real audience behavior did not feed the next decision.
Instrument first, then build two pipelines over one knowledge base.
Pipeline A learns
It aligns produced scripts and subtitles with real audience behavior, joins performance, and mines evidence.
Pipeline B evaluates
It compares new scripts against that knowledge and gives editors a grounded report. AI advises, editors decide.
Cost, reversibility, and human judgment were architecture requirements.
- Gemini and ChatGPT were compared and routed by cost, latency, and quality.
- Prompts, embeddings, and features were versioned from day one.
- Retries and alerts made failure visible.
- Human review stayed in the loop by design.
The room still decides. It stopped waiting.
Review fell from two weeks to days, an 80% reduction. Instrumentation and analytics guided acquisition with growth while the product moved from about 100K to about 1M users during the engagement.
A million users write a million rows. The same patterns became a starter other teams can run.