A scoring layer that checks AI-generated content against brand voice, factual accuracy, and SEO signals before it goes live.
Three-stage pipeline: Stage 1 — Claude generates the content using a brand voice prompt trained on 20+ existing pieces. Stage 2 — a separate Claude instance scores the output on voice match (0-100), factual grounding (does it cite real data?), and SEO compliance (keyword density, heading structure, meta description). Stage 3 — Zod validates the structured output. Content scoring below 80 on any dimension gets auto-rejected with specific revision notes.
Prototyping the scoring thresholds — currently testing whether 80 is the right cutoff or if 85 produces meaningfully better content without rejecting too much. Also testing whether a single Claude call can handle all three scoring dimensions or if separate calls per dimension produce more accurate scores. Early results: separate calls are 15% more accurate but 3x slower.
Being developed for BRVO's Content Engine integration. When a client's AI generates blog posts or product descriptions, this gate ensures nothing publishes that doesn't match their brand voice. Will be standard in all Launch and Ignite packages by Q3.
An agency producing 40 blog posts per month across 10 clients. The quality gate ensures each post matches the specific client's voice — not generic AI copy.
A retailer with 2,000 SKUs. The gate scores each AI-generated description against the brand guidelines and rejects anything that sounds generic or misrepresents a product feature.
A product team that needs docs updated every release. The gate checks factual accuracy against the changelog and ensures the tone stays developer-friendly, not marketing-speak.