── ── AI & agents

AI Content Factory and Brand Consistency

AI drives the marginal cost of a draft toward zero, so the bottleneck moves to consistency, accuracy and compliance. Volume without gates is brand dilution, factual error and legal exposure, multiplied by output.

How it works

Output: Content Factory Runbook (voice spec + fact base + gates + cadence + metrics)

1. Voice spec. 3–5 tone attributes written as is / is-not pairs, banned words, sentence length, person, and 3 good/bad example pairs. Gate: two reviewers score the same draft differently → the spec is too vague; rewrite it → stop. 2. Fact base (single source of truth). Product specs, prices, case data, customer-approved quotes — each entry with source, expiry date and owner. Gate: any number or quote in a draft that is not in the fact base → reject the draft. 3. Risk tiers. Low (opinion, tutorials) · Medium (product descriptions) · High (prices, efficacy, comparisons, reviews and testimonials, legal / health / financial topics). Gate: no high-risk piece publishes without a line-by-line human check against the fact base. 4. Generate → review gates. AI drafts → automated checks (banned words, fact-base match, links, required AI disclosure) → human spot-check (low) or full review (medium and high). Gate: any claim that cannot be traced to a source → delete it or rewrite it as a provable statement. 5. Style QA. Sample weekly and score voice fit 1–5; write low-scoring samples back into the spec as bad examples. 6. Cadence and metrics. Fix the publishing frequency; track quality (dwell, replies, conversion, corrections issued), not just count. Gate: correction rate rising for consecutive periods → cut volume and fix the spec first. 7. Stop-rule: no one will own the fact base → halt volume production and publish only low-risk opinion content until someone does.

Output template: voice spec v_ · fact-base entries / expired · risk-tier rules · gates (auto / human) · cadence · quality metrics · corrections this week · spec revision log

When to use it

• user says 'use AI to mass-produce content', 'how do we make AI write like our brand?', 'the AI content got facts wrong', 'how do we keep AI content out of legal trouble?', 'build a content factory', or plans 10+ AI-assisted pieces per week (blog, social, email, product pages) across several writers or agents

Worked example

CNET's AI-Written Finance Explainers (2022–2023)

A worked example of a content factory that had a human editor on every piece and still shipped errors in more than half of its output — documented in CNET's own editor's note of January 25, 2023, and in contemporaneous reporting by The Verge. It shows why "a human reviewed it" is not a gate, and why the riskiest content was the content treated as routine.

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