detecting ai-generated content in marketing
customers detect ai marketing before detectors do. they feel the flat rhythm and safe transitions. your job is to catch it in draft, not in a twitter screenshot.
marketing teams chase external ai detectors while ignoring internal evidence. if the draft does not match your profile, it does not matter what a third-party score says. you already know.
read ai writing detection guide for the full framework. this post focuses on marketing workflows under brand pressure.
what should marketers check first?
- sentence-length standard deviation versus your baseline
- signature transition count per thousand words
- concrete-to-total noun ratio
- opening paragraph authorship (human vs ai)
- profile score from
hyv scan
why do external detectors miss brand risk?
detectors optimize for model fingerprints, not your voice. a heavily edited ai draft can pass detectors and still fail subscribers. profile scoring catches statistical drift even after polish.
the embarrassing screenshot is not ai wrote this. it is this does not sound like them anymore. founder twitter cycle, recurring
what process prevents public detection?
no customer-facing ai draft ships without scan. agency and freelancer deliverables included. pair with why writing gets flagged as ai for anxiety-driven search traffic.
what do you tell leadership?
we use ai for speed and hyv for voice control. here is the score log. that sentence ends debate faster than policy PDFs.
get started for $1 — create your account and scan your first draft in minutes.
get started for $1 →
get started for $1 — create your account and scan your first draft in minutes.
get started for $1 →
writes about brand voice, ai writing patterns, and the craft of sounding like yourself. built hold your voice after watching his own voice flatten across six months of heavy ai drafts.
co-written with ai as sidekick. shashank drafted the observations; the ai pressure-tested the structural claims. if something reads too smooth, that's the ai's fault.