Lab

Brand & claims gate for AI-written copy

Before an agent ships marketing copy, the gate checks every line against brand voice, approved claims, competitor rules and regulated wording. Each draft is auto-approved, sent for review or blocked, and the page shows why.

Adjust inputs ↓

What the gate decides

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–Risk score
–Block
–Warn

Highlights mark each finding. Select one to jump to it.

Edit the draft; the verdict updates as you type

Load a sample draft

Each sample sets its own channel and market.

Each finding comes with a fix

    What gets written to the ledger

    The gate appends every decision to the ledger: which draft, which rule set, which rules fired. An auditor or legal reviews that record later.

    
              
    How it works
    Method
    Config-driven rules engine: phrase and pattern matchers, length limits, readability, and fuzzy matching against a claims register.
    Built from
    The check logic of an agent-governance prototype I built: tone, claims, competitor and regulated-language checks over an agent action ledger.
    Data
    Fictional brand, competitors, claims and evidence. Nothing leaves the page.

    The rules engine

    Each rule is a row of config: an ID, a category, a severity (info, warn or block), the markets and channels it applies to, a matcher and a suggested fix. The engine parses the copy into fields (headline, description, subject and so on), runs every rule in scope, and returns findings with character positions.

    Claims work differently. The engine finds every number that reads as a claim (a percentage, a currency, a "+", or a number next to words like hours, companies or faster) and matches its clause against the register by shared words and the number itself. Same topic and same number: approved, with the evidence ID. Same topic, different number: blocked as altered. No match: blocked as unsubstantiated. Retired claims and claims cleared for another market are blocked too.

    The verdict is fixed logic. Any block finding blocks. Otherwise the warnings add up to a risk score, and the draft goes to a human if the score reaches the threshold. Info findings cost nothing.

    In production

    An LLM agent drafts the copy. This page does not run one; it runs only the deterministic layer that gates its output (same input, same answer, no model call). In production the gate sits between the agent and the ad platform or email tool, and runs on every draft:

    • Blocked drafts go back to the agent with the findings as feedback, so it rewrites against the exact rule it broke.
    • Review drafts land in a queue for a human, with the findings attached.
    • Every decision is appended to a ledger: draft hash, rule set version, findings, outcome, and who overrode what.
    • A weekly drift report shows which rules the agents keep tripping, so the prompt or the brief gets fixed at the source.

    Limits

    • Rules catch known patterns. They miss novel misleading framing, implied claims and context a reader would pick up.
    • An LLM judge can add semantic checks on top, such as tone fit or implied promises. The deterministic layer stays the gate, because it is auditable and gives the same answer every time.
    • Claim matching is word overlap plus the number. A clever paraphrase can slip past, and a rare false match is possible. Both are reasons to keep a human on the review queue.
    • Readability uses the English Flesch formula. It means little for German copy.
    • Legal owns the rule set. The regulatory rules here are simplified illustrations, not legal advice.