Ad copy & landing page optimization

Business value

• Cuts wasted spend on bland variants. Generators often produce dozens of slight rewordings; this gate keeps the ones with concrete proof and rejects the ones that just rearrange benefit-words.
• Turns evidence spans into rewrite prompts the copy agent can act on — 'add a specific outcome, name a customer, or cite a benchmark' beats 'make it punchier.'
• Builds a paid-side quality bar your media buyer and your generation system both agree on, which is rare and worth keeping.

Agent job to be done

Be a senior conversion copywriter reviewing pre-launch creative. Keep specific, proof-backed copy. Rewrite vague benefit claims, generic CTAs, and unsupported trust statements. Reject the variants that wouldn't survive a creative review.

format: otherintended_use: publishdomain: ad copy / landing page optimization

When to call VeracityAPI

After variant generation, before campaign activation. Re-run on landing pages after every major hero change.

What text to submit

Ad headline, primary text, description, CTA, landing hero, proof blocks, FAQ, and offer copy. Score ads separately from landing pages — they fail in different ways and need different thresholds.

Decision policy

• allow: low risk AND concrete proof present (named customer, named outcome, specific metric, or testimonial with attribution).
• revise: medium risk, generic benefit claims, weak specificity, or no proof anchor. Most generated variants land here.
• human_review: claims affecting compliance, pricing guarantees, regulated industries (financial, medical, legal), or competitor mentions.
• Local rule: every hero section needs at least one of [specific audience, specific outcome, specific mechanism, specific proof point]. None of the four means revise regardless of score.

Automation recipe

• Campaign agent generates copy variants in a structured format (headline, body, CTA as separate fields).
• Score each variant individually; the failure modes differ by field (headlines tend to fail on 'fatigued_hook,' bodies on 'generic_benefit').
• Discard high-risk variants before they hit the A/B harness — no point burning impressions on copy you'd reject in review.
• Rewrite medium-risk variants with evidence spans as the prompt.
• Top low-risk variants enter the A/B test. Feed winners back into the brand-voice constraint.

Evidence spans agents should inspect

• 'generic_benefit' — 'helps your business grow' / 'unlocks better results' without specifying what changes
• 'unsupported_promise' — conversion or outcome claims with no proof anchor
• 'vague_trust' — 'trusted by leading brands' without naming them
• 'category_copy' — copy that could fit any SaaS, any DTC brand, any agency

Policy pseudocode

if (result.recommended_action === "allow") continueWorkflow();
if (result.recommended_action === "revise") rewriteWith(result.evidence, result.recommended_fixes);
if (result.recommended_action === "human_review") queueForHumanReview(result);
if (result.recommended_action === "reject") discardOrRebuild();

KPIs to track

• % of generated variants filtered before paid spend
• CTR / CVR delta between gated and ungated variants
• cost per acquisition over a 4-week test cycle
• landing page bounce rate by allow vs. revised-then-published
• revision pass rate (target: 60%+ on second pass)

What can go wrong

• VeracityAPI doesn't predict conversion. The signal is specificity, which correlates with performance but doesn't replace A/B testing.
• Highly emotional brand copy can be specific without being factual ('built for the founder who's tired of pretending'). Judge by evidence categories, not just by score.
• Compliance review still required for regulated industries. The score is a quality gate, not a legal gate.

Cost and latency notes

Analyze only is $0.005 per 1,000 characters; Analyze + revise with auto_revise=true is $0.010 per 1,000 characters. Both round up to the nearest 1,000 characters. Short captions/emails usually cost $0.005; longer pages or chapters scale linearly by length. Current v0.1 latency is LLM-bound, so batch/concurrent orchestration is recommended for high-volume pipelines.