Pre-flight compliance checker for ad creatives targeting Meta, TikTok, and Google; flags policy risks before rejection. Growth teams at consumer subscription app studios pay $199–$499/mo. · Ad Compliance SaaS
STOP · SKIP
4.8 ShipScore / 10
Productized software helps time; ClearPolicy at $0 MRR and silence sink need.
Condition: Revisit only if 10+ subscription-app growth leads confirm rejections cost real money and pre-commit at $199+/mo.
Part I — The Verdict what the instruments read
01 — The ShipScore breakdown
Control
5
Policy rules are owned by Meta, TikTok, and Google, but no single API lifeline.
Entry barriers
4
LLM policy-checker is cloneable; lane open since ClearPolicy is stalled.
Need
3
Only tracked rival ClearPolicy shows $0 verified MRR; no Reddit or X demand.
Global, but subscription-app growth teams are a narrow buyer pool.
What the five commandments measure
C — Control
Do you own the customer, or does a gatekeeper stand between you and revenue?
E — Entry
How hard is this to copy once it works? A rival's existence isn't the question — the durability of their advantage is.
N — Need
Is demand proven with money rather than vibes?
T — Time
Can income detach from your hours, or did you buy yourself a job?
S — Scale
How far does it reach without linear cost?
Scored on the evidence below, against CENTS as we've adapted it plus a Buffett-style moat reading of every rival — the same referee for every idea, including the ones we generate ourselves. The verdict follows the arithmetic: we never relabel a score to make it read better, and the weakest commandment is always named. The exact rubric stays in-house so the scale can't be written for.
02 — The pace test — $1M/yr
At $299/mo (assumed category price), you need 279 subscribers for a $1M/yr pace.
Nearest tracked rivals on that curve:
ClearPolicy
$0k 0%
Part II — The Evidence the receipts, good and bad
03 — Rival scan — corpus tracks 1 similar, 1 stalled under $2k MRR
ClearPolicy — Same pre-flight compliance lane; its $0 verified MRR signals buyers aren't paying yet.
Meta/Google/TikTok native review — Free platform review and appeals are the default alternative, and they can add pre-checks anytime.
In-house ChatGPT prompts — Growth teams can paste a policy doc into an LLM for free, which caps willingness to pay.
04 — Demand evidence
The single tracked rival, ClearPolicy, shows $0 verified MRR, stalled under $2k.
No Reddit demand signals were found for these keywords.
X chatter is almost none: sparse ad-policy complaints and no product momentum.
No funded companies are tracked in or near this space.
05 — Risks
Rejection pain may be tolerated as a cost of doing business rather than paid to avoid.
Platforms could ship native pre-submission policy checks for free.
False negatives destroy trust fast: one approved-then-banned account ends the subscription.
Policy drift across three platforms creates heavy maintenance load.
06 — Kill switches — what kills this with one decision
Meta Ads Manager native pre-check — A free in-platform policy preview makes a third-party checker redundant overnight.
Platform policy opacity — Undisclosed enforcement models mean predictions miss, so accuracy can't be proven.
Part III — The Plan if you insist on proceeding
07 — What this scan can't tell you
The scan can't see real rejection rates or the cost of account bans for subscription-app studios.
One stalled rival may reflect poor execution, not absent demand.
The scan can't tell whether agencies or regulated verticals (health, finance) would pay more than app studios.
Scores judge the market and business model — not your ability to execute. Verified MRR = independently tracked revenue; reviews/upvotes = platform-reported. Judged 2026-10-16.
08 — The wedge & the next step
Narrow to the high-rejection claims in subscription apps (health, finance, weight-loss) and use a proprietary dataset of real rejection outcomes.
Cheapest next step: DM 20 growth leads at subscription app studios asking about last month's rejected ads and their cost; ask for a $199 pre-order.
Not quite it? Spin the model
Business-model variations of this idea, engineered to beat 4.8 — different vertical, audience, model, or wedge. Same harsh scale. Pro feature.
One evidence-backed gap a day, free
This one got a skip. Every morning the engine files one mined gap that could be a yes — idea, score, and what holds it back, free at 7am AEST. Unedited, like this verdict.
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