How the Compliance Score works
Optimus Pass scans ad copy for policy-risk language across 10 platforms using 59 risk detectors. The current ruleset contains 59 enabled detectors. Here's how the scoring, weighting, and detection actually work under the hood.
How is the Optimus Pass Compliance Score calculated?
The Compliance Score comes from 59 risk detectors across claim language, personal wording, platform safety, restricted categories, and landing-page review signals. The active ruleset currently contains 59 detectors. Each flagged detector gets a base severity — critical (−30), high (−15), or medium (−5) — which is then multiplied by a platform-specific weight. For example, disease claims carry a 1.3x weight on OpenAI Ads but only 1.0x on TikTok. If three or more flags land in the same category, a 1.5x compounding kicker applies. The raw score gets normalized to a 0-100 scale (100 = lowest estimated risk). Beyond keywords, the engine also catches semantic patterns — recovery arcs, transformation claims, personal-attribute framing, emotional frustration targeting, and unsubstantiated authority — that no single banned word would ever trigger. The score helps you spot risk patterns worth reviewing. It doesn't predict whether a platform will approve or reject your ad.
How do ad review policies differ across platforms?
Every platform enforces different rules. Something that gets rejected on Meta might fly on TikTok, and vice versa. Meta cracks down hard on disease claims and clinical language (1.1x to 1.2x weight multipliers) but is more relaxed about exaggeration. TikTok has the highest exaggeration weight (1.3x) — its short-form video format makes overpromising claims more visible and harder to miss. OpenAI Ads applies the strictest scrutiny across the board, with disease claims at 1.3x and most other categories at 1.2x. Google Ads sits in the middle (1.0x to 1.1x across categories). LinkedIn has the lightest touch, with most categories at 0.9x to 1.0x — its professional context attracts a different type of enforcement. Keep in mind, these weights are estimates based on observed patterns. Actual enforcement depends on your account history, ad context, and region.
59 Risk Detectors
Each detector is a pattern — a term, phrase, or semantic structure — that correlates with higher ad-review risk on one or more platforms. Detectors are organized into five categories:
Clinical Language
7 detectorsPatterns in this category are directional review signals and may require contextual review.
Medical Claims
5 detectorsPatterns in this category are directional review signals and may require contextual review.
Disease Claims
4 detectorsPatterns in this category are directional review signals and may require contextual review.
Overpromise
4 detectorsPatterns in this category are directional review signals and may require contextual review.
Exaggeration
3 detectorsPatterns in this category are directional review signals and may require contextual review.
Outcome Claims
14 detectorsPatterns in this category are directional review signals and may require contextual review.
Personal Attributes
1 detectorsPatterns in this category are directional review signals and may require contextual review.
Platform Safety
6 detectorsPatterns in this category are directional review signals and may require contextual review.
Adult / Sexual Content
2 detectorsPatterns in this category are directional review signals and may require contextual review.
Hate / Harassment
3 detectorsPatterns in this category are directional review signals and may require contextual review.
Violence / Self-Harm
2 detectorsPatterns in this category are directional review signals and may require contextual review.
Financial Claims
1 detectorsPatterns in this category are directional review signals and may require contextual review.
Misleading Offers
1 detectorsPatterns in this category are directional review signals and may require contextual review.
Restricted Products
3 detectorsPatterns in this category are directional review signals and may require contextual review.
Urgency
1 detectorsPatterns in this category are directional review signals and may require contextual review.
Endorsements
1 detectorsPatterns in this category are directional review signals and may require contextual review.
Authority Claims
0 detectorsPatterns in this category are directional review signals and may require contextual review.
Other
1 detectorsPatterns in this category are directional review signals and may require contextual review.
Semantic Patterns
Beyond keyword detection, Optimus Pass analyzes the narrative structure of ad copy. Narrative patterns catch implied claims that no single word triggers. Examples include:
- •Recovery Arc: "I finally feel like myself again" — zero banned terms, but implies recovery from a negative state.
- •Transformation Claim: "See results in 7 days" — implies a specific outcome within a timeframe.
- •Personal-Attribute Framing: "For people who struggle with focus" — targets a personal attribute, which some platforms restrict.
- •Emotional Frustration Targeting: "Tired of low energy?" — exploits a negative emotional state as a hook.
- •Unsubstantiated Authority: "Doctors recommend" — implies endorsement without evidence.
Platform Weighting
Each platform enforces different policies. A term that triggers rejection on Meta may pass on TikTok. Optimus Pass applies platform-specific weights to each detector based on observed policy patterns:
| Category | Meta | TikTok | OpenAI | ||
|---|---|---|---|---|---|
| Disease Claims | 1.2x | 1.1x | 1.0x | 1.3x | 1.0x |
| Medical Claims | 1.1x | 1.0x | 1.0x | 1.2x | 0.9x |
| Clinical Language | 1.1x | 1.0x | 0.9x | 1.2x | 0.9x |
| Overpromise | 1.0x | 1.1x | 1.2x | 1.2x | 1.0x |
| Exaggeration | 1.0x | 1.1x | 1.3x | 1.2x | 0.9x |
Weights are directional estimates based on observed policy patterns and enforcement signals. Actual enforcement varies by account history, context, and region. A higher weight means the platform is estimated to scrutinize that category more heavily.
Compliance Score Calculation
The Compliance Score is a directional indicator, not a pass/fail guarantee. It is calculated as:
- Each flagged detector receives a base severity: critical (−30), high (−15), or medium (−5).
- That severity is multiplied by the platform-specific weight for the detector's category.
- If 3+ flags are detected in the same category, a 1.5× compounding multiplier is applied.
- Scores are normalized to a 0-100 scale, where 100 = lowest estimated risk.
Important: The Compliance Score is directional. It estimates the number and severity of risk patterns detected. It does not predict whether a platform will approve or reject an ad. Platform enforcement decisions are made independently and may change without notice.
Data Sources & Updates
Optimus Pass detectors are based on:
- Published platform advertising policies (Meta, Google, TikTok, LinkedIn, X, Reddit, Etsy, OpenAI)
- Confirmed enforcement signals from advertiser reports and appeals
- Industry compliance research and regulatory guidance (FDA, FTC, ASA)
- Pattern analysis of rejected versus approved ad copy
Risk patterns are updated as policy language and enforcement signals change. There is no automated update schedule — updates are published when confirmed changes are identified.
Ruleset 2026-07-24-enforcement-risk-v1 · 59 enabled detectors
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