Demystifying the
Approval Process
Our mission is to help you ship faster and reduce rejection risk. We use continuously refreshed validation datasets and verified rejection evidence to turn a black-box process into a measurable pre-submission check.
The Foundation
We codified Apple and Google policy rules that are practical for static analysis, from manifests and permissions to metadata and policy-sensitive code patterns, including Apple 1.2 user-generated content controls.
The Engine
We run evidence-first detection and compare outputs against validation sets and verified rejection examples to tune confidence, severity, and rule behavior.
Continuous Calibration
Rules are regularly updated as policies change. Each rule is assigned a precision tier (Definite, Likely, or Advisory) based on detection confidence, helping you focus on what matters most.
By the Numbers
Our scanner is calibrated using real-world data, not theoretical guidelines.
Precision-Based Tiers
Not all detections are equal. We assign each rule a confidence tier based on detection precision.
Accuracy rankings treat support=0 FP-only rules as a separate pathology list, not part of normal worst-rule F1 ordering.
Configuration checks and file existence rules. If triggered, this is strongly associated with rejection risk and should be fixed first.
Multi-signal pattern detection. High probability of rejection based on pattern correlation across code.
Broader patterns and heuristics. Flagged for manual review—false positives are possible.
The Vision Engine
Reviewers don't just read code; they look at your app. We now use multimodal AI to "see" what they see, catching visual compliance issues before submission.
Context-Aware Scanning
Our engine doesn't just look for "Lorem Ipsum". It understands context. It knows that a "Login" screen is fine, but a screenshot showing only a login screen (Guideline 2.3.3) is a rejection.
Cross-Verification
We correlate code presence with visual evidence. Only here can we detect if your code contains In-App Purchases (Guideline 3.1.2) but your screenshots fail to show a compliant paywall.
Metadata Validation
Most rejections aren't caused by code bugs, but by "unforced errors" in App Store Connect. We automatically parse your Fastlane configuration to catch these before you even open a browser.
Privacy & Lexical Checks
We verify your Privacy Policy URL's reachability and scan your app description for accidentally pasted legal jargon (EULA) that triggers Guideline 2.3.1.
Screenshot Completeness
Our engine checks if you've included all required device sizes for both iPhone and iPad, preventing the "Missing Screenshots" roadblock.
Submission Readiness
Automatic validation of review notes, demo account credentials, and release note quality to improve submission readiness before manual review.
Radical Transparency
No scanner is perfect. We believe you deserve to know exactly where our blind spots are. While we provide industry-leading coverage for code, privacy, and metadata issues, we cannot replicate the human elements of App Review.
We catch many API/config issues with high signal quality; critical paths still require manual verification.
Deep inspection of manifests and capabilities.
We cannot see dynamic content or server responses.
We can't judge design aesthetics or app value.