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App Release Regression Intelligence
x402
App Release Regression Intelligence is a paid API for AI agents from api.timzinin.com, paid per call via x402, $0.08/call, status unknown (last checked 2026-09-14).
Analyzes app store reviews around a release date to detect rating regressions or quality degradations introduced by a specific app version
Price / call$0.08
Success—
StatusUnknown
How this service works
Pay-per-call data & tool APIs
Output
Returns a structured regression analysis report indicating whether the specified app release caused a statistically meaningful decline in user ratings or sentiment. Includes pre-release and post-release rating comparisons, a regression signal (detected/not detected/inconclusive), confirmation status, and optionally an LLM-generated explanation of what users complained about if explanation mode is enabled.
Example use cases
Post-launch regression alert for iOS app
“We shipped version 2.5.0 of our iOS app 'FitTrack' on 2025-04-01T00:00:00Z and I have 300 App Store reviews. Can you analyze them with a 21-day pre and post window to see if that release caused a meaningful drop in user ratings?”
Google Play quality check after hotfix
“We pushed a hotfix (v1.9.3) to Google Play for 'Budgetly' on 2025-02-10T00:00:00Z — here are 150 reviews. Run a regression analysis with 7 pre-days and 7 post-days to tell me if user sentiment got worse after that fix went out.”
Automated release QA gate for CI pipeline
“After every production deploy of our Android app 'ShopEase' (com.shopease.app), I want to check 500 recent Google Play reviews around the release timestamp to detect any rating regression — use a 30-day pre-window, 14-day post-window, and require at least 10 reviews to confirm.”
Technical details
Endpoint
POSThttps://api.timzinin.com/api/app-release-regression-intelligenceWhen to prefer
Choose this endpoint when you need to automatically detect whether a specific app release caused a quality regression based on real user reviews from Apple App Store or Google Play. It is purpose-built for correlating release events with review sentiment shifts, making it ideal for CI/CD quality gates, post-release monitoring, and mobile release health dashboards. Prefer this over generic sentiment analysis when you need version-aware, time-windowed regression detection with confirmation logic rather than just overall sentiment scoring.
Side effects
- Too few reviews in the pre or post window (below minReviews threshold) — returns inconclusive result
- Invalid version string format — returns schema validation error
- Release timestamp outside review date range — no data to compare
- openrouterApiKey missing when explanation mode is 'byok' — returns error requiring API key
- Exceeding 500 rows limit — request rejected with validation error
- Invalid platform enum value — rejected with schema error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| appId | string | |
| appName | string | |
| options | object | |
| watchId | string | |
| platform | string | |
| datasetId | string | |
| releaseAt | string | |
| requestId | string | |
| schemaVersion | string | |
| releaseVersion | — | |
| openrouterApiKey | string | |
| maxTotalChargeUsd | number |
Tags
x402
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