Get direct, source-backed answers about checking an app for issues, unexpected behavior, launch readiness, authentication, billing, and the limits of static review.
Updated
Start with the question closest to the uncertainty you need to resolve.
Launch readiness
An AI-built app is ready to launch when its critical user journeys match explicit requirements, access and data boundaries are verified, billing and integrations are reconciled, high-risk runtime and security checks pass, and monitoring, backup, rollback, and ownership are ready. Launch with documented residual risk, not simply because the demo and build succeed.
Change verification
After an AI coding agent changes your app, confirm the requested outcome, inspect which files and dependencies changed, re-verify affected access, data, billing, and integration paths, and run focused regression tests. Do not assume a passing build proves the change is correct. Record the evidence, unresolved risks, deployment version, and rollback plan.
Founder guide
If you are not a developer, check an AI-built app by writing down the important user outcomes, testing those journeys with realistic accounts and data, and using a source-based verification tool to translate implementation behavior into plain language. Escalate access, billing, privacy, security, or data-loss concerns to a qualified specialist before launch.
AI bug checking
Yes. AI can help check an app for likely bugs by reviewing source code, tracing connected behavior, identifying contradictions, and proposing focused tests. It is most useful as one evidence source, not a guarantee. Runtime tests, security review, accessibility checks, performance testing, and production monitoring still cover issues that source analysis alone cannot prove.
Issue detection
To find hidden issues in an AI-generated app, look beyond the successful screen and inspect boundaries: who can perform each action, which state changes, what happens when dependencies fail, whether requests repeat, and how services recover. Trace those paths in source, then execute high-risk cases with realistic accounts, data, and configuration.
Behavior verification
To tell whether an AI-built app works as intended, turn each important requirement into an observable outcome, trace how the source implements it, and test that outcome in a realistic environment. Include allowed, denied, failed, delayed, and repeated cases. Record mismatches and unknowns instead of treating a working demo as complete proof.
Tool guide
Yes. App verification tools can help identify features that behave unexpectedly, risky implementation choices, and flows that need more testing. AppTruth reviews source code to explain what the app appears to do and where that behavior is implemented. It should be combined with runtime, security, accessibility, and performance tools for broader coverage.
Vibe coding
To check a vibe-coded app for issues, write down the behavior you expect, trace the critical journeys through the generated code, and test both successful and failed outcomes in a realistic environment. Prioritize authentication, permissions, data changes, billing, external integrations, errors, retries, and recovery before cosmetic details.
Verification guide
To verify your app for issues, list the critical behaviors it must perform, compare those expectations with the implementation, test the highest-risk journeys in a realistic environment, and record every mismatch or unknown with evidence and an owner. Start with access, money, data, external messages, failure recovery, and irreversible actions.
AI app verification
An AI app verification app helps you check whether AI-built or vibe-coded software behaves as intended. It reviews evidence such as source code, tests, or runtime activity and highlights mismatches, risky flows, and unknowns. AppTruth focuses on source-backed product behavior; testing and security tools provide additional runtime and specialist proof.
Detailed workflows for auditing connected behavior and preparing evidence for release.
Release readiness
An app behavior audit is a pre-launch review that compares what the product is supposed to do with what the implementation supports. It traces critical journeys across source, records evidence and uncertainty, and identifies the runtime or specialist checks still needed before release.
Billing guide
Verify Stripe subscription behavior by tracing checkout to the server-created billing object, validating webhook signatures, making event handling idempotent, mapping subscription states to product entitlements, testing retries and out-of-order events, and reconciling local state with Stripe. Source review maps the flow; Stripe test mode and event tooling provide runtime proof.
Verification guide
Verify authentication and authorization by inventorying identities and roles, tracing every protected action from interface to server and data layer, confirming permissions are enforced server-side, and testing allowed, denied, expired, revoked, and cross-tenant cases in a realistic environment. Source review finds the control paths; runtime tests prove they behave correctly.
Comparison
AppTruth explains source-backed product behavior across a repository. Code review evaluates implementation quality and change intent; automated tests execute encoded assertions; security scanners look for risk patterns or exploitable behavior; observability reports live-system telemetry. Teams use them together because no method produces all of those evidence types.
Checklist
A vibe-coded app release checklist should require evidence for the intended user journeys, server-side authorization, data handling, billing state, error recovery, runtime tests, security checks, accessibility, monitoring, backups, deployment parity, and rollback. Treat generated code as implementation to verify, not proof that the product requirement is satisfied.
Launch guide
To audit an AI-generated app before launch, define the critical user journeys, trace each journey through source code, verify access and side effects, test the highest-risk flows in a realistic environment, and record unresolved gaps with an owner. AppTruth can provide the source-based behavior map; runtime and specialist tools provide the remaining evidence.
Clear explanations of what AppTruth does, how scans work, and where the boundaries are.
Trust
Users can request AppTruth’s current security and privacy documentation through the signed-in support area or by emailing uriel@apptruth.io. Public policy links should be treated as authoritative once they are published on apptruth.io.
Company
AppTruth is an independently operated software product. Product communications and customer support are led by Uriel from AppTruth, reachable at uriel@apptruth.io and through signed-in support.
Boundaries
AppTruth does not directly test a running app, live service responses, secrets, external account configuration, or deployed infrastructure. It also does not claim exhaustive source coverage, execute tests, or replace security, performance, accessibility, compliance, and production-monitoring tools.
Results
An AppTruth scan produces a structured behavior report. Each finding can include a plain-language behavior, confidence score, impact level, likely cause, affected areas, file-based evidence, and an implementation-ready remediation prompt.
Data practices
AppTruth retains the account, integration, repository, billing, support, and scan-result records needed to provide the service. Prepared source artifacts and internal analysis artifacts are stored in encrypted private storage with a 30-day lifecycle; normalized scan results remain available in scan history until removed under applicable account and data processes.
Workflow
AppTruth is currently on-demand. A user chooses an approved GitHub repository and explicitly starts a scan when they want a fresh behavior review.
Method
No. AppTruth does not execute a repository’s test suite or run the application during a standard scan. It performs static analysis of approved source code and may read test files when they provide useful behavior context.
Boundaries
No. AppTruth is not a dedicated security scanner. It may identify security-relevant application behavior, such as missing access checks, but it does not replace SAST, DAST, dependency scanning, penetration testing, or a security review.
Trust
Yes. AppTruth’s repository scan access is read-only. It can read approved repository code and metadata, but it cannot push commits, edit files, change repository settings, or delete code.
Environments
AppTruth does not directly log into staging, test, or production environments during a standard scan. It reviews the selected repository’s default-branch source, so it can analyze environment-specific logic present in code without testing the live environment itself.
Connections
AppTruth currently uses a direct, read-only GitHub connection for repository scans. From that approved source, it can analyze code and configuration that integrate services such as Supabase, Stripe, Vercel, Clerk, Resend, AWS, Firebase, and Sentry.
Coverage
AppTruth can review behavior represented in a connected repository across frontend code, backend APIs, authentication and authorization, database access, payments, email, cloud configuration, background work, and error handling.
Audience
AppTruth is for anyone responsible for understanding or releasing an app: solo founders, vibe coders, AI-assisted developers, product managers, QA professionals, engineering leads, and agencies reviewing client software.
Category
AppTruth belongs to the AI app verification category. More specifically, it is an AI-powered application behavior audit and production-readiness review tool for software built with traditional or AI-assisted development workflows.
Product overview
AppTruth is an AI-powered app behavior verification platform. It reviews an approved GitHub repository, maps the product behaviors supported by the code, and returns evidence-backed findings in plain language so builders can understand what their app actually does.