AI-Native Release Intelligence

AI-native apps
fail differently.

Brama identifies hidden production, trust, onboarding, accessibility, AI-behavior, and platform-review risks before launch.

AI accelerated software development. It did not accelerate release judgment.

Human-reviewed findingsAI-native & web appsiOS & Android buildsUsed before launch, not after failure

The problem

Why AI-native apps fail differently.

AI-generated software introduces new operational risks traditional workflows were never designed to catch.

Tools like Cursor, Lovable, Bolt, Base44, and Rork help teams build faster. They don't add release judgment. That gap is where Brama operates.

Fake / demo functionality shipped to production

Hardcoded placeholder names, mock API responses, or sample datasets left in production builds — a direct App Store rejection risk and a trust failure with real users.

Hallucinated AI outputs

Generated responses or summaries presented as real without validation — including fabricated references, hallucinated data, or placeholder outputs that reached production silently.

Inaccessible generated UI

Screen reader labels omitted, tap targets undersized, color contrast failing — systematic gaps introduced when AI generates UI without accessibility awareness.

Broken onboarding states

AI-generated onboarding sequences that skip consent steps, duplicate state, or drop users into dead ends — common in apps built with code-generation tools.

Localization failures

RTL layout breaks, AI-generated translation mismatches, truncation in unsupported locales, and timezone or date formatting errors that only surface in specific markets.

Misleading AI feature claims

Features prominently labeled 'AI-powered' that return static or pre-generated results — a direct App Store guideline violation and a user trust risk.

Privacy disclosure mismatches

Apps requesting runtime permissions that don't match declared privacy labels — often introduced by AI-generated module scaffolding that copies patterns without context.

AI-generated dead-end flows

Polished UI hiding broken core flows — purchase paths that drop silently, auth states that don't persist, error handling that never surfaces to the user.

These aren't hypothetical scenarios. They are operational patterns Brama reviews identify specifically — findings traditional release workflows aren't structured to surface.

Positioning

Not a replacement for QA.

A new operational layer AI-native development created.

Traditional QA was built for software teams with stable specs and documented requirements. AI-generated codebases need a different kind of pre-release review.

Validates that features work as specified
Validates release integrity across production, trust, onboarding, and platform risk
Regression-focused — catches changes from known baselines
Identifies hidden launch risks with no prior baseline required
Tests expected, documented behavior
Reviews AI-native failure patterns that don't appear in spec
Relies on stable requirements and reproducible test cases
Works even with minimal documentation or AI-generated codebases
Focuses on bugs and regressions
Analyzes trust signals, AI behavior, onboarding integrity, accessibility, and platform risk
Usually run by developers or internal QA teams
An external release intelligence layer — independent of the build team

Brama and QA are not in competition. QA validates implementation. Brama validates release integrity — the operational layer AI-native teams are missing.

Where Brama fits

The AI-native release gate.

AI-native apps are shipping without the operational layers traditional software teams used to have. Brama fills that gap — before deployment, not after failure.

Idea & Design

AI Generation

Cursor · Lovable · Bolt · Base44 · Rork

Build

Internal QA & Validation

Unit tests · Staging · Internal review

Brama Release Gate

Production-readiness · AI behavior · Trust · Onboarding · Platform risk

Operational validation before public release

Release Intelligence Layer

Release Approval / Deployment

App Store · Google Play · Web production

Production

Brama operates between internal testing and public release — identifying risks that only become visible at the release boundary.

Review coverage

What Brama reviews

Structured across the dimensions that actually determine whether an AI-native app ships safely — and survives in production.

Web Apps & AI-Native Products

Live URL review for production-readiness, runtime behavior, onboarding integrity, accessibility, AI trust risks, privacy signals, responsive and foldable layouts, and future native app expansion risks.

App Store & Google Play Risks

Store guideline signals, metadata accuracy, review blocker detection

Accessibility & Inclusive UX

Screen reader support, contrast, tap targets, missing labels

AI Behavior & Trust Risks

Simulated outputs, misleading AI claims, hardcoded demo data, hallucination signals

Production Readiness

Error states, unhandled failures, build hygiene, data validation gaps

Privacy & Permission Risks

Over-requesting permissions, privacy disclosure gaps, data handling signals

Regulated & High-Risk Categories

Additional review strictness for healthcare, fintech, child safety, mental health, crypto, and regulated experiences.

Fake / Placeholder Functionality

Demo data shipped to production, non-functional primary flows

Release Build Hygiene

Debug artifacts, exposed keys, store metadata inconsistencies

Process

How it works

01

Submit your app materials

GitHub repo, APK/IPA, screenshots, or a build link. You control what you share — more access means deeper findings.

02

We run structured release-intelligence analysis

AI-assisted analysis combined with human review judgment — covering production risks, AI-behavior signals, trust integrity, onboarding failures, accessibility gaps, and platform-specific risks.

03

You receive a prioritized release-risk report

P0/P1/P2 findings with clear severity, production impact, and recommended actions. No generic checklists — only what actually affects your release.

Most release risks are invisible until they reach a store reviewer or a real user. Brama surfaces them first.

Review depth

What each artifact enables

Review depth depends on the artifacts provided. More context enables deeper findings.

URL / Live Web App

Enhanced with runtime access

Runtime behavior analysis
AI-output review
Responsive layout validation
Accessibility & onboarding review
Browser interaction checks
Production trust analysis

Best for web apps, AI-native products, and pre-native expansion reviews.

APK / XAPK / AAB / IPA

Strong mobile runtime coverage

Platform-risk analysis
Runtime validation
Accessibility review
Store-readiness indicators
Production behavior analysis

Enhanced further with runtime/demo access.

GitHub / ZIP

Architecture & implementation visibility

Release-risk detection
Runtime assumption analysis
Environment/configuration review
Dependency and implementation signals

Works best when combined with runtime or build artifacts.

Screenshots / Video

Partial UX & trust review coverage

Onboarding integrity
Accessibility observations
Trust & UI consistency
Localization signals
Production-state analysis

Best for early-stage or lightweight reviews.

TestFlight / Runtime

Deepest release-risk visibility

Full runtime behavior validation
AI-output integrity
Interaction-state analysis
Real accessibility behavior
Production environment signals

Provides the highest review depth across all dimensions.

Some findings may require runtime validation depending on the artifacts provided.

Coverage

Review Coverage Matrix

Different artifacts provide different levels of release visibility.

Deeper runtime access increases release visibility and review accuracy.

Platform / Surface

Coverage

AI-native Web Apps

High Visibility

AI SaaS Platforms

High Visibility

iOS

High Visibility

Android

High Visibility

PWAs

High Visibility

Smart TVs / Media Platforms

Partial Visibility

XR / Smartglasses

Partial Visibility

Automotive Systems

Partial Visibility

Artifact depth matrix

Production-readiness

Screenshots/Video

Partial

APK/AAB/IPA

Strong

GitHub/ZIP

Strong

Runtime/URL

Strong

Runtime behavior

Screenshots/Video

Limited

APK/AAB/IPA

Partial

GitHub/ZIP

Limited

Runtime/URL

Strong

AI-output validation

Screenshots/Video

Partial

APK/AAB/IPA

Partial

GitHub/ZIP

Partial

Runtime/URL

Strong

Onboarding integrity

Screenshots/Video

Partial

APK/AAB/IPA

Strong

GitHub/ZIP

Partial

Runtime/URL

Strong

Accessibility

Screenshots/Video

Partial

APK/AAB/IPA

Partial

GitHub/ZIP

Partial

Runtime/URL

Strong

Privacy / permission review

Screenshots/Video

Partial

APK/AAB/IPA

Strong

GitHub/ZIP

Strong

Runtime/URL

Strong

Architecture review

Screenshots/Video

Limited

APK/AAB/IPA

Partial

GitHub/ZIP

Strong

Runtime/URL

Partial

App Store readiness

Screenshots/Video

Strong

APK/AAB/IPA

Strong

GitHub/ZIP

Partial

Runtime/URL

Strong

Localization signals

Screenshots/Video

Partial

APK/AAB/IPA

Partial

GitHub/ZIP

Partial

Runtime/URL

Strong

Trust & safety signals

Screenshots/Video

Partial

APK/AAB/IPA

Partial

GitHub/ZIP

Partial

Runtime/URL

Strong

High Visibility
Partial Visibility
Limited Visibility

Niche-aware

Niche-Aware Review Intelligence

Brama adapts review logic based on app category instead of running a generic release scan.

Core release-risk analysis runs across every app. Brama adds additional niche-aware review logic where relevant.

Healthcare / Medical

HIPAA-related signals
Sensitive onboarding review

Fintech

Payment-flow interruption risks
Financial trust UX

Travel

Localization & translation risks
Timezone/date logic

Gaming

COPPA-related signals
Account/reward-flow validation

Crypto / Web3

Wallet connection safety
Misleading balance/output risks

Social / Creator Apps

Moderation/reporting gaps
Upload-state failures

AI Productivity / AI Agents

Hallucinated outputs
Fake/demo AI responses

Smart Devices / XR / Wearables

Glanceable interaction review
Voice-first interaction risks

Fit

Who this is for

Teams shipping AI-native software accelerated by Cursor, Lovable, Bolt, Base44, Rork, or similar AI tooling
Founders preparing for App Store or Google Play submission
Teams launching web apps to real production users for the first time
Teams recovering from App Store or production release failures
Engineers validating a build before public launch
Product teams that need external release-integrity validation before shipping
Not for coursework, design drafts, or pre-prototype experiments. Brama is a production-readiness and release-intelligence service — best used at the launch boundary.

Security & trust

Your code and data stay yours

We do not claim ownership of your code, assets, models, or product ideas
Files removed after review completion — not stored permanently
Materials are never used to train public AI models
GitHub access is read-only only. Access can be revoked anytime.

You don't need to share everything to get started. Audit depth scales with what you provide — most critical findings surface early.

Pricing

Release intelligence without enterprise complexity.

Start with a single release. Go deeper when your product needs it. Billed monthly, cancel anytime.

Automated

Release Scan

$29

/mo

Typically within 24 hours

Fast first-pass release-risk analysis for teams that want to identify obvious risks before shipping.

URL, GitHub/ZIP, APK/IPA or available artifact
Core production-risk analysis
Architecture/configuration signals where available
AI-generated software risk signals where applicable
Platform and release-risk signals
Prioritized high-confidence findings
Evidence for surfaced findings
BRAMA Release Risk Score
MOST POPULAR

AI-assisted + human reviewed

Release Review

$99

/mo

Typically 1–2 business days

Comprehensive release intelligence before production.

Everything in Release Scan
Full prioritized findings
Production readiness
Onboarding integrity
Accessibility risk signals
Privacy & permissions
Localization risks
AI behavior & trust when applicable
Platform-review risks
Fake/demo/placeholder functionality signals
Release build hygiene
Remediation guidance
Full BRAMA Release Intelligence Report

Human-reviewed

Runtime Review

$199

/mo

Typically 2–4 business days

Deeper validation of what actually happens when users interact with the product.

Everything in Release Review
Real runtime validation
Critical user journeys
Onboarding behavior
Empty/loading/error/offline states
Permission behavior
Device/runtime behavior
AI-output behavior where applicable
Cross-screen consistency
Payment/subscription flows where applicable
Evidence and reproduction steps
Release recommendation

Custom scope

Enterprise

Custom

Confirmed after scope review

For complex, recurring and multi-platform release intelligence.

Recurring release analysis
Multiple apps/platforms
CI/CD integration
Advanced governance workflows
Specialized environments
Custom review scope
Organization-level reporting

BRAMA automatically determines which intelligence modules apply based on your product, platforms, artifacts, and risk profile. You choose the depth — BRAMA handles the rest.

Adaptive Intelligence

BRAMA adapts the review to what you're shipping.

You don't need to choose every type of analysis yourself. BRAMA identifies the relevant platforms, product characteristics, artifacts and risk areas, then applies the appropriate release intelligence.

Production ReadinessAI Behavior & TrustApp Store & Platform RiskAccessibility & Inclusive UXPrivacy & PermissionsLocalizationOnboarding IntegrityFake / Placeholder FunctionalityRelease Build HygieneMobile / Tablet / FoldableWearables / XR / Smart DevicesHealthcare & High-Risk Categories

Niche-aware intelligence

HealthcareFintechTravelGamingCrypto / Web3Social / Creator AppsAI Productivity / AgentsEmerging Devices

These are review dimensions, not separate paid products. BRAMA activates the relevant intelligence automatically based on your submission.

Early feedback

Used by AI-native teams preparing for production release.

Trav****te.ai

TravelAI-nativeMulti-platform

We thought the app was launch-ready until Brama surfaced multiple hidden runtime and App Store review risks our team completely missed.

– Founder, AI Travel App

RemiM**** AI

HealthcareAI-nativeAndroid

We expected a generic scan, but the review actually understood the sensitivity of healthcare-style onboarding and accessibility expectations. It helped us validate several runtime and UX risks before release.

Paramita M.

Looki*****s

TransportationAndroidMobility

Brama caught multiple release and runtime issues we honestly would've missed before another production push. The report exposed outdated Android assumptions, compatibility gaps, and operational risks tied to our deployment flow.

Nirit A.

Wizj***V

Streaming TVIPTVDune Media Platform

We originally expected a basic APK scan, but the review uncovered major playback and platform compatibility risks tied to older TV environments and legacy middleware assumptions. Several findings would've been extremely difficult to diagnose internally.

Private Operator

Ra**

SocialMediaAI-native

Even after scaling past 100K+ users, we continued facing App Store review friction we couldn't fully explain. Brama identified hidden release-risk patterns and platform-review gaps that our team believed were already covered.

– Product Team, Ra**

Dee***r

Audio StreamingAutomotiveMulti-platform

The automotive review helped us identify gaps between expected Android Auto behavior and what the submitted build was actually exposing at the artifact level. The report clarified several compatibility assumptions and highlighted modernization risks that could've impacted future automotive integrations and platform support.

Private Product Team

Ready to ship?

Know your risks before your users do.

Submit your app and receive a prioritized findings report — release blockers, production-risk signals, and AI behavior findings — before you ship.

Most release-critical issues are invisible until production. AI can accelerate shipping — Brama helps prevent expensive surprises after launch.