Launchieve
Launch readiness for AI-built products, before real users arrive.
About
Launchieve reviews products built with Cursor, Lovable, Bolt.new, Replit, Claude, Bubble and v0 for what breaks after launch rather than during the build. Its framing is that AI-built products rarely fail while being built, they fail when real users arrive, and the two failure paths it names are technical (fragile auth, weak permissions, scalability) and commercial (unclear positioning, onboarding friction). Two audits are published with prices. The Technical Launch Audit at $299 covers API and database inspection, security and stability checks, infrastructure and scalability, code quality and product readiness. The GTM Launch Audit at $399 covers positioning, messaging and onboarding for a product that already works. A third engagement, Complete My App, is quoted per project. The entry point is a free AI-assisted surface scan with no card, run against files you upload, on either the technical or the go-to-market side. Read from launchieve.com on 16 September 2026.
Services
Launch Readiness Scan
Free AI-assisted surface scan on either the technical or the go-to-market side, run against uploaded files with no card.
Technical Launch Audit
API and database inspection, security and stability checks, infrastructure and scalability review, code quality and product readiness.
GTM Launch Audit
Positioning, messaging and onboarding review for a product that already works but has not been launched to real traffic.
Complete My App
Hands-on completion of an AI-built product that stalled short of launch. Quoted per project.
Vibe Tool Expertise
Tech Stack
Problems This Agency Can Fix
AI coding tools often generate code with exposed API keys, missing input validation, broken authentication, and insecure data handling. These vulnerabilities can lead to data breaches, unauthorized access, and compliance failures.
Authentication is one of the most common failure points in vibe-coded apps. AI tools frequently generate insecure auth flows, missing session validation, broken password resets, and improperly configured OAuth.
AI-generated database schemas often lack proper indexes, have no Row Level Security, use inefficient query patterns, and create data integrity problems. These issues worsen as your app grows.
AI-generated codebases frequently have duplicated logic, inconsistent patterns, missing error handling, no TypeScript strict mode, and poor separation of concerns. This makes maintenance and feature additions increasingly difficult.
AI tools often generate API integrations with missing error handling, no retry logic, hardcoded endpoints, and insecure credential storage. These integrations break silently and are difficult to debug.
AI-generated UIs often look great on desktop but break on mobile devices. Missing responsive breakpoints, oversized images, touch-unfriendly controls, and fixed-width layouts create poor mobile experiences.
AI-generated apps often lack proper meta tags, structured data, semantic HTML, and server-side rendering. This makes them invisible to search engines and kills organic traffic potential.
AI-generated code often works locally but fails during deployment. Common issues include missing environment variables, incorrect build configurations, incompatible dependencies, and misconfigured hosting platforms.