Questions? We've got answers.

You built an app with AI (Cursor, Bolt, v0, Lovable, Replit, etc.) and it looks great, but something is off. Maybe it crashes. Maybe the auth is broken. Maybe you're stuck in a loop where fixing one thing breaks two others. We take your existing code, figure out what's wrong, and fix it so it actually works in production. We don't start over. We work with what you've got.

It starts with a free code audit. We hop on a 30-minute call, look at your codebase, and tell you exactly what's broken and what it'll take to fix. Then we give you a plan with a fixed price and timeline. Most projects take 1 to 3 weeks. You get daily updates and a production-ready app at the end.

Every project is different, so we quote after the free code audit. Some apps need a quick tune-up, others need serious surgery. We'll always give you a clear scope, a fixed price, and a timeline before any work starts. No hourly billing. No surprise invoices. You know exactly what you're paying for.

All of them. We've fixed apps built with Cursor, Bolt.new, v0, Lovable, Replit, Windsurf, and more. On the framework side, we handle Next.js, React, Node.js, Python, Django, FastAPI, and databases like Postgres, Supabase, and Firebase. We deploy to Vercel, Railway, Fly.io, and AWS. If AI can build with it, we can fix it.

Absolutely. Most of our clients are founders, solopreneurs, and creators who used AI tools to build something without a coding background. You don't need to understand the code — that's literally our job. We'll walk you through everything in plain English, keep you updated daily, and hand you back something that works.

We've never seen one that was. Seriously. Some need more work than others, but there's always a path forward. During the free audit, we'll be straight with you about the effort involved. And if a rebuild truly makes more sense than a fix, we'll tell you that too. We'd rather be honest upfront than charge you for a rescue that doesn't make sense.

You can try. Most people do. That's usually how they end up here. The problem is that AI is great at writing code fast, but terrible at debugging the systemic issues it created. You ask it to fix one bug and it confidently introduces two more. Research shows AI-generated code has 1.7x more issues than human-written code. Security vulnerabilities are up to 2.7x higher. At some point you need a human who understands architecture, not just pattern matching.

This is one of the most common vibe coding issues we see. AI tools often generate code that works perfectly in a development environment but fails in production. The causes range from hardcoded localhost URLs and missing environment variables to incorrect build configurations, missing API keys, and CORS policy violations. We audit your deployment pipeline end to end and make sure it runs properly on Vercel, Railway, Fly.io, AWS — wherever you're hosting.

Not normal, but extremely common with AI-generated code. AI tools tend to write code that loads everything into memory instead of paginating, uses redundant API calls, creates N+1 database query problems, and doesn't implement caching. Over time this compounds into an app that works with 10 users but dies with 100. We optimize your queries, add proper caching, and restructure the data flow so your app can actually scale.

This is the classic vibe coding death spiral. AI tools don't understand the full architecture of your app — they're pattern-matching on the snippet you give them. When they fix one thing, they often overwrite shared state, break component dependencies, or introduce conflicting logic elsewhere. The fix isn't more AI prompts — it's a human who can trace the entire codebase, understand the interconnections, and make surgical fixes that don't cascade.

Very serious. Auth bugs are one of the most dangerous issues in AI-generated code. We've seen apps where every user was treated as an admin because of a simple assignment vs. comparison error the AI introduced. Others had broken JWT implementations, missing role checks, or session handling that expired randomly. We fix your auth properly — role-based access control, secure token handling, proper session management — the works.

AI tools often generate database rules that look correct on the surface but have subtle permission gaps. Common issues include overly permissive row-level security policies, missing auth checks on certain tables, and rules that grant read access to all authenticated users instead of just the data owner. We audit every rule, lock down your data access, and test edge cases the AI never considered.

AI tools tend to generate layouts that look perfect on the screen size they were tested on — usually a desktop monitor. Mobile responsiveness issues include overlapping elements, unreadable text, broken navigation menus, touch targets that are too small, and forms that extend past the viewport. We fix your responsive design so it works seamlessly across all devices and screen sizes.

Payment integration is one of the trickiest things to get right, and AI tools frequently get it wrong. Common issues include missing webhook verification, no idempotency keys causing duplicate charges, incorrect handling of Stripe/PayPal return URLs, and race conditions in credit/subscription logic. We fix your payment flow end to end — webhook handling, error recovery, receipt generation, and edge cases like failed charges and refunds.

Probably not as secure as you'd want. Research from GitClear and academic studies consistently show AI-generated code has significantly higher rates of security vulnerabilities — up to 2.7x more than human-written code. Common issues include SQL injection, cross-site scripting (XSS), missing input validation, exposed API keys, insecure direct object references, and broken access control. Our rescue process includes a full security audit and remediation.

Yes — this is a critical security issue and one of the most common mistakes in AI-generated projects. If your API keys, database credentials, or secrets are committed to a Git repository (even a private one), they should be considered compromised. We'll rotate all exposed credentials, move them to proper environment variables, set up a secrets management system, and add safeguards to prevent future exposure.

Most AI-generated apps don't handle privacy compliance at all. The AI doesn't add cookie consent banners, privacy policies, data deletion endpoints, or proper consent management unless explicitly asked — and even then it often gets it wrong. We review your data collection, add necessary compliance features, implement proper consent flows, and make sure your app respects user privacy according to GDPR, CCPA, and other relevant regulations.

Yes. These are two of the most common vulnerabilities in AI-generated code. AI tools sometimes generate raw SQL queries using string concatenation instead of parameterized queries, and output user input directly into HTML without sanitization. Both are trivially exploitable. We audit every database query and user input handling path, implementing parameterized queries, input validation, output encoding, and Content Security Policies.

Signs include unexpected user behavior, unexplained database changes, spikes in API usage, unfamiliar admin accounts, or customers reporting issues you can't explain. If your AI-generated code had exposed credentials or security gaps, it's possible someone exploited them without you knowing. We perform a thorough forensic analysis, check for indicators of compromise, and lock down your app while preserving any evidence.

It matters more than you think. Messy code — what developers call 'technical debt' — means every future change takes longer, costs more, and is more likely to break something. AI-generated code is particularly prone to this: it duplicates logic across files, uses inconsistent patterns, creates circular dependencies, and violates the DRY principle (Don't Repeat Yourself). GitClear's research showed AI-written code has significantly higher 'churn rates' — meaning it gets rewritten much sooner. We refactor your code into a clean, maintainable structure.

Absolutely. Once we've stabilized your codebase, adding features is straightforward. In fact, many clients come to us thinking they just need a fix, but what they really need is a stable foundation to build on. We clean up the architecture, and then new features plug in cleanly instead of creating the cascading breakage you experienced before.

This is where AI-generated code often falls apart. AI tools write for the 'happy path' — a single user testing locally. They don't implement database indexing, query optimization, caching strategies, rate limiting, or connection pooling. When traffic increases, the app slows to a crawl or crashes. We restructure your backend for real traffic — optimized queries, proper caching, CDN setup, and load-tested infrastructure.

The hosting provider is rarely the problem — the code usually is. That said, each platform has strengths: Vercel excels at Next.js apps, Railway is great for full-stack deployments, Fly.io handles globally distributed apps well, and AWS offers maximum flexibility. We'll recommend the right platform for your specific needs and make sure your deployment is properly configured, including cold start optimization, serverless function limits, and database connection management.

It's not ideal, but it's the reality for virtually every vibe-coded app. AI tools almost never generate proper tests unless specifically asked. The problem is that without tests, every change is a gamble — you can't know if you broke something until a user reports it. We add the critical tests your app needs: authentication flows, payment processing, core business logic, and API endpoints. Not exhaustive test coverage for the sake of it, but targeted tests that catch the things that matter.

Yes, database migrations are something we handle regularly. Whether you're moving from Supabase to a self-hosted Postgres, from Firebase to Supabase, or from MongoDB to a relational database, we manage the full migration — schema translation, data transfer, updating all queries and ORM code, and making sure nothing is lost in the process. We test exhaustively before cutting over to ensure zero data loss.

Absolutely. Most AI-generated apps deploy via manual pushes or basic Vercel auto-deploy with no guardrails. We set up proper CI/CD pipelines — automated testing on every commit, staging environments for review, branch-based deployments, and production safeguards. This means you can keep iterating (even with AI tools) without the fear that a bad deploy will take down your live app.

Yes, Cursor-built apps are one of our most common rescue projects. Cursor is powerful but tends to produce code that drifts architecturally over long sessions. Common issues include duplicated logic across files, inconsistent state management, broken imports after refactoring, and environment configuration issues. We've fixed hundreds of Cursor-generated codebases and know exactly what to look for.

Bolt.new excels at generating impressive-looking prototypes quickly, but the generated code often has superficial functionality — forms that don't submit data, buttons that don't connect to backend logic, authentication that doesn't persist, and API routes that return static data. We turn your Bolt prototype into a working production app by wiring up the real backend logic, database connections, and third-party integrations.

v0 generates beautiful React UI components but they're essentially standalone — they look like they work but aren't wired to any data source. The gap between 'beautiful UI' and 'functional app' is where most people get stuck. We connect your v0 components to real APIs, databases, and authentication systems, and make sure the data flows correctly between your frontend and backend.

This is a common pattern with AI-assisted app builders. When you try to add new features or make changes, the tool sometimes rewrites parts of the codebase that were already working. Without version control or proper architecture boundaries, one update can cascade through the entire app. We untangle the broken state, restore functionality, and add proper version control so this doesn't happen again.

Yes. Replit is great for prototyping and initial builds, but many founders outgrow it and want a more production-ready hosting setup. We handle the full migration — extracting your code, setting up a proper repository, configuring your own hosting on Vercel, Railway, AWS, or wherever makes sense, setting up custom domains, SSL, environment variables, and everything else needed for a professional deployment.

Yes. We support apps built with any AI coding tool — Windsurf, Codeium, GitHub Copilot, Amazon CodeWhisperer, Tabnine, Cody, and any others. The underlying issues are the same regardless of which tool generated the code: architectural inconsistencies, security gaps, missing error handling, and scalability problems. We fix the code, not the tool.

The audit is a 30-minute call where we screen-share and walk through your codebase together. We identify the major issues — broken functionality, security vulnerabilities, architectural problems, and performance bottlenecks. After the call, we send you a summary of findings and a clear proposal: what needs to be fixed, how long it'll take, and exactly what it'll cost. No obligations, no hard sell.

Most projects take 1 to 3 weeks. A simple bug fix or deployment issue can sometimes be resolved in a few days. A full architectural rescue — fixing auth, database, payments, and deployment — typically takes 2 to 3 weeks. Complex apps with multiple integrations, real-time features, or significant security remediation may take a bit longer. We'll give you a clear timeline in the proposal.

We work transparently throughout. You get daily progress updates, access to a shared project board, and we're available for questions via your preferred communication channel. For larger projects, we schedule weekly review calls so you can see the progress, test features, and provide feedback. You're never in the dark about where things stand.

You get a production-ready app that actually works. Specifically: your codebase cleaned up with proper structure, all identified bugs fixed, security vulnerabilities patched, documentation of what was changed and why, deployment instructions, and a handoff call where we walk you through everything. If you use Git, all changes are properly committed with clear messages so you have full history.

Yes. Many clients keep us on a retainer after the initial rescue — typically for continued feature development, on-call support, or periodic security audits. We offer flexible maintenance plans based on your needs. Some clients need us weekly, others monthly, others only when something breaks. We'll work out whatever arrangement makes sense for you.

We include a 2-week post-delivery support period for bug fixes and minor adjustments at no extra cost. If you need additional feature development beyond the original scope, we'll give you a new quote. Because we've already stabilized your codebase, future work is typically faster and more affordable than the initial rescue.

Yes — this is actually one of our most requested services. Investors and technical due diligence teams will scrutinize your codebase. An app full of AI-generated spaghetti code raises red flags. We clean up the architecture, add proper security, write documentation, and make sure your technical foundation can withstand scrutiny. Several of our clients have successfully raised funding after we stabilized their apps.

Much easier. One of the biggest problems with AI-generated codebases is that they're nearly impossible for new developers to navigate — inconsistent patterns, no documentation, tangled dependencies. After our rescue, your code follows industry-standard conventions, has proper documentation, and is structured in a way any competent developer can pick up and run with. We're essentially making your app hirable-friendly.

Absolutely. We sign NDAs regularly and treat all client code as strictly confidential. We don't share your code, your business logic, or any proprietary information. All access is revoked after project completion unless you explicitly want us to stay on for maintenance. Your IP stays yours.

Yes. We understand the urgency when you have live users and revenue at stake. We work on a separate staging environment, test everything thoroughly, and then deploy changes with zero or minimal downtime. For critical fixes — like payment bugs or security vulnerabilities — we can do emergency hotfixes. We'll coordinate deployment timing with you to minimize any impact on your users.

Three things. First, we specialize exclusively in rescuing AI-generated code — we've seen the patterns hundreds of times and know exactly what to look for. A general freelancer is starting from scratch on diagnosing vibe-code issues. Second, fixed pricing with guaranteed deliverables beats hourly billing where scope creep eats your budget. Third, we deliver a complete rescue — from security audit to production deployment — not just one piece of the puzzle.

AI tools are getting better at writing first drafts. They're not getting better at understanding your specific business logic, fixing their own systemic bugs, or doing architectural thinking. The 'fixing one thing breaks two others' problem isn't going away because it's a fundamental limitation of pattern matching without system understanding. Also, your app needs to work now — not in six months when tools might be better. Your users, your investors, and your revenue can't wait.

Traditional dev agencies want to rebuild from scratch—it's more billable hours. We work with your existing codebase. We understand AI-generated code patterns because it's all we do. We're faster because we're not learning your stack; we already know what Cursor, Bolt, and v0 produce. And we give you fixed pricing instead of open-ended hourly retainers. Think of us as the specialist vs. the generalist.

Vibe coding is a term coined by AI researcher Andrej Karpathy in early 2025. It describes a workflow where you describe what you want in natural language and an AI tool writes the code. You 'give in to the vibes' and forget the code exists. It exploded in popularity with tools like Cursor, Bolt.new, v0, and Lovable making it accessible to non-developers. The catch? It works brilliantly for prototypes and demos, but the resulting code often has deep issues that surface when you try to go to production.

The data says yes, in several measurable ways. GitClear's analysis of 153 million lines of code found that AI-era code has significantly higher churn rates — meaning it gets rewritten much sooner after being written. Code duplication increased, while code reuse and refactoring decreased. Academic studies show AI-generated code has 1.7x more defects and up to 2.7x more security vulnerabilities. The code looks right but often isn't — it's confidently incomplete.

Not anytime soon. AI has collapsed the barrier to building a first version, but the hard parts of software — architecture decisions, security, performance at scale, complex integrations, debugging systemic issues — still require human judgment. What's changing is the role: developers are becoming more like architects and reviewers than typists. If anything, there will be more demand for humans who can fix and stabilize what AI builds, which is exactly what we do.

Not scoping their sessions. AI tools don't resist scope creep — they comply enthusiastically. You ask for a login page and then say 'while we're here, add a dashboard, payments, and a notification system.' The AI happily writes all of it, but nobody's tracking the architectural decisions, and everything is interconnected in fragile ways. The best vibe coders treat each session like a sprint: one feature, one goal, review, commit, move on.

The numbers are staggering. GitHub reports over 50% of developers use AI coding assistants. Replit has millions of users building with AI, as does Cursor. Bolt.new's hackathon had over 130,000 participants. The AI coding tools market is growing rapidly. More importantly, a huge percentage of these users are non-developers building their first app — founders, designers, marketers, students. That's a lot of AI-generated code going into production without expert review.

Using LLMs directly (via chat) to write code has the same fundamental issues as using integrated tools, just with a different workflow. The AI still produces code that's confidently incomplete — missing error handling, insecure patterns, and no architectural coherence across files. The advantage of direct prompting is that you see every line, but the disadvantage is you're doing all the integration yourself. Either way, the code still needs human review before going to production.

Real-time features are notoriously difficult to implement correctly, and AI tools almost always get them wrong. Common issues include missing reconnection logic, no heartbeat/ping-pong implementation, improper connection cleanup causing memory leaks, and servers that hit connection limits. We fix your real-time implementation with proper connection management, error recovery, graceful fallbacks, and scalable WebSocket infrastructure.

This usually comes down to environment differences: missing environment variables, different Node.js versions, hardcoded URLs, file system dependencies that don't exist on serverless platforms, or cold start timeouts. API routes on serverless platforms like Vercel have strict execution time limits and no persistent file system. We audit your API routes for production compatibility and fix all environment-specific issues.

Correct — AI tools rarely generate SEO-optimized code unless explicitly instructed. Common gaps include missing meta tags, no sitemap.xml, no robots.txt, missing Open Graph tags for social sharing, no structured data (JSON-LD), poor heading hierarchy, no alt text on images, and client-side rendering that search engines can't crawl. We implement comprehensive SEO: proper metadata, server-side rendering where needed, structured data, sitemaps, and performance optimization that improves your search rankings.

Absolutely. Third-party integrations are where AI-generated code often has the most subtle bugs — missing webhook verification, incorrect error handling, no retry logic, and race conditions. We specialize in integrating payment processors (Stripe, PayPal), email services (SendGrid, Resend, Postmark), SMS (Twilio), analytics (Mixpanel, PostHog), and any other third-party API your app needs, with proper error handling and production-grade reliability.

AI tools set up email sending in the simplest way possible — usually direct SMTP or basic API calls — without configuring the infrastructure that keeps emails out of spam. You need SPF records, DKIM signing, DMARC policies, proper sender reputation setup, and correct email headers. We configure your email infrastructure properly and make sure your transactional emails (welcome emails, password resets, receipts) actually reach your users' inboxes.

AI tools typically load full-resolution images without optimization — no lazy loading, no proper formats (WebP/AVIF), no responsive image sizes, and no CDN caching. We implement Next.js Image optimization (or equivalent), lazy loading, proper image formats, responsive breakpoints, CDN configuration, and bundle optimization. Most clients see 50-70% improvement in load times after our performance pass.

This is increasingly common. AI-generated code that integrates with LLM APIs (OpenAI, Anthropic, etc.) rarely optimizes for cost — it sends full context windows every time, doesn't cache responses, doesn't implement streaming properly, and uses expensive models for tasks that cheaper models handle fine. We implement response caching, context window optimization, model routing (use cheaper models for simpler tasks), streaming for better UX, and proper rate limiting to protect your API spending.

File uploads in AI-generated code commonly fail because of incorrect multipart form handling, missing file size limits (allowing uploads that crash the server), no file type validation (security risk), uploading to the wrong directory on serverless platforms, and missing cloud storage integration. We implement secure, production-grade file uploads with proper validation, cloud storage (S3, GCS, Cloudflare R2), progress indicators, and error handling.