Questions? We've got answers.

You built an app with AI and got to a demo fast. Then production happened. We audit the code, figure out what should be rescued, what should be rebuilt, and what should be removed, then ship the fastest path to a stable app.

It starts with a free code audit. We identify the real bottleneck, tell you whether the fastest path is a rescue sprint, a rebuild sprint, or both, and map the work into a fixed-scope plan. If the audit does not end with a clear recommendation, we do not pitch a paid engagement.

Every project is different, so we quote after the free code audit. Most work falls into a rescue sprint or a rebuild sprint depending on how risky the critical path is. Either way, you get a clear scope, fixed price, and timeline before any work starts. No hourly billing. No surprise invoices.

We work on apps built with Cursor, Bolt.new, v0, Lovable, Replit, Windsurf, and similar AI tools. On the stack side, we handle Next.js, React, Node.js, Python, Django, FastAPI, Postgres, Supabase, Firebase, and common deployment targets like Vercel, Railway, Fly.io, and AWS.

Absolutely. This service is built for founders and operators who do not want to become full-time debuggers. You do not need to understand the code. We explain the risks, options, and tradeoffs in plain English, then give you a clear plan.

Sometimes the right answer is not a rescue. If the fastest path is a rebuild, we will say that upfront, explain why, and scope it clearly. The goal is not to preserve bad code. The goal is to get you to a stable product.

You can try. The problem is that AI is good at patching the next issue, not understanding the whole system. That is why one fix often creates two more. At some point you need someone to trace the architecture, remove the risky parts, and make changes that hold up in production.

This is a common AI-built app problem. Code that works in development often fails in production because of hardcoded localhost URLs, missing environment variables, incorrect build settings, missing API keys, or CORS issues. We audit the deployment path end to end and fix the gaps between local and production behavior.

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 highest-risk parts of AI-generated code. Common failures include missing role checks, broken JWT or session handling, and login flows that work in the demo but fail with real users. We lock down the auth model, access rules, and session flow so it behaves predictably.

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.

Not by default. AI tools can generate working features without the security controls around them. Common issues include SQL injection, cross-site scripting (XSS), missing input validation, exposed API keys, insecure direct object references, and broken access control. Security is part of the audit, not an afterthought.

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 means every future change takes longer, costs more, and is more likely to break something else. AI-generated code often duplicates logic, mixes patterns, and leaves behind dead branches. We clean up the structure so the next change is easier than the last one.

Absolutely. But the first step is making sure the foundation can support them. Often the real blocker is not the missing feature. It is the brittle code underneath it. Once that is stabilized, new work becomes much safer and faster.

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. If moving to a different database is part of the fastest path, we can scope the migration, update the data model, move the data safely, and cut over carefully. The goal is to reduce risk while getting you onto a setup that fits the product better.

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 a strong fit for this service. Cursor is powerful, but long sessions can drift architecturally over time. Common issues include duplicated logic across files, inconsistent state management, broken imports after refactoring, and environment configuration issues. We audit the codebase and decide whether those parts should be repaired or rebuilt.

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. The same failure modes show up across AI coding tools: brittle architecture, missing guardrails, shallow error handling, and scaling problems. We focus on the code and the deployment path, not the brand name of 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 fit into a 1 to 3 week window. Rescue sprints tend to be shorter. Rebuild sprints usually take longer because they replace brittle core flows instead of patching them. We give you the timeline before work starts, not halfway through.

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 more stable product, a clearer codebase, documented changes, deployment guidance, and a handoff that makes future work easier. The exact deliverables depend on whether the scope was a rescue sprint or a rebuild sprint, but the goal is always the same: fewer surprises and a safer release path.

Yes. Ongoing support is available after the initial rescue or rebuild if you want help with hotfixes, releases, or new feature work. We can scope that separately based on how hands-on you want us to be.

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 the codebase is already more stable after the initial engagement, future work is typically faster and more affordable than the first rescue or rebuild.

Yes. If you're heading into diligence, we can focus the work on code structure, security, documentation, and deployment hygiene so the product is easier to review and easier for a future team to inherit.

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 can sign an NDA before reviewing the codebase. Confidentiality and access boundaries can be set before any audit starts.

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, the offer is built around AI-generated app rescue and rebuilds, not generic ticket work. Second, you get a clear scope, fixed price, and handoff instead of open-ended hourly drift. Third, we can handle the full path from audit to production hardening rather than just one isolated bug.

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.

Many dev agencies default to large rebuilds or broad retainers. We start with an audit and recommend the fastest path: rescue what works, rebuild what doesn't, and skip the rest. The focus is not selling the biggest project. It's getting you to a stable product.

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 current data points in that direction: more churn, more duplication, and more security problems. But the practical version matters more than the headline. AI-generated code often looks correct before anyone tests the edge cases, the auth rules, or the deployment path.

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, weak error handling, no retry logic, and race conditions. We can wire up payment, email, SMS, analytics, and other external APIs with the guardrails production systems need.

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 optimize the image pipeline, delivery, and caching so the app feels faster on real devices and real connections.

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.