You built it quickly
- You built an MVP using Cursor, Claude, ChatGPT, Copilot, or another AI coding tool.
- Your prototype became a real product faster than expected.
- Your AI assistant keeps generating duplicate utilities or components.
Vibe Coding Cleanup
Your app works. Now make sure it can survive success.
AI-assisted development can turn an idea into working software faster than ever.
But fast-moving projects can also accumulate duplicated code, security gaps, fragile architecture, inconsistent patterns, hidden dependencies, performance issues, and technical debt.
Vibe Coding Cleanup helps you understand what you built, keep what works, and strengthen what needs attention.
Positioning
The goal is not to rewrite everything.
Getting software working quickly is valuable. The next challenge is making sure it is safe, understandable, maintainable, scalable, and ready for continued development.
Rapid development is useful. Production engineering still matters.
The goal is to determine:
- What is solid.
- What is fragile.
- What is risky.
- What is duplicated.
- What is difficult to maintain.
- What should be fixed now.
- What can wait.
Philosophy
Fast development isn't the problem.
Modern AI tools are excellent at accelerating implementation. They can generate interfaces, APIs, database queries, tests, integrations, configuration, and application logic at remarkable speed.
The challenge is that each generated solution may be locally reasonable while the overall system gradually becomes inconsistent.
Production engineering is the work of making sure all of those individual pieces form a coherent application.
Speed gets you to the first version.
Architecture helps you keep going.
Fit
This may be for you if...
Cursor, Claude Code, ChatGPT, Copilot, Replit, Lovable, Bolt, rapid prototypes, inherited codebases, outsourced development, and low-code tools can all get you working software quickly. This review is useful whenever that software starts to matter.
You inherited the code
- You inherited a codebase and do not know what is safe to change.
- Different parts of the codebase use different patterns.
- You keep fixing one problem and creating another.
It is becoming real
- You are preparing for production users.
- You need to add payments, authentication, sensitive data, or integrations.
- You are preparing to bring in additional developers.
Momentum is getting harder
- The application works, but you are not confident in the architecture.
- Deployments are becoming unpredictable.
- The app feels slower or harder to maintain as features are added.
- You need a senior technical review before investing further.
Assessment
What we review.
This is application-level review for architecture, quality, and production readiness. It is not a formal penetration test or security certification unless a separate engagement is explicitly contracted.
Architecture
How the application is structured, and whether the pieces still form a coherent system.
- Application structure
- Boundaries between components and services
- Dependency patterns
- Server and client responsibilities
- Unnecessary complexity
- Architectural consistency
Code Quality
Whether the code is understandable, reusable, and maintainable as the product grows.
- Duplicated logic
- Oversized components
- Dead code
- Inconsistent patterns
- Naming
- Missing or unnecessary abstraction
Security
Application-level review of trust boundaries and production readiness — not a formal penetration test or certification.
- Authentication flows
- Authorization
- Exposed secrets
- Client and server trust boundaries
- Unsafe input handling
- Insecure API usage
- Dependency and storage concerns
Database & Data Model
How data is structured, accessed, and protected as the application evolves.
- Schema design
- Relationships
- Indexes
- Query patterns
- Duplication
- Access controls
- Migration consistency
- Data integrity
APIs & Integrations
How the application talks to itself and to the rest of the world.
- Endpoint design
- Error handling
- Authentication
- Retries
- Webhooks
- Duplicated integrations
- External service handling
- API boundaries
Performance
Obvious bottlenecks and scalability concerns, not an unbounded optimization pass.
- Unnecessary rendering
- Expensive queries
- Repeated network calls
- Oversized payloads
- Caching opportunities
- Bundle and runtime issues
Dependencies
What the project actually needs versus what accumulated along the way.
- Unused packages
- Duplicated libraries
- Outdated dependencies
- Risky dependencies
- Inconsistent tooling
- Overlapping functionality
DevOps & Deployment
Whether the application can be configured, shipped, observed, and recovered with confidence.
- Environment configuration
- Secrets
- Build process
- CI/CD
- Deployment consistency
- Logs and monitoring
- Rollback considerations
Testing
Whether tests protect the workflows that actually matter.
- Critical flows
- Missing tests
- Brittle tests
- Test strategy
- Coverage of high-risk functionality
Documentation
Whether another person — or an AI coding tool — can understand the system without archaeology.
- Setup instructions
- Architecture notes
- Environment configuration
- Critical workflows
- Onboarding information
- Technical decisions
Patterns
Patterns we often look for in AI-assisted projects.
These problems are not unique to AI-generated software. Traditional teams create the same issues. AI can simply accelerate their accumulation.
duplicate_solutions
Duplicate Solutions
Three utilities doing almost the same thing because each feature was generated independently.
pattern_drift
Pattern Drift
One feature uses server actions, another uses API routes, another talks directly to the database.
component_explosion
Component Explosion
Many nearly identical components instead of a reusable design system or shared behavior.
hidden_security
Hidden Security Assumptions
The interface hides an action, but the backend does not actually enforce permission.
dependency_accumulation
Dependency Accumulation
A new package gets installed every time a generated solution needs a slightly different capability.
patch_on_patch
Patch-on-Patch Fixes
Repeated fixes solve symptoms without addressing the underlying architectural problem.
context_loss
Context Loss
The AI solves the current file correctly without understanding how the decision affects the rest of the application.
inconsistent_data
Inconsistent Data Access
Different features query, mutate, validate, and authorize data in different ways.
Process
How the cleanup works.
01
Understand
We learn what the application does, how it was built, where it is going, and what concerns you already have.
02
Inspect
We review the codebase, architecture, dependencies, data flow, security boundaries, deployment setup, and high-risk workflows.
03
Prioritize
Issues are separated by impact and urgency instead of treating every imperfection as equally important.
04
Improve
Depending on the engagement, we can provide recommendations, make targeted fixes, refactor larger areas, or help establish a cleaner foundation for future development.
05
Continue
You leave with a codebase that is easier for people — and AI coding tools — to understand and extend safely.
Priority
Not every imperfect line of code needs to be rewritten.
Findings are separated by impact and urgency so cleanup stays practical. Labels, not color alone, communicate what belongs where.
01
Fix Now
Address before production or continued exposure.
02
Fix Next
Likely to create security, reliability, or maintenance problems.
03
Improve Later
Worth addressing as the product evolves.
04
Leave Alone
Cleanup or optimization that can safely wait.
Direction
Cleaner. More predictable. Easier to understand.
The aim is not a perfect codebase. It is a lower-risk foundation that is better prepared for users, teammates, and continued development.
before
Working, but harder to trust
- Working application
- Rapid feature growth
- Mixed architectural patterns
- Duplicate logic
- Unknown security gaps
- Fragile deployment
- Harder AI prompts because context is inconsistent
- Fear of changing existing code
after
Still working — better prepared
- Clearer architecture
- Reusable patterns
- Documented critical flows
- Reduced duplication
- Better security boundaries
- More predictable deployments
- Cleaner context for AI-assisted development
- Greater confidence adding features
Context
AI accelerates development. Experience provides context.
AI coding tools are very good at generating solutions within the context they are given. The difficult engineering questions often exist outside that context.
Those questions benefit from broader architectural context.
- How should this feature fit into the rest of the system?
- Where should authorization actually happen?
- Is this abstraction useful or unnecessary?
- Should this package be added?
- Will this query still work with ten times the data?
- What happens when this integration fails?
- How will another developer understand this six months from now?
We don't turn AI off. We use it better.
AI-assisted development can remain part of the workflow. Once the application has clearer architecture, shared patterns, better documentation, and stronger boundaries, AI tools often become more effective because they are working inside a more consistent system.
The objective is not to return to slow development. It is to make fast development safer.
- Better project instructions
- Reusable patterns
- Shared components
- Architecture documentation
- Clear data-access conventions
- Defined authorization boundaries
- Consistent error handling
- Testing around critical workflows
Deliverables
A clear picture — not a 100-page report.
The goal is to give you a clear understanding of where the application stands and what deserves attention — not a long list of low-value observations. Assessment and cleanup are distinct: review can stand on its own, or continue into implementation.
Assessment
Codebase assessment
A clear picture of how the application is put together.
Architecture review
Where the system is coherent, and where it has drifted.
Prioritized findings
What matters now versus what can wait.
Security observations
Application-level risks and trust-boundary issues worth attention.
Technical debt analysis
Duplication, inconsistency, and maintenance pressure.
Dependency review
Unused, overlapping, outdated, or risky packages.
Performance observations
Obvious bottlenecks and scaling concerns.
Recommended remediation plan
A practical next step, not a 100-page report.
Cleanup / Implementation
Refactoring recommendations
Targeted improvements where they reduce real risk.
Production readiness considerations
Deployment, configuration, observability, and recovery.
Engagement options
These map to how Untapped already works — advise, improve, or stay involved as the product evolves. Scope is determined by the problem, not a packaged price list.
Review
Understand the condition of the application and identify the highest-priority issues.
Review + Plan
Assessment plus a prioritized remediation roadmap.
Review + Cleanup
Assessment followed by targeted implementation and refactoring.
Ongoing Technical Support
Continue improving architecture and development practices as the product evolves.
Timing
Good times for a technical cleanup
- Before production launch
- Before adding payments
- Before storing sensitive information
- Before hiring or onboarding developers
- Before a major feature expansion
- Before migrating infrastructure
- After a rapid MVP build
- After inheriting a project
- When deployments become unreliable
- When feature development starts slowing down
- When AI tools keep producing inconsistent implementations
You may not need a cleanup engagement if...
- The project is a disposable prototype with no intention of becoming production software.
- You are still validating whether anyone wants the product.
- The codebase is about to be intentionally replaced for a well-understood business reason.
- The application contains no meaningful data, users, integrations, or operational risk and remains purely experimental.
Not every prototype needs production engineering. The right time to invest is when the software starts becoming important to the business.
Snapshot
How healthy is your codebase?
A lightweight self-check. Answers stay in your browser. This is not a formal security assessment, automated scan, or certification.
Check the statements that are true today. The snapshot updates as you go.
Questions
FAQ
Do I need to stop using AI coding tools?
No. The goal is to create a cleaner foundation that makes AI-assisted development more reliable.
Will you rewrite my entire application?
Not unless there is a compelling reason. We prefer keeping working code and improving the areas that create real risk or maintenance problems.
Is this only for AI-generated code?
No. The same review is useful for inherited applications, rapidly developed MVPs, outsourced projects, legacy systems, and traditional codebases with accumulated technical debt.
Can you fix the problems you find?
Depending on the engagement, Untapped Technologies can provide the assessment, remediation plan, targeted fixes, refactoring, or continued technical support.
Is this a penetration test or formal security audit?
No, unless a separate formal security service is explicitly contracted. Security is reviewed as part of application architecture and production readiness, but this service should not be represented as a formal penetration test or certification.
Can you review an application before it launches?
Yes. Pre-production is often an ideal time to address important architectural, security, deployment, and maintenance concerns.
Playground
Explore related technical demos.
These are working examples, not a substitute for a codebase review.
Code Diff Viewer
Messy AI and legacy output versus production TypeScript — toggle the rewrite.
Launch Code Diff Viewer
Built fast? That's fine. Let's make sure it is built to keep going.
You do not need to know what is wrong with the codebase before reaching out. Tell us what you built, how you built it, and what concerns you have.
