AI Code Rescue
Built your app with AI?
Make it production ready.
Claude, Codex, Cursor, Lovable, Bolt or Replit generated your application. It works, but is it ready for real users, real data and real money? We audit it, repair the weak points and put production controls around it.
The work
AI Code Rescue
More people can now build software than can safely maintain it in production. Whether the code came from an AI app builder, a coding agent or a mixed team, the first task is the same: understand its behavior and the risks around it.
Audit
Map architecture, dependencies, authorization, data handling, integrations and critical business flows. Rank findings by impact and urgency.
Repair
Address root causes in small, reviewable changes. Remove duplicated logic, correct broken integrations and replace unsuitable dependencies.
Stabilize
Add regression tests, reproduce known failures, inspect performance and document the behavior that must remain intact.
Prepare for production
Check environment configuration, secrets handling, observability, backups and rollback. Record remaining risks and a release recommendation.
What you receive
A prioritized findings register with reproduction evidence, an AI Code Health Score across the 13 areas below, a repair plan, reviewed fixes, regression checks and a production-readiness assessment. An audit reduces uncertainty; it is not a guarantee that no defect remains.
What HUB checks
13 areas between a demo
and production
The audit scores each area from evidence in your repository and infrastructure, so you can see progress after every repair. Want a first impression now? Take the free self-check.
Architecture
Structure, duplicated logic and whether the design fits the business rules.
Security
Secrets, input handling, exposed endpoints and known vulnerability patterns.
Authentication
Server-side authorization for every protected page and API action.
Database
Schema, migrations, query safety, indexes and data integrity.
Performance
Behavior with production-sized data and realistic traffic.
SEO
Rendering, metadata and crawlability of public pages.
API usage
Timeouts, retries, error handling and spending limits for third-party and AI APIs.
Dependencies
Versions, maintenance status, known vulnerabilities and licences.
Tests
Coverage of the flows that earn money or handle personal data.
Scalability
The first bottleneck if usage grows, and how to remove it.
Deployment
A repeatable release process with a rehearsed rollback.
Backups
Separate, automatic backups and a tested restore.
Logging
Error tracking, useful logs and alerts that reach a person.
Generated vs engineered
Generated code is a draft
until someone can operate it
Agents produce working-looking software in minutes. Closing the distance between that and a system your business can depend on is engineering work, and it does not get smaller because the first draft arrived quickly.
What we keep finding
- Logic duplicated across files instead of shared
- Authorization checked in the UI but not on the server
- Secrets and keys committed into the repository
- Dependencies chosen by popularity, not by fit or licence
- Queries that pass in a demo and time out on real data
- No tests, so no way to tell a fix from a regression
- No deployment path, backups or way back to yesterday
What we put around it
- Architecture reviewed against the business logic it serves
- Permissions and data boundaries verified on the server
- Secrets, configuration and environments separated properly
- Dependency and licence review before anything is adopted
- Performance checked against production-shaped data
- Regression tests around the behavior that must not change
- Deployment, monitoring, backups and a tested rollback
How we deliver
What happens between
the brief and the release
AI accelerates implementation. Our engineers control context, architecture, acceptance and the release.
- 01 /
Understand
Business goals, codebase, constraints.
- 02 /
Design
Architecture, scope, acceptance criteria.
- 03 /
Build with agents
Bounded tasks, context and version control.
- 04 /
Verify
Automated tests and human review.
- 05 /
Operate
Deploy, monitor and plan for recovery.
A practical starting point
Start with the
actual problem.
Share a short description of the application, the symptoms and its current hosting setup. We agree secure repository access after scoping; do not send credentials in an initial email.
Which AI tools’ code do you work with?
Any. Output from app builders such as Lovable, Bolt and Replit, from coding agents such as Claude Code, Codex and Cursor, or from a mixed team. The review focuses on behavior and risk, not on how the code was produced.
Do we need to rebuild everything?
Not necessarily. We assess what can be kept, what needs repair and what is more economical to replace. A rewrite needs evidence, not assumptions about how the code was created.
Can you review a live application?
Yes. We agree safe access and reproduce issues in an appropriate non-production environment where possible. Disruptive testing requires a separate agreed scope.
Is the audit free?
Audit scope and pricing are agreed after an initial conversation. The free self-check gives a first impression from your own answers; the audit verifies each area in the code.
Explore our capabilities
AI code health self-check ↗ Before you ship AI-generated code ↗ All engineering services ↗What does your software
need to do next?
A new build, a difficult codebase or a system that needs support. Let’s talk.