Vibe Coding Rescue

AI-assisted code reads well and passes a casual review. The gap between apparent quality and production behavior stays invisible until load, an edge case, or an audit exposes it.

• Source-grounded system baseline. We trace dependencies and uncover hidden business logic directly from your codebase using the AI Solution Accelerator™, then an experienced engineer reviews the findings and fills the gaps.
• Module-level risk and security scoring. We score every module for risk and security exposure before recommending any change. Veracode reports that 45% of AI-generated code samples introduce OWASP Top 10 vulnerabilities.
• Technical debt and architecture review. We document architectural weak points, undocumented business logic, and dependencies that make change risky, with an effort estimate for each area.
• Security surface mapping. We map access boundaries, exposed credentials, and the paths where authorization checks are absent or applied inconsistently.
• Committable remediation estimate. We deliver an estimate we are prepared to staff and deliver against, with the assumptions that could change it listed separately.

We provide a clear, documented picture of your system’s reality through a detailed vibe coding audit, complete with a prioritized remediation plan and an estimate you can confidently pitch to your board or investors.

The answers already exist, scattered across tickets, wikis, and old test cases. Reassembling them by hand consumes weeks of senior engineering time.

• Environment-resident deployment. We build the platform within your environment, connecting source code and your comprehensive project artifacts.
• Living documentation. The knowledge base regenerates from the source on every commit, which keeps it accurate as remediation changes the underlying system.
• Dependency and impact map. Engineers see what each proposed change touches before they make it, which matters most in code where coupling was never designed.
• Developer search with source references. Your team gains direct insights linked back to the exact code that produces the behavior.
• Human-validated knowledge. Senior engineers decide what represents the product today, so outdated behavior recovered from old tickets stays out of the record.

Remediation carries its own risk during a vibe coding cleanup, because the parts of a system most in need of repair are frequently the parts where a mistake costs users real money.

• Structural protection of sensitive logic. We define, by design, which paths AI is barred from touching, covering sensitive system workflows.
• Scoped access per task. AI tools receive only the access required for a given task, with human approval mandatory for any change to a protected workflow.
• Specs and tests before code. A senior engineer agrees on the requirement, AI generates the tests that prove it, humans review those tests, and code is validated against them.
• Traceable authorship. Every AI-assisted change is tagged with the model used and the engineer who reviewed it, so a post-incident review has an owner for each decision.
• Build gating. Failed builds are returned to the team and remain out of production until they pass.

Test suites generated alongside the code assert whatever that code currently does. They pass even when the logic is wrong, and they lock defects in place the moment refactoring begins.

• Invariant definition and stress testing. We define the rules your system must never break, then stress-test the code against them at scale rather than walking a checklist.
• Side-by-side behavioral comparison. We run new logic against the previous version on identical inputs to catch silent behavior changes before release.
• Requirements-driven test reconstruction. We rebuild coverage from your Jira tickets and codebase to target the gaps a manual QA cycle misses.
• Release impact analysis. We show what each change affects before a single test runs, which keeps regression scope proportional to risk.
• Coverage visibility. Your team sees tested and untested areas across the whole system before any release goes out.

Vibe-coded systems usually reach production via an undocumented path, leaving security teams without an AI-generated code audit or evidence of what was reviewed and when.

• Pipeline configured to your stack. We shape CI/CD around your technology and approval workflow, running automated tests and security review on every slice.
• Automated quality gates. Every commit is validated in the pipeline as part of delivery, and unreviewed code is blocked before production.
• Configuration drift monitoring. We watch for divergence between staging and production, so the version that passed review is the version that ships.
• Rollback at every step. Each rollout starts small, with a rollback plan in place.
• Audit trail with named approval. Your security and compliance teams see what was reviewed, who signed off, and when it shipped.

This results in a release pipeline that automatically generates audit-ready evidence—exactly what your auditors and enterprise clients ask for.

Remediation without a change in the operating model yields the same findings within two quarters, because the delivery pressure and the tooling stay exactly as they were.

• Architecture and boundaries set upfront. Senior engineers define the structure AI operates within before any generation occurs.
• AI-assisted implementation under review. Features land faster while senior engineers own integration logic and edge cases.
• AI-assisted code review with human sign-off. Security exposure and long-term technical debt stay a senior engineering decision.
• Living documentation maintained from source. Documentation updates on every commit and stays connected to tickets and test cases.