Prototype to Production & Vibe Code Rescue Services

The fastest path to production often introduces the highest operational risk. Our AI codebase audit finds hidden risks such as hardcoded secrets, architecture drift, and performance bottlenecks.

Our audit focuses on the areas most likely to block production:

• Architecture Drift Mapping. Restore a consistent system architecture. We build a map of architectural deviations, identifying duplicated logic and inconsistent layers that emerged from chaotic prompt iterations.
• AI Capability Boundary Mapping. We identify which parts can stay AI-assisted, which need human review, and which need a rewrite because the current logic cannot be verified for production.
• Dependency and Supply Chain Audit. We review dependencies added by AI agents, check for CVE risk, and recommend safer alternatives.

Is cleanup turning into an endless sprint? A Vibe Coding Rescue Sprint stabilizes AI-generated code by fixing transactions, state management, and error handling.

This sprint focuses on the production issues most likely to break the product:

• Backend Stabilization Pass. Prevent production failures from going undetected. We rewrite critical backend code that AI agents created without proper error handling, transactions, or validation.
• State and Data Flow Normalization. We clean up chaotic application state caused by random prompt changes and restore predictable behavior.
• Error Handling Framework. We add centralized error handling so hidden failures do not make it into production.
• API contract re-alignment. We rebuild API contracts broken by uncontrolled AI changes and keep frontend and backend behavior in sync.

Moving from an AI-built MVP to a production system requires significant engineering work. Our production-ready migration adds the controls, observability, transaction safety, and secure authentication your product needs before launch.

The migration covers the controls your MVP needs before launch:

• Environment Separation Setup. We create full dev/stage/prod environments that AI platforms do not generate and set up secure deployment pipelines.
• Transaction-Safe Backend Rewrite. We rewrite critical business operations using transactions and idempotency to avoid race conditions often created by AI code.
• Product Invariant Reconstruction. We define and enforce the rules AI agents usually leave implicit: user identity, account ownership, billing logic, role boundaries, data access, and failure behavior.
• Production Observability Layer. We add the monitoring and alerts your team needs to understand how the system behaves in production.
• Secure Auth and Session Rebuild. We restore authentication and sessions that AI agents build insecurely, adding key protections.
• Production-Grade Error Budgeting. We define error budgets so your team can track product stability after migration.

New AI features should not put your existing business operations at risk. We help AI-built products integrate safely with ERP and CRM systems without disrupting existing workflows.

We stabilize legacy integrations by focusing on the systems that carry real business risk:

• Legacy API Wrapping. We create adapters that allow AI-generated code to work with legacy ERP/CRM systems without the risk of breaking existing processes.
• Zero-Downtime Migration Planning. We design a phased transition so modernization doesn’t stop production or financial transactions.
• Data Model Harmonization. We align legacy data schemas with new AI modules, avoiding type conflicts and duplication.
• Legacy Security Hardening. We close vulnerabilities in old systems that AI code might accidentally exploit through incorrect integrations.
• Event-Driven Refactor. We move core systems toward event driven architecture so AI modules can run asynchronously and scale without blocking critical workflows.

Stop spending senior engineering time on cleanup that should be automated and reviewed. Our AI-augmented refactoring removes dead code, adds tests, and standardizes interfaces under engineering review.

The refactoring process follows a clear engineering workflow:

• Agent-Supervised Code Rewrite. We use AI agents under engineering supervision to speed up refactoring without losing architectural integrity.
• Automated Test Generation. We generate unit tests and integration scenarios covering critical paths to stabilize code before changes.
• Interface Contract Normalization. We unify interfaces between modules that AI agents created with different styles and patterns.
• Performance-First Refactor. We use performance analysis to optimize hot paths that LLM agents generated inefficiently.

The biggest security risk in AI-generated code is weak protection for sensitive data and secrets. We secure the delivery process by isolating secrets, rebuilding access controls, and adding SAST and DAST checks to CI/CD.

The DevSecOps framework we implement involves the following critical services:

• Auth and RBAC Reconstruction. We rebuild authorization with clear roles and access policies, replacing insecure AI-generated solutions.
• CI/CD Security Gates. We add static and dynamic security checks to pipelines to block dangerous changes before deployment.
• API Abuse Protection. We add rate limiting and anomaly detection that many AI-built MVPs skip.
• Compliance-Aligned Logging. We configure audit logs that meet the requirements of modern compliance standards.

AI compliance requirements are moving fast across regulated industries. We build compliance ready AI architecture with explainability, risk testing, bias testing, and automated audit trails.

This readiness is achieved by implementing the following:

• Explainability Layer (XAI). We add decision explanation modules to models to comply with necessary regulations.
• Risk and Bias Testing. We test for biased outcomes and document the risks your team needs to address.
• Data Governance Framework. We implement data retention, access, and deletion policies in accordance with modern privacy frameworks.
• Automated Audit Trails. We create a full log of model and user actions for regulatory audits.
• Model Deployment Compliance Checks. We verify models for compliance with state requirements before production.

Unplanned cloud spend is one of the hidden costs of uncontrolled AI deployment. Our cloud re-architecture work gives you a clear migration plan, a scalable runtime, and infrastructure that grows predictably.

The re-architecture process is structured around these core actions:

• Cloud Migration Blueprint. We design the migration path from AI platforms to the cloud, considering infrastructure constraints.
• Serverless/Container Rebuild. We rewrite the backend into containerized or serverless architecture for scalability.
• Autoscaling Policies. We configure horizontal scaling based on real-world load metrics.
• Observability Stack Deployment. We install a comprehensive monitoring stack for full system control.
• Cost-Efficiency Optimization. We optimize costs by eliminating inefficient AI-generated resources.

Your AI development process needs clear controls. The AI Delivery Harness turns vague prompts into clear specs and requires AI-generated code to pass security, logic, and workflow checks before deployment.

The delivery harness enforces control and structure through the following:

• Agent Orchestration Framework. We build a system where agents work with clear roles rather than chaotically as in vibe coding.
• Spec-Driven Development. We turn product rules into agent-readable specifications: formal product specifications that prevent AI tools from reshaping the architecture with every prompt.
• Guardrail Enforcement. We implement restrictions that prevent agents from changing critical modules without review.
• Verifiability Pipelines. We build testable environments where AI-generated changes must prove they preserve production integrity and core business logic before deployment.
• Multi-Agent Workflow Automation. We orchestrate interaction between multiple agents so they perform complex processes without conflicts.