AI Prototype to Production

You built it with AI. We make it ready for production.

Audit, secure, deploy, and operate the products you’ve built with AI tools so they hold up when real users, real data, and real traffic arrive.

The risks of taking AI-built code to production

Building has never been faster. Getting a prototype to behave like a real product is where the work starts.

The patterns we see most often are not bugs in the traditional sense. They are decisions that made sense for moving fast, and that turn into liabilities the moment real users, real data, and real traffic show up.

Architectural shortcuts that do not scale: Single files holding thousands of lines. Business logic mixed with rendering. Components that work in isolation but collapse the moment you need to extend them. The product runs, but the next feature breaks the last one.
LLMs used where they should not be: Using an AI model to perform deterministic logic, scoring, calculations, structured outputs, is one of the most common patterns we see, and one of the most fragile. The fix is rarely a better prompt. It is moving the logic where it belongs: the server.

Sample Audit

The engagement follows the same path regardless of the type of pentest. Five phases, each with a defined deliverable.

What you receive Security Audit

Complete

Production-readiness audit

A representative slice of findings from a full-scope review — surfaced, classified by severity, and traced to root cause.

Critical Critical 5shown

C-01API keys and live credentials committed to source control

C-02Database backups committed to source control

C-03LLM used as a deterministic computation engine

C-04No CI/CD pipeline; deployment coupled to a single hosting provider

C-05No test suite or ground-truth examples

High-risk High 5shown

H-01Platform-specific dependencies creating vendor lock-in

H-02No README or developer-facing setup documentation

H-03Dual-source-of-truth state management causing silent sync failures

H-04Single 5,000-line routes file mixing concerns

H-051,600-line components mixing rendering, filtering, and business rules

Architectural concerns Medium 4shown

A-01Compensatory regex patches without addressing root causes

A-02Magic strings scattered across the codebase

A-03Duplicate or triplicate pages indicating dead code

A-04Save confirmation toasts firing before operations complete

73 verifications · 5 critical 8 high 12 medium

How we audit

Every audit combines human expertise with AI-assisted analysis, validated by senior engineers.

Six layers:

Automated security and quality scanning. We run tools like SonarQube and Snyk to detect security vulnerabilities, code quality issues, and risky dependencies.
Manual code review. A senior engineer reads and analyzes the codebase, covering architecture, security, data handling, and code quality.
AI-assisted analysis. We run Claude Code alongside custom prompts to assess security, observability, code quality, operability, and architectural soundness. Every AI finding is validated by the human auditor before it enters the report.

What we do

Two services. One goal: a product that is ready for the real world.

You start with the audit, with no commitment beyond that. If you want, we take it to production and keep it running. The path is yours to choose.

Diagnosis Start here

Diagnosis · Audit

01 Audit

We review your solution against a concrete checklist and deliver a clear report: what is working, what is risky, and what to fix first.

Security and data handling

Secrets, keys, and permissions exposure

AI-specific risks, including prompt injection

Supply chain and dependency analysis

Scalability assessment

Prioritized action plan

Take it to production

Production Optional · next

Production · Deploy & Operation

02 Deploy & Operation

We keep what works from your prototype and rebuild what does not. The good news: what you already built is the perfect specification. Our engineers, working with coding agents and secure development practices, can rebuild the rest faster than you would expect.

We salvage the components and flows that are solid

We rebuild what is fragile, applying secure-by-design standards

We deploy to real, secure, monitored infrastructure

We hand off a product ready to grow, and can keep operating it with you

A real case

A fintech assessment platform came to us with a working product built end-to-end on a vibe-coding tool. The interface looked clean, the demos worked, and the founder had paying clients lined up.

The audit told a different story.

Live API keys were committed to the source repository. Database backups sat in version control. The platform had no CI/CD pipeline and was fully locked into a single hosting provider.

The core issue, though, was deeper: the team had been using an LLM as a deterministic calculation engine. The entire scoring logic of the product was running on non-deterministic AI output, with regex patches stacked on top to compensate for the inconsistencies.

We delivered a clear verdict: the frontend was salvageable as a visual foundation, and the backend needed a full rewrite. Honest, specific, with a concrete path forward.

That report is what the audit produces.

FAQ's

Does it work if I built it with Claude, Lovable, Cursor, or any AI agent?

Yes. We work with any stack that came out of your prototype, Lovable, Bolt, Cursor, v0, Replit, Claude artifacts, or anything else. The framework matters less than the production readiness of what is underneath.

What exactly does the audit cover?

A complete plan for how to take your product to production: secure, stable, and ready to scale. The report covers what is working, what needs to be rebuilt, and the priority order to get there.

Will you guarantee it is 100% secure?

We follow secure development practices end to end, including threat modeling sessions, secure-by-design architecture, and senior-level code review. Security is treated as an engineering standard, not a deliverable, which is why we recommend operation as part of the service.