Fordel StudiosThe prototype-to-production gap — bridged.
Cursor + Claude building production apps — the one-person startup narrative is real. What it glosses over: the prototype-to-production gap is where most vibe-coded products stall. Security debt accumulates fast when AI generates code that looks right but skips authentication hardening, input validation, and secrets management. We audit, harden, and ship.
The one-person startup narrative is accurate: a founder with Cursor and Claude can build a working full-stack application in days that would have taken months before. This is real and it has changed what early-stage product development looks like. What the narrative glosses over is the prototype-to-production gap: what AI code generation reliably produces and what production deployment reliably requires are different things, and the difference is not small.
The security debt in vibe-coded prototypes is consistent across tools and languages. API keys committed directly to source code. JWT tokens that never expire. No rate limiting on authentication endpoints. CORS configured to allow all origins. SQL queries constructed with string concatenation. Passwords stored without proper hashing. These are not edge cases — they are what AI code generation produces for features that are not visible in happy-path flows. A prototype with these issues is fine for a demo. Onboarding real users to it creates real exposure.
- Authentication and authorization with proper session management and token lifecycle
- Input validation and sanitization — SQL/command injection vulnerabilities are common
- Secrets management — API keys and credentials in code or unprotected .env files
- Rate limiting on authentication and sensitive endpoints
- Error handling beyond the happy path — unhandled exceptions expose stack traces to users
- Database connection pooling and query parameterization
- CI/CD pipeline, staging environment, and deployment automation
- Observability — Sentry for error tracking, basic uptime monitoring
We audit the prototype codebase first. The goal is to understand what exists, what works, what needs hardening versus what needs replacing. The business logic embedded in a working prototype is often correct — it represents validated product thinking. We preserve what works and harden what does not, rather than rewriting for the sake of rewriting.
Security hardening is priority one — before any users are onboarded, regardless of how long other hardening work takes. Secrets rotation, authentication implementation, input validation, injection vulnerability fixes. We produce a security findings list with severity ratings and implement critical and high findings before proceeding. The deployment infrastructure and observability work follows.
Prototype to MVP process
Review the prototype: what works, what is architecturally sound, what needs hardening vs. replacement. Security scan for committed secrets, dependency vulnerabilities, and common injection patterns. Produce a findings list with effort estimates.
Rotate committed secrets. Implement proper authentication (session management, JWT lifecycle, refresh tokens). Add input validation and parameterized queries. Fix CORS configuration. Add rate limiting to auth and sensitive endpoints. Implement proper password hashing if applicable.
Add error boundaries throughout. Handle failure modes gracefully. Replace stack trace exposures with user-appropriate error messages. Add retry logic for external service calls with proper backoff.
Production hosting on Vercel, Railway, Fly.io, or AWS based on requirements. CI/CD pipeline with staging environment. Database with connection pooling. Basic monitoring.
Sentry for error tracking and performance monitoring. Basic uptime monitoring. Alert routing to a channel your team actually watches. Enough visibility to know when users are hitting errors — not a full observability platform.
Preserve-first codebase audit
We audit before rewriting. Business logic embedded in a working prototype is often correct. We preserve what works and harden what does not — rather than imposing an architecture or rewriting for engineering aesthetics. The audit determines which is which.
Security hardening for AI-generated code
Security review and remediation before any users are onboarded. We find and fix the issues AI code generation reliably introduces: committed secrets, injection vulnerabilities, insecure authentication patterns, missing rate limiting, and open CORS configurations.
Authentication and authorization
Proper session management, JWT token lifecycle with expiry and refresh, OAuth2 integration where needed, and authorization checks enforced server-side. Authentication is the most common and highest-risk gap in vibe-coded prototypes.
Deployment pipeline and hosting
We select hosting appropriate for an MVP: cost-effective, scalable for early user load, operationally simple enough for a small team. Vercel for Next.js, Railway or Fly.io for backend services, Supabase or PlanetScale for the database. CI/CD with staging environment and one-command rollback.
Sentry observability for early-stage products
Sentry error tracking, performance monitoring, and alert routing to a channel your team actually watches. Early-stage products need visibility into what is breaking — not a complex observability platform, but enough to know when users are hitting errors before users tell you.
- Codebase audit report with security findings and production readiness assessment
- Security hardening: secrets rotation, authentication implementation, input validation, rate limiting
- Error handling and resilience improvements throughout the application
- Production deployment on appropriate hosting with CI/CD pipeline and staging environment
- Sentry error tracking and basic uptime monitoring setup
- Deployment runbook for ongoing operations
The prototype-to-production gap typically requires 4-8 weeks of focused engineering on the concerns AI code generation omits. Without this work, onboarding users to prototype code creates security exposure and reliability failures that damage early customer relationships and require emergency response.
Common questions about this service.
Harden the prototype or rebuild from scratch?
Harden when: the prototype architecture is fundamentally sound (correct data model, reasonable API structure, working core logic) and the missing concerns are additive — security, monitoring, deployment — rather than structural. Rebuild when: the data model is wrong, the API is not designed for actual usage patterns, or the prototype was built in a framework inappropriate for the production use case. The audit tells us which situation applies.
What frameworks do you work with for this?
We meet the prototype where it is. Most Cursor and Claude-generated prototypes use Next.js, React, Python FastAPI, or Node.js/Express — the frameworks AI tools generate fluently. We work with whatever was generated rather than imposing a technology preference. If the framework genuinely cannot support the production requirements, we surface that in the audit.
How do you handle AI-generated code that uses deprecated patterns?
We flag them in the audit and fix the security-relevant ones before deployment. AI code generation tools have knowledge cutoffs and sometimes generate patterns that are outdated or deprecated. Dependencies are scanned for known vulnerabilities (npm audit, pip-audit) and updated. Deprecated API usage is flagged and updated within the engagement scope.
What hosting platform should we use for an MVP?
For most Next.js or Node.js MVPs: Vercel for the frontend and API routes, Railway or Supabase for the database. For Python backends: Railway, Fly.io, or a small cloud instance. For products expecting significant early traction: a simple Kubernetes setup scales better than trying to migrate under load. We recommend based on expected traffic patterns, team operational capacity, and cost constraints.
Can you help after launch?
Yes. We offer ongoing retainer-based engineering support post-launch. Early-stage products iterate fast and need engineering capacity that scales with discovery velocity. Retainer engagement covers feature development, bug fixes, and infrastructure scaling as user load grows.
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Tell us what you are building. We will scope it, price it honestly, and give you a clear plan.
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