Services / Startup MVP Development
Startup MVP Development: AI-Accelerated with Security Built In
Ship production platforms using Cursor, Claude Code, and MCP workflows with security testing built in from day one. CodeWheel brings 15 years of production engineering to AI-accelerated development so you iterate faster without accumulating security debt.
Most teams build prototypes, then rewrite everything for production. We ship production-grade platforms on the first pass-Next.js with auth, billing, monitoring, and security testing-because AI handles boilerplate while we architect guardrails, test harnesses, and deployment pipelines.
Ready to build your MVP?
Ship your AI platform in weeks, not months
Share your MVP requirements in a 30-minute session. We’ll map AI-accelerated development workflows, security checkpoints, testing automation, and deployment architecture-so you know exactly what we can build rapidly and what requires careful architecture.
What is AI-accelerated development?
Practical explanation for founders
AI-accelerated development means using AI assistants (Cursor, Claude Code, MCP servers) to generate boilerplate, scaffold features, and accelerate implementation-while you focus on architecture, security, and business logic. It's not “let the AI write everything.” It's pairing deep production experience with LLM-powered acceleration to ship faster without cutting corners.
When AI-accelerated development works
- You're building a greenfield MVP on modern stacks (Next.js, TypeScript, Supabase).
- You need rapid iteration with investor demos every 2-4 weeks.
- Your team has production experience but wants 10x faster scaffolding.
- You're comfortable with automated testing and security reviews.
When to use traditional development
- You're working on legacy codebases (Rails 4, Drupal 7, Java EE).
- You need formal compliance audits without dedicated security review capacity.
- Your team lacks production engineering experience to review generated code.
- You're building life-critical systems (medical devices, flight control).
Use cases we build
Why production expertise matters
AI coding tools like Cursor make implementation accessible to everyone. But implementation isn't the hard part-architecture, security, and scale patterns are. Here's what we've learned shipping production platforms with AI-accelerated development.
AI tools democratize implementation, not expertise. LLMs generate clean code for single features, but they don’t architect auth boundaries, design multi-tenant data isolation, or prevent the subtle bugs that compound into security incidents months later.
Security gaps you won't see until audit
Cursor generates auth middleware that looks secure but leaks tenant data through missing RLS policies. You ship to 10 customers before discovering User A can query User B's records. Fixing this post-launch costs 10x more than architecting it correctly from day one.
Production patterns LLMs don't know
AI generates database queries without indexes, API calls without rate limiting, and background jobs without dead-letter queues. Your MVP works fine at 10 users. At 1,000 users, PostgreSQL melts down and you’re debugging prod at 2am because the LLM never learned about connection pooling.
Investor diligence failures
Series A investors hire security firms to audit your platform. They find hardcoded API keys, missing audit logs, and SQL injection vectors-all from AI-generated features that “worked” in demos. The round stalls for 8 weeks while you remediate findings that shouldn't exist.
Technical debt that compounds
Every AI-generated feature without tests creates debt. Six months in, you can't add new features without breaking existing ones. Regression bugs slip through because nobody architected a test harness. You spend more time fixing bugs than shipping features-exactly what rapid development was supposed to prevent.
Cost overruns from rework
Rebuilding auth, migrating to proper multi-tenant architecture, and adding observability post-launch costs multiples more than doing it right initially. Bringing in a senior engineer later means full-time compensation, onboarding, and lost time. Our 8-week engagement bakes in security so you keep velocity and own the IP.
Unreliable systems lose customers
Your AI-built platform crashes during a customer demo because the AI didn't implement circuit breakers for third-party APIs. You lose the deal. It happens again, and the “cheap” DIY approach turns into hundreds of thousands in lost pipeline and churned revenue.
The real choice
DIY AI development
- Learn production patterns by making mistakes
- Discover security gaps during investor diligence
- Rebuild features when architecture doesn't scale
- Spend time debugging instead of talking to customers
Best for: founders with 6-12 months to learn production engineering while building.
Expert-led AI development
- Ship with security architecture from day one
- Pass investor diligence without remediation cycles
- Automated testing prevents regressions
- Focus on customers while we handle infrastructure
Best for: founders who need to ship fast without cutting corners on security.
We compress 15 years of production mistakes into an 8-12 week engagement. You can absolutely learn this yourself-but the tuition costs more than hiring a team that's already made (and fixed) these mistakes.
How Security-First Development Works
Most teams rush MVPs with AI, then panic when security reviews find issues. We wire testing, observability, and security checkpoints into the development workflow-so audits pass on the first attempt.
Production-Grade Development Stack
We use AI assistants to accelerate implementation, but the stack underneath is battle-tested production infrastructure-not experimental frameworks.
- Cursor with Claude Opus 4.6 for full-stack implementation
- Claude Code for batch coding tasks and refactoring
- IntelliJ IDEA Ultimate + Codex for inline completion and test generation
- v0 for rapid UI component scaffolding (with manual review)
- Custom MCP servers for project-specific context
- Next.js 16+ with App Router and TypeScript strict mode
- Vercel Edge functions for low-latency APIs
- Supabase/Neon Postgres with pgvector for RAG
- Clerk for multi-tenant auth with SSO/SAML
- Stripe for billing with usage-based metering
- PostHog for analytics, feature flags, and session replay
- Semgrep for static analysis on every commit
- Playwright for E2E testing with visual regression
- Jest for unit tests generated alongside features
- OWASP ZAP for automated vulnerability scanning
- Custom prompt injection test suites for AI features
- Docker for sandboxed execution environments
- GitHub Actions CI/CD with security gate checks
- Vercel preview deployments for every PR
- Sentry for error tracking and performance monitoring
- Cloudflare for DDoS protection and WAF rules
- Database replication and automated backups
- Incident response runbooks and rollback procedures
What we're building
Recent MVP projects
Real platforms shipped with AI-assisted development and security testing built in. These show the approach: rapid iteration, production guardrails, and transparent deliverables.
- Next.js + Postgres + Redis architecture
- 2,000+ security and regression tests
- Rate limiting and auth boundaries from day one
- Preview environments for every pull request
- Live sitemap and keyword scanning
- Multi-LLM inference across providers
- Python API with Playwright coverage
- Automated deployment to Vercel
- AI-assisted code transformation
- Playwright regression tests for every route
- Incremental rollout to minimize risk
- Modern stack: Next.js, TypeScript, Vercel
Building in public: we focus on quality over quantity. These projects represent the approach-production-grade platforms with security testing from day one, shipped faster using AI-assisted workflows.
Engagement flow
- 1. Feasibility assessment. Review requirements, stack preferences, and security needs. Determine what to build with AI vs. manually architect.
- 2. Security scaffolding. Set up auth boundaries, data isolation, logging, and test harnesses before feature development.
- 3. AI-accelerated sprints. Use Cursor/Claude Code to implement features with automated testing and code review on every commit.
- 4. Hardening & launch. Penetration testing, load testing, documentation, and staged rollout to production.
What you get
- Production-ready Next.js platform with auth, billing, and AI features.
- 800+ automated tests (E2E, unit, integration) for regression safety.
- Penetration test report showing security posture before launch.
- PostHog dashboards tracking adoption, performance, and costs.
- Deployment pipeline with preview environments and rollback procedures.
- Handover documentation and runbooks for your team.
- Simple MVP (auth + core feature): 3-4 weeks including security review.
- Multi-tenant SaaS with AI: 6-8 weeks including RAG, billing, and pen testing.
- Platform migration: 8-12 weeks depending on codebase size and complexity.
- Internal tools: 2-4 weeks for single-tenant applications with SSO.
Timelines assume clear requirements and stakeholder availability for weekly reviews. Complex integrations or compliance requirements may extend schedules.
Security Architecture
The risk with AI-accelerated development isn't the AI-it's shipping generated code without verification. We add security checkpoints at every stage so fast iteration doesn't create vulnerabilities.
Want deeper security testing? Combine AI-accelerated development with our AI security consulting service or fractional AI architect engagement .
FAQ
Common questions
Honest answers to the questions founders ask before committing to AI-assisted development.
Ready to build your platform?
Ship production platforms 10x faster using AI-accelerated development with security testing built in. Book a feasibility call to map what we can build with AI-and what requires manual architecture.
Serving companies across the San Francisco Bay Area, Silicon Valley, and remote teams worldwide.