Built with AI.
Taken to the next level.
AI tools ship working apps fast. But getting from prototype to a stable, secure, production-ready product is a different challenge — one that requires real engineering judgment, not just generated code.
Something real has been built. Time to make it production-grade.
Founders are shipping working apps with AI tools — Claude, Copilot, Cursor, Codex. The product is real. Users are signing up. But the gap between a working prototype and a stable, scalable production app is wider than it looks.
Not a rescue operation for broken code — a production path for AI-built apps that are already working and ready for the next stage of growth.
"The app works and there are real users — but no clear path to proper deployment, scaling, or production stability without a dedicated engineering team."
Acting as a senior engineering partner — taking a working AI-built app and handling everything needed to scale: production infrastructure, deployment pipelines, code quality improvements, and long-term technical support.
The work that
actually gets done.
A solid foundation, built to last.
AI tools write code — cloud infrastructure is a different discipline entirely. Proper setup on AWS, GCP, or Azure means no scrambling later when traffic grows or something breaks in production.
Deployments that are boring on purpose.
Pipelines, monitoring, and automated processes that make shipping predictable — not stressful. The kind of setup that catches problems before they hit production, not after.
Book a Free Call →Everything an AI-built app needs to grow.
From deployment to scaling — senior engineers handle the technical heavy lifting, keeping the product team focused on what matters.
Production Deployment
Proper deployment for working apps — cloud infrastructure, environment configuration, and go-live support on AWS, GCP, or Azure.
CI/CD Pipelines
Automated build, test, and deployment pipelines so every code change ships reliably — no more manual deploys or broken releases.
Architecture & Scaling
Architecture assessed and strengthened for growth — database design, API structure, caching, and service boundaries built to scale.
Monitoring & Observability
Error tracking, logging, uptime monitoring, and alerting — issues surface immediately, before they reach end users.
Code Quality Improvement
Senior engineers review and improve AI-generated code — refactoring for maintainability, removing dead code, and setting quality standards that hold long-term.
Ongoing Engineering Support
A senior engineering partner on an ongoing basis — reviewing, advising, and building alongside the product team as it grows.
From first call
to production-ready.
Four clear steps — starting with a discovery call, moving fast to reach production-ready.
Book a Free Call →How production-ready is an AI-built app?
Check every item that applies. All of these are covered in every engagement.
Which of these apply to the current codebase?
Be honest — this is exactly what every engagement is designed to address.
Security & Secrets
AI Development Process
Code & Architecture
Never too late — this is exactly when an assessment matters most. Most apps reviewed are already live. The sooner the gaps are identified, the faster they can be addressed before a real incident.
Infrastructure, deployment, CI/CD, architecture, code quality, and long-term production readiness. Every engagement starts with a clear plan — prioritized actions, timelines, and ownership.
Codebases are treated as confidential. Access is handled with care, and nothing is shared or modified without clear agreement on how the engagement is structured.
Usually fast. After an initial call to understand the app and the goals, a plan gets put together quickly. Most engagements are scoped and moving within a week or two of first contact.