You shipped something real with Lovable, Bolt, Replit, v0, or Claude, and the demo works. Now you need it to handle real users, real data, and real money without falling over. That last stretch, from working prototype to production-grade product, is exactly what I do. No judgment on how it was built; getting to a demo is genuinely hard, and production is just a different sport.
I'm Sheshan Bandara, a senior full-stack engineer. I take AI-built MVPs and make them safe, stable, and scalable: fixing bugs, closing the security holes that AI code tends to leave open, repairing fragile architecture and slow database queries, adding tests and observability, and moving the app onto infrastructure you actually control. Usually that means hardening what you have, not an expensive rebuild from scratch.
Roughly one in five AI-built apps ships with a serious vulnerability. I start with auth, access control, secrets, and row-level security, so your first thousand users aren't your first breach.
Most vibe-coded apps crack somewhere around real traffic: unindexed queries, fragile architecture, no error handling. I fix the structure underneath so growth is a good day, not an outage.
Your momentum is an asset. Where the code is fine, it stays; where it's risky, I refactor it, document it, and hand back something you or your team can actually understand and extend.
EdTech · Microservices · Video
The existing lecture video platform had become difficult to scale and maintain. Playback was inconsistent, analytics and logs became unreliable under load, and lecturers sometimes waited overnight before uploads were ready to stream.
FinTech · Biometric · Realtime
Community finance groups needed a more reliable way to manage members, loans, payments, and identity checks. Paper ledgers made balances difficult to verify, while member identity at the counter depended on manual recognition.
SaaS · Multi-tenant · Bilingual
A multi-branch institution was managing finance, alms schedules, donor pledges, and branch records across paper logbooks. Visitors also relied on printed signs or staff explanations to find information on site.
Yes. That's the core of this work: taking an app built on an AI platform and getting it onto production infrastructure you own, with the bugs, security, and architecture sorted out along the way.
Usually not. Most AI-built apps have a solid core and a few genuinely risky parts. I harden and refactor the risky parts rather than throwing away work that already functions, which keeps cost and timeline sane.
Often yes, and not because you did anything wrong. AI tends to generate code that works in the demo but leaves auth, access control, and secrets exposed. A short audit will tell you exactly where you stand before you commit to any fixes.
Not even slightly. AI tools are a real way to get an idea in front of users fast, and I use them in my own workflow every day. My job starts where 'it works on my screen' needs to become 'it works for everyone, safely.'
Send a short note with a link or a description, and I'll tell you what it'll take to get it production ready.
A few other selected ways I can help