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Over the past few years, I've had the opportunity to build software that goes far beyond simple websites.
I've developed AI-powered SaaS platforms, enterprise applications, and government digital systems used across multiple countries. From national civic engagement platforms to AI automation tools and scalable web applications, my focus has always been on solving complex problems with clean engineering.
My expertise includes:
• AI & LLM Applications
• Full-Stack Web Development
• React, Next.js, Node.js & Python
• SaaS Product Development
• Government & Enterprise Systems
• API Integrations & Cloud Infrastructure
• Performance Optimization & Scalable Architecture
I enjoy working with founders, startups, agencies, and enterprises that need more than just code—they need a technical partner who can turn ambitious ideas into production-ready products.
If you're building something challenging and need an experienced engineer who can take ownership from architecture to deployment, I'd love to connect.
#AI #SoftwareEngineering #FullStack #React #Python #SaaS #Startups #Enterprise #ProductDevelopment #Contra
The network for creativity
Join 1.25M professional creatives like you
Connect with clients, get discovered, and run your business 100% commission-free
Creatives on Contra have earned over $150M and we are just getting started
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Awesome dude
Before you add AI to your product, find the screen where your users already copy and paste.
That's usually where the real AI feature lives. Not a chatbot in the corner, but a step people already do by hand: summarising a ticket, drafting a reply, tagging an upload, pulling fields out of a PDF.
Here's the order I follow when a founder asks me to add AI to an existing SaaS:
1. Pick one workflow, not "AI everywhere." Watch where users leave your app to paste something into ChatGPT and come back. That round trip is your feature.
2. Keep the model behind your own API. One service owns prompts, retries, logging and cost limits, so you can switch providers without touching the frontend.
3. Make it tenant-aware from day one. Each customer's data, prompts and usage stay scoped to their account, or the first enterprise security review will stop you.
4. Ship a draft, not a decision. Let the AI suggest and the user approve. You earn trust and get real feedback before you automate anything.
5. Measure accepted output, not tokens. If users rewrite most of what it produces, the prompt or the context is wrong, and that's your real roadmap.
Most AI features that flop didn't fail because of the model. They were bolted onto the wrong step.
What's the one task your users still copy into ChatGPT?
Watching where users paste into ChatGPT and come back is such a practical way to find the real feature. Most teams start with the chatbot instead.
Blog Listening Web Application With Admin Dashboard.
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