Writing
Thoughts on engineering, architecture, product, and building things.
How We Integrated AI Into an Existing SaaS Platform Without Rewriting the Entire System
Most AI projects begin from scratch. Mine didn't. I joined an existing production SaaS to design and integrate an AI infrastructure that could support intelligent assistants, Copilot-style workflows, and future AI capabilities without disrupting the application's existing architecture. This wasn't about calling an LLM API. It required designing a scalable AI layer with model routing, context engineering, memory management, usage limits, caching, security, and observability. This article shares the engineering decisions, trade-offs, and lessons I learned while bringing production AI into an already mature software platform.
Building AI Products with a Product Mindset
Why most AI demos fail to become products, and the mental shift required to build something people actually use every day.
Building a Fintech App in React Native
Lessons from shipping a payments app to 10k+ users biometric auth, offline-first architecture, and the security pitfalls I almost fell into.
System Design for SaaS at Scale
How I architect multi-tenant SaaS products from day one — database isolation strategies, caching layers, and the tradeoffs nobody talks about.
What I Learned Leading My First Engineering Team
The uncomfortable transition from individual contributor to team lead — context-switching, code review culture, and why 1:1s are the most valuable meeting on your calendar.