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IASA Access is a fleet management and operations platform designed to help companies manage vehicles, drivers, utilization, service requests, maintenance, and operational reporting.
I was responsible for the development, implementation, and ongoing maintenance of the mobile and cloud infrastructure powering the platform across Android, iOS, and cloud environments. I worked across the full technology stack, from mobile applications and backend/cloud services to integrations and operational infrastructure.
Tech Stack: Google Cloud Platform, Firebase, Flutter, Angular, NodeJS, Git.
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Darakai — Food Delivery App
Darakai is a modern food delivery app concept designed to make discovering restaurants, ordering meals, and tracking deliveries simple, fast, and enjoyable.
The project covers the complete customer journey—from onboarding and restaurant discovery to checkout, real-time tracking, delivery confirmation, and profile management.
Key Features
🍔 Restaurant & food discovery
🔎 Search and category browsing
🏪 Restaurant menus & reviews
🛒 Cart & order management
💳 Multiple payment options
📍 Delivery address management
🚴 Real-time order tracking
⭐ Order rating & feedback
👤 Profile & order history
📱 Cross-platform Flutter-ready experience
Design Focus
A clean and modern UI with strong orange branding, intuitive navigation, clear CTAs, consistent components, and a smooth ordering experience optimized for mobile users.
Project Goal
To create a scalable food delivery experience that connects customers with restaurants while making every step of the ordering process effortless.
Tags:
#FoodDelivery #MobileApp #UIUXDesign #Flutter #AppDesign #Figma #ProductDesign #UXDesign #UI Design #RestaurantApp #DeliveryApp #MobileUX #CrossPlatform #Prototyping #UserExperience
The end-to-end journey is mapped thoughtfully, especially the handoff from discovery to tracking and delivery confirmation. The orange accent keeps the dense feature board energetic; I’d use it sparingly on the checkout step to make the primary action even clearer.
I’ve been building SabiFlow for a while, and honestly, the most interesting part of the project isn’t the code.
It's the problem.
Because it's personal.
I've experienced that thing where money comes in and somehow, without you really noticing, it starts disappearing.
Not because you don’t earn enough.
Not necessarily because you're irresponsible either.
Sometimes money simply has no job when it arrives.
And as an engineer, that got me thinking:
What if the problem isn't budgeting? What if the problem is that we’re asking people to make too many good decisions at the exact moment they have the most temptation to make bad ones?
That question became the foundation for SabiFlow.
Instead of telling someone, "You should save 20% of your income," I started thinking about what would happen if the system simply helped assign every inflow a purpose the moment it arrived.
That led me down a rabbit hole.
Funnels.
Automated distribution.
Wallet infrastructure.
Virtual accounts.
User behaviour.
Transaction flows.
KYC.
Compliance.
Even the psychology behind notifications.
And this is probably my favourite part of being both an engineer and a founder.
I don’t just ask:
"How do I build this feature?"
I ask:
"Why does this problem exist, and what kind of system could make dealing with it easier?"
Then the engineer in me comes along and asks:
"Okay… but how do we actually make this work reliably?" 😂
That tension between the founder thinking about the problem and the CTO thinking about the system is probably what I enjoy most about building SabiFlow.
I’m still figuring a lot of it out.
But I'm curious:
What’s a problem you’ve experienced personally that eventually made you want to build something around it?
The framing around “why does this problem exist?” is exactly the kind of product thinking that keeps a financial tool from becoming another dashboard. SabiFlow sounds strongest where the behavioral insight meets the practical system design—especially around notifications and reliable follow-through.
just shipped the core of Skillship — an AI-powered LMS built for schools.
One platform, many schools, and one rule that shaped every decision: School A must never see School B's data. Tenant-scoped models, filtered querysets at the base class, UUIDs everywhere.
Four AI features live:
— Career Pilot: personalised career paths for students
— Question generator: chapter PDF in, board-aligned MCQs out
— Adaptive quizzes: difficulty follows actual performance
— Semantic search: teachers ask in plain English, pgvector finds the material
Django + DRF for the LMS core, a separate FastAPI service for all AI work (Gemini + a pgvector RAG pipeline), Next.js 14 on the front. Django brokers every AI call — the AI layer never touches the browser.
The hard part wasn't the AI. It was making multi-tenancy boring enough that no future dev can leak a school's data by accident.
Open for new work — full-stack builds, AI integrations, RAG pipelines, multi-tenant SaaS. Send me a project.
The tenant boundary is where I'd put the first failure test. I'd try to make a user from one school retrieve another school's record before I trusted any of the AI features.