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iOS security app with modern cryptography — Swift, FIPS 203 (ML-KEM)
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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Designed a modern health biomarkers mobile app that turns complex medical data into a simple, insight-driven experience.
Beautiful UI with clear overview, detailed biomarker trends (Hemoglobin, Albumin, etc.), optimal/sub-optimal indicators, progress tracking, and an AI health assistant for personalized guidance.
Very refined
What it actually takes to build a multi-tenant edtech SaaS from scratch
Everyone wants to build a SaaS. Few people talk about what happens when 500+ schools hit your system at the same time, each expecting their data to be completely isolated.
I built EduPilotPro, an AI-powered school operating system serving schools across South Asia, the Middle East, and Africa. Here's what the "multi-tenant SaaS" part actually looked like:
Tenant isolation is the first real decision. Every school needs to feel like they own the platform. Their data, their config, their branding. But you're running one codebase, one database cluster, one deployment. We used PostgreSQL with row-level isolation on self-hosted Supabase. Each school gets its own logical boundary without the cost of spinning up separate infrastructure per tenant.
The 8:45 AM problem. Schools don't use software evenly throughout the day. At 8:45 AM, every teacher in every school marks attendance simultaneously. That's a traffic spike that would embarrass most startups. We used Redis + BullMQ to queue attendance writes and process them asynchronously, so the UI stays responsive even when thousands of classrooms hit the system at once.
AI agents, not AI features. We didn't bolt a chatbot onto a school ERP. We built 6 purpose-built AI agents: attendance, admissions, fees, communication, reporting, and scheduling. Each one handles a specific administrative function end-to-end. A teacher types "All students present except Ali who is sick and Ahmed who is 10 minutes late" and the attendance agent parses it, records it, and notifies parents. No forms. No checkboxes.
The stack: Next.js + React + TypeScript on the frontend. Node.js + self-hosted Supabase on the backend. PostgreSQL + Redis + BullMQ for data and queuing. Railway for cloud infrastructure.
What I'd tell any founder about to build multi-tenant SaaS:
Design your tenant model before you write a single feature. Retrofitting isolation is painful.
Assume your traffic will be spiky, not smooth. Queue everything you can.
If you're adding AI, make it do the whole job, not half of it. A half-automated workflow is worse than a manual one because now nobody knows who's responsible.
→ Full case study: EduPilotPro on Contra
Perfect
Echo is a multi-tenant SaaS for AI customer support with realtime chat, an AI voice agent, and AI automation. I was the primary developer on the full build, frontend, backend, and the AI layer. The AI is the core. It runs OpenAI, Claude, Gemini, and Grok through one multi-model setup, so a client can switch providers without a rewrite. A RAG pipeline connected to a vector database grounds every answer in the client's own content, so the chatbot never returns generic output. VAPI powers the voice agent, so customers can speak to support on a live call. Each tenant gets its own AI agent built on its own documents. The stack is Next.js 15 and React 19 inside a Turborepo monorepo, with separate apps for the dashboard, the embeddable chat widget, and backend services. Realtime chat runs on Convex. Clerk handles auth. API keys are encrypted per tenant through AWS Secrets Manager, so no two clients share credentials or data. Launch day held 60 live conversations at once with zero dropped sessions. What the client gets: an AI chatbot and voice agent that answer from their own content, work across multiple LLM providers, and stay isolated and secure per tenant.:
Perfect
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