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I designed and developed an AI-assisted application-building system that supports product definition, architecture, code generation, testing, review, deployment and controlled iteration.
Unlike unrestricted code generation, the platform uses structured workflows, specialist agents, human approvals, validation gates and deployment controls to create more accountable and reproducible software builds.
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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Ledger — AI CFO Platform
Ledger is an AI platform that acts as an always-on Chief Financial Officer for founders — connecting to accounts, learning cash rhythm, and surfacing risks before they become real problems. I designed the full landing page.
My role: UI/UX Design
Scope: Landing page
Financial tools for founders usually feel either cold (dashboards) or intimidating (accounting software).
For Ledger, we borrowed from old-world institutional finance instead — serif type, oil-painting textures, classical architecture — so an AI CFO feels like real authority, not another app.
This color pallet works so well with data and makes everything look premium. love it!
The low-poly scene already has a strong sense of place, and the 60fps browser target makes the interaction feel ambitious in the best way. I especially like how the restrained palette lets the “Explore the Silo” typography lead the experience.
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.