DomainFlip — How I Started With an Empty Repo and Built a Full Domain Investment Platform

Aliasgar Sogiawala — Sun, 07 Jun 2026 20:50:46 +0000

This is a submission for the GitHub Finish-Up-A-Thon Challenge.

I’ll keep this short since the repo docs go much deeper into the product and build. Hope you enjoy it.

What I Built

DomainFlip AI is a domain investment intelligence platform for analyzing domain quality, valuation signals, registration history, comparable sales, and acquisition potential in one workspace.

Core features

Analyze page

Enter a domain and trigger a full analysis pipeline:

100-point composite score across 10 weighted signals
AI advisory layer via Gemini
Python ML valuation reference
RDAP registrar lookup
Comparable sales matching with similarity scoring
3-year value projection with trend and risk drivers
Deterministic Buy / Watch / Avoid investment report

Domain Comparison

Compare 2 domains side by side, or run Battle Mode with 3–5 domains to surface liquidity, brand, and acquisition leaders.

Market Intelligence page

Explore:

TLD performance charts
Price distribution bands
category breakdowns
anomaly detection for hot and soft pricing pockets
a Domain Comparison Lab for interactive multi-domain analysis

Watchlist

A Convex-backed watchlist with target buy price, max budget, and negotiation stance tracking.

AI Assistant

A Gemini-powered assistant with Google Search grounding for domain market questions and naming suggestions.

Stack

Next.js 16 App Router · TypeScript · Tailwind CSS v4 · Convex · Clerk · Gemini (@google/genai) · Python ML · RDAP · Geist Sans/Mono

Demo

The Comeback Story

This project did not start with a polished product or even a clear system already in place. It started with a blank repo, a rough idea, and the challenge of turning domain investing into something that felt structured, technical, and actually useful.

Over the course of the build, it grew from a simple analyzer concept into a full domain intelligence workspace. The scoring engine came first, then valuation logic, then RDAP lookups, comparable sales, projections, AI advisory, market intelligence, watchlist tracking, and comparison tools. Each layer made the product feel less like a demo and more like a real decision-making surface.

What I’m proud of is that the project didn’t stay at the level of “enter a domain, get a number.” It became a system that tries to explain why a domain looks strong or weak, what the risks are, what the market context looks like, and how someone might actually act on that information.

By the end, it felt less like I had built a single feature and more like I had built the foundation of a real product.

My Experience with GitHub Copilot

GitHub Copilot was a real part of this build, especially once the project grew beyond a simple prototype and started turning into a full product with multiple moving parts.

It helped most in the places where the architecture was already clear in my head, but the implementation was repetitive, verbose, or easy to lose time on. That included wiring up utility functions, filling in TypeScript shapes, building repeated UI structures, scaffolding fallback objects, and speeding up the more mechanical parts of API and component work.

As the project expanded into scoring logic, AI advisory flows, market views, watchlist persistence, ML integration, and chart-heavy UI, Copilot was useful for maintaining momentum. Instead of slowing down on boilerplate or repetitive code, I could stay focused on the product decisions and system design.

What I liked most was that it worked well as an accelerator, not a replacement. I still made the calls on architecture, valuation logic, product direction, and tradeoffs, but Copilot helped reduce friction and made it much easier to keep shipping quickly throughout the challenge.