Case study

ForgeAI

Career intelligence
TypeScript product
Signals & scoring

AI-powered career audit that pulls together GitHub activity, LeetCode practice, resume structure, and ATS readiness—so improvement areas read as a single narrative instead of scattered tabs.

Preview

Screen recording of the product flow. Repository has setup and source if you want to run it locally.

Stack

Next.js React TypeScript Tailwind Python LLM APIs

Impact

Hiring loops reward consistency across code, practice, and narrative. ForgeAI is built to surface gaps in one pass—activity depth, problem patterns, document structure, and how each reads to an ATS—so you iterate with intent instead of guessing what to fix first.

Architecture

Client: Next.js App Router, TypeScript, Tailwind, focused audit flows and report views.

Intelligence: ingestion and scoring over GitHub, LeetCode-style metadata, resume text, and rubric checks for ATS alignment.

Delivery: clear module boundaries so each signal can evolve independently (new parsers, models, or rules without rewriting the UI).

Shipped

Unified career snapshot across repos, practice, and documents
Rubric-driven ATS and resume structure feedback
Extensible scoring and narrative generation
Typed API surface between UI and analysis workers