AppCraftly
An AI-Powered Career Positioning Tool
My Role: Product Owner & Solo Builder
Overview
MY ROLE
Product Owner, Builder, AI Prompt Engineer
TEAM SIZE
1 (Solo Project)
TECH STACK
Bolt.new, OpenAI API, Google Gemini
KEY RESULTS
Tested with 50+ beta users, User-reported 20% increase in interview callbacks
The Problem: The "Black Hole" of Job Applications
Every job seeker knows the frustration of the "spray and pray" approach. Countless hours are spent tailoring resumes and cover letters, only to have them disappear into a digital void. The core problem is a misalignment: generic applications fail to speak to a company's specific challenges. I saw an opportunity to use AI to bridge this gap, transforming the application process from a game of chance into a strategic exercise in value alignment.
The Solution: Hyper-Personalization at Scale
The solution was AppCraftly, an AI tool designed to help job seekers position themselves as the ideal solution to a company's problems. The platform was built around three core AI-driven features:
AI-Driven Experience Analysis
The tool ingests a user's resume and a target job description, using AI to identify core competencies and map them to the company's most pressing needs.
Achievement Statement Generation
AppCraftly transforms passive responsibilities into powerful, quantified impact statements. It helps users reframe their experience using the X-Y-Z formula favored by top tech companies.
Tailored Recommendations
The final output is a set of tailored recommendations for the user's resume and cover letter, ensuring their application directly addresses the challenges and keywords found in the job description.
The Build: Leveraging Google Gemini for a Smarter Engine
As a solo builder, I relied on a "no-code first" approach with Bolt.new for the front-end. The core intelligence of the platform, however, was powered by a sophisticated AI pipeline. For this, I used Google's Gemini model to handle the complex natural language processing tasks. Gemini was instrumental in parsing unstructured text from resumes and job descriptions, identifying key themes, and generating human-like, context-aware recommendations. This allowed me to build a powerful and nuanced AI tool without needing a dedicated machine learning team.
Impact: Helping Users Land More Interviews
The platform was tested with over 50 beta users, primarily students and recent graduates navigating the competitive tech job market. The feedback was overwhelmingly positive, validating the hypothesis that a targeted, AI-driven approach is more effective than a generic one.
50+
Beta Testers
Provided feedback during the pilot phase.
20%
Increase in Callbacks
Self-reported by users who applied the tool's recommendations.
Learnings & Future Steps
The key learning from AppCraftly was the immense user appetite for hyper-personalization. Users are tired of generic advice and crave tools that provide specific, actionable insights for their unique situation. The future of AppCraftly would involve building out a more robust feature set, including interview preparation tools and a system for tracking application success rates, to create a truly end-to-end AI career co-pilot.