Qualifi is an AI-powered job matching platform designed for recent graduates who are stuck in the entry-level experience paradox. Users upload their resume, set their preferences, and get matched to real, live job listings scored by AI on a 0-100 scale. Each match comes with a plain-language explanation of why the job fits (or doesn't), so job seekers actually understand what they're up against. Built from scratch over one semester, Qualifi covers the full product lifecycle: market research, brand identity, UX design, full-stack development, and go-to-market strategy.
qualifi application
roles
product designer, UX/UI developer, brand strategist
programs
claude, sublime, vercel, github, adobe express
timeline
november 2025 - june 2026
brand kit
customer brand journey
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Research & Ideation - Identified the entry-level experience paradox as the core problem through market research and competitive analysis, then formalized findings into two personas: Jordan Park (recent grad, job seeker) and Derek Mills (skeptical hiring manager)
Brand Development - Built a full brand system before writing a single line of code, including color palette, typography, voice pillars, and social media content, ensuring design consistency throughout development
UX Design - Mapped the full customer journey and designed a 4-step onboarding flow that moves users from signup to their first match without friction
Iteration - Refined the product through multiple rounds of stakeholder presentations, including a formal pitch to media industry leaders and a capstone presentation to faculty
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Built with HTML, CSS, JavaScript, and Bootstrap, deployed on Vercel with GitHub version control
Backend powered by Supabase, managing user authentication, profiles, certifications, saved jobs, and a job score cache
Live job listings pulled from Adzuna and JSearch APIs, filtered dynamically by the user's role preferences and location
Claude API integrated for AI-powered match scoring and plain-language job explanations via the "Explain My Match" panel
Shared auth helper manages session tokens consistently across all pages of the app
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Color-coded match scoring: green (85%+), blue (70%+), amber (50%+), red (below 50%) for instant visual read on fit
"Explain My Match" AI panel breaks down exactly why a job scored the way it did, referencing the user's actual resume and skills
Preference-aware filtering: results update based on role and location settings, with cache-clearing logic to prevent stale data
4-step onboarding flow: persona selection, profile setup, resume upload, and skills review before the user sees a single match
Employer waitlist page with lead capture for the platform's future B2B side
instagram ads
Outcome - A fully deployed, AI-powered job matching web app built from concept to live product in one semester, covering product strategy, brand identity, UX design, and full-stack development.
Live MVP - get-qualifi.vercel.app