Quick Apply
Client
Personal
Location
London, UK
Year
2025
Overview
Quick Apply is an AI-powered application that reduces job applications from hours of manual tailoring to a 30-second experience. The product was built around a validated problem: while tailored applications perform better, most job seekers skip the process due to time pressure and cognitive load.
Through user research, iterative AI testing, and hands-on development, I built and launched a focused MVP that prioritises speed, reliability, and real-world use. The beta launch showed strong early traction, with high success rates, repeat usage, and clear potential to scale into a broader job search tool.
The Problem
Job seekers know that tailored CVs and cover letters perform better, but most don’t tailor consistently because it takes too long. Based on interviews and personal research, the average application takes 2–3 hours to customise properly, which leads to fatigue, inconsistent quality, and fewer applications overall.
This problem is made worse by ATS filtering. A large portion of applications are rejected before a human ever sees them, meaning generic applications are often wasted effort. The time spent manually rewriting documents could be better used applying to more roles, networking, or improving skills.
Key Insights
Most job seekers skip tailoring not because they don’t care, but because the effort doesn’t scale when applying to 50+ roles.
Existing CV tools focus on formatting and templates, not fast, role-specific content.
Early AI tests showed that speed alone wasn’t enough. Reliability and formatting consistency were critical, especially for CV content.
Reducing friction mattered more than adding features. The simpler the flow, the more likely users were to complete applications.
The Solution
I built a focused MVP that takes a CV and a job description and generates tailored application content in under 30 seconds. Users can upload files or paste text, depending on what’s easiest for them.
The app uses AI to parse job requirements and generate a personalised statement and cover letter, with instant copy and download options. Authentication and submission history allow users to reuse and iterate on past applications. The UI stays intentionally minimal, with a dark theme and mobile-friendly layout to keep the focus on speed and clarity.
Impact & Results
The beta launched successfully in August 2025 with real users.
95% successful AI generation rate
Average processing time of 25 seconds
30+ beta users in the first week
4.2 out of 5 average user rating
Application time reduced from hours to minutes
45% return usage within the first month
Users reported higher-quality applications and less burnout during job searches.
What I Learnt
This project pushed me beyond design into full product delivery. I learned how to translate UX decisions into working code, handle AI reliability issues through prompt iteration, and design systems around real constraints like performance and failure rates.
Most importantly, I learned that good UX in AI products is less about novelty and more about trust, speed, and consistency. Shipping a real product with real users changed how I think about design decisions, trade-offs, and what actually matters once something leaves Figma.
