Context
A Changing Job Market in the Age of AI
When I moved to Berlin and began my own job search, I noticed how diverse the job market was, with people from different cultural, educational, professional, and language backgrounds. At the same time, AI was changing how candidates were screened and evaluated, from AI-powered CV screening to AI-led interviews.
Me (blue and red printed T-shirt) in discussion with fellow job seekers and career mentors at a Berlin employment meetup, listening to diverse perspectives on navigating the German job market.
Problem
Job Searching Had Become a Process to Manage
Finding the right opportunity was only the beginning. Each application involved understanding the role, preparing and tailoring a CV, writing a cover letter, navigating AI-driven screening, and managing multiple applications, follow-ups, and recruiter conversations. Keeping track of everything became a job in itself.
Research
Understanding the Job Search Journey
Before exploring solutions, I wanted to understand how people actually manage their job search. I captured recurring behaviors, frustrations, and workarounds through informal conversations with job seekers and recruiters.
Analysis
From Insights to Product Decisions
Conversations with job seekers revealed four recurring themes. Rather than solving every problem directly, I focused on the areas where Tracka could create the greatest impact within its own experience.
01 · Personalized Applications
"I have to tweak my CV for every job, and in Germany the cover letter matters too. Sending the same one everywhere just doesn't work anymore"
Insight
Tailoring applications was the most time-consuming part of the job search. Users needed faster ways to personalize resumes and cover letters without compromising quality.
Feature
Resume & Cover Letter Builder
Analyze the job description
Extract ATS keywords
Generate personalized application materials
02 · ATS Optimization
"Everyone keeps talking about ATS these days... before I apply, I compare my CV with the job description using AI"
Insight
Candidates increasingly relied on ATS checkers and manually compared resumes with job descriptions to improve their chances of reaching recruiters. Identifying the right keywords had become an additional step in every application.
Feature
ATS Match Analyzer
Extract ATS keywords from the job description
Highlight missing skills and keywords
Estimate ATS match score
03 · Application Management
"By the time someone emails me back, the job posting has disappeared, I can't remember which CV I sent, where I saved the cover letter, or what the role was actually looking for."
Insight
As applications progressed, important information became scattered across job portals, emails, folders, spreadsheets, and documents. Reconstructing the full context of an application often took longer than expected, especially during interviews and follow-ups.
Feature
Unified Workspace
Save the original job description
Store tailored CVs and cover letters
Keep recruiter contacts and notes together
04 · Frictionless Onboarding
"I already uploaded my CV... why am I filling in my work experience, education, and address all over again? After a few applications, those long forms get really tiring."
Insight
While we couldn't eliminate repetitive forms across external job portals, we could remove the same friction from our own onboarding experience.
Feature
AI-Powered Onboarding
Upload an existing CV
Automatically extract profile details
Review and confirm before getting started
User Journey
Designing for Activation and Retention
The journey map captures how the experience evolves from a user's first interaction to repeated use. Comparing both journeys helped identify moments of uncertainty, opportunities to reduce friction, and design decisions that support long-term engagement.
Product Hierarchy
Defining the Information Hierarchy
Before moving into UI design, I defined the information architecture and key user entry points. The experience separates marketing, authentication, onboarding, and the core workspace into a clear navigation structure, creating an intuitive journey for both first-time and returning users.
Design System
From Design System to Consistent Interfaces
A design system is only valuable if it can be applied consistently. I translated the system into structured documentation that AI could interpret, enabling reusable components, predictable layouts, and consistent outputs across the product.
Concept Exploration
Exploring Ways to Structure the Experience
Before committing to the final direction, I explored different ways of bringing the Tracker, Resume Builder, and Letter Builder together. I evaluated each concept against core UX considerations such as navigation clarity, cognitive load, scalability, and continuity across the job-search journey.
This helped me narrow the exploration to the direction that provided the clearest structure while keeping the three core workflows connected.
Rather than choosing a direction based on visual preference, I used the product goals and user needs as evaluation criteria. The selected concept became the basis for the next stage, where I could validate the structure further through prototyping and usability testing.
Concept A
Concept B
Concept C
Production Process
From Hand Sketch to Production UI
I started with hand-drawn sketches to explore the information hierarchy, content structure, and overall layout. Once the concept was clear, I recreated it as low-fidelity wireframes in Figma. These wireframes served as the design specification and context for AI Coding assistant to generate the initial high-fidelity interface, which I then refined through iterative design decisions.
Technical Architecture
Building a Scalable Architecture
Rather than building custom infrastructure, I evaluated modern tools that could accelerate development while keeping costs low and maintenance minimal. The architecture prioritizes rapid iteration today, with the flexibility to replace individual services as the product scales.
Business Logic
Building a Consistent ATS Scoring System
The Problem - Tracka's ATS Analyzer scores how well a resume lines up with a job description before someone applies. Tracka initially used an LLM to calculate ATS scores. Because the model could interpret the same CV and job description differently across runs, the same application would receive different scores.
The Decision - I separated understanding from scoring. The LLM extracts structured job requirements once, while a deterministic ATS engine calculates the score using predefined matching logic and weighted dimensions. The result is consistent, transparent, and repeatable. The same input always produces the same score.
How It Works
Reflection
What I Learned While Building Tracka
Designing, building, and testing the product exposed challenges that only became visible during implementation. These experiences reshaped how I think about design systems, product logic, and product decisions.
01 · Layered Decision Making: Reliable products are built on clear fallback paths.
Building Tracka required defining product behavior for situations where a single rule wasn't enough. For example, deciding which theme the application should display meant evaluating multiple signals before reaching a final decision. This taught me to design layered decision paths that keep the experience predictable even when preferred inputs aren't available.
02 · Consistency & Reliability: Reliable products build more trust than clever features.
While designing the job tracker, I initially allowed users to add a job by pasting a job posting URL. Since different job boards use different page structures, universal URL extraction wasn't practical. Instead of forcing the feature to work everywhere, I redesigned the workflow so users paste the job description directly while the AI automatically extracts and fills the required fields. This reinforced that reliability creates more value than automation that only works in certain situations.
03 · Continuous Refinement: A design system evolves through continuous feedback.
While building Tracka with an AI coding assistant, I found that every new feature introduced new components, variants, and design refinements. Simply updating the design system in Figma wasn't enough because the AI also needed the latest design context to generate consistent interfaces. I learned to establish a feedback loop that kept both the design system and the AI aligned as the product evolved.
User Feedback
Build a tailored job application in under 15 minutes.
Tracka is live and continuously evolving through feedback from early users. I'm refining the experience with each iteration before making it available to everyone. Below are a few comments shared by people who have used it so far.
