JobAid AI
Job seekers often face the challenge of tailoring their resumes and cover letters for different job roles. Most existing tools are either generic or lack deep personalization. JobAid AI was designed to bridge this gap by using AI to analyze job descriptions and optimize application materials with up to 95% accuracy thereby helping users stand out in the competitive job market and land interviews faster.
Client
Jobaid AI
DELIVERABLES
BRAND DIRECTION UI/UX DESIGN
Year
2025
Role
Creative Direction
Overview
JobAid AI is an AI-powered platform designed to help job seekers craft stronger, tailored job applications and significantly improve their chances of getting hired. The product leverages intelligent automation to analyze job descriptions, optimize resumes, generate context-specific cover letters, and eventually automate the job application process altogether.
JobAid AI is an AI-powered platform designed to help job seekers craft stronger, tailored job applications and significantly improve their chances of getting hired. The product leverages intelligent automation to analyze job descriptions, optimize resumes, generate context-specific cover letters, and eventually automate the job application process altogether.
JobAid AI is an AI-powered platform designed to help job seekers craft stronger, tailored job applications and significantly improve their chances of getting hired. The product leverages intelligent automation to analyze job descriptions, optimize resumes, generate context-specific cover letters, and eventually automate the job application process altogether.
Context & Problem
Context & Problem
Context & Problem
The job market has become increasingly competitive, especially for tech professionals. Many applicants submit generic resumes and cover letters, leading to poor outcomes even when they are qualified. Meanwhile, recruiters struggle with a high volume of low-quality applications.
JobAid AI was created to solve this dual-sided challenge:
Help job seekers tailor their applications to specific roles with ease.
Reduce the time spent on manual applications.
Improve application quality through data-driven resume and cover letter enhancement.
The job market has become increasingly competitive, especially for tech professionals. Many applicants submit generic resumes and cover letters, leading to poor outcomes even when they are qualified. Meanwhile, recruiters struggle with a high volume of low-quality applications.
JobAid AI was created to solve this dual-sided challenge:
Help job seekers tailor their applications to specific roles with ease.
Reduce the time spent on manual applications.
Improve application quality through data-driven resume and cover letter enhancement.
The job market has become increasingly competitive, especially for tech professionals. Many applicants submit generic resumes and cover letters, leading to poor outcomes even when they are qualified. Meanwhile, recruiters struggle with a high volume of low-quality applications.
JobAid AI was created to solve this dual-sided challenge:
Help job seekers tailor their applications to specific roles with ease.
Reduce the time spent on manual applications.
Improve application quality through data-driven resume and cover letter enhancement.
My Role & Responsibilities
As the sole Product Designer, I led the end-to-end design process. My responsibilities included:
Shaping the product vision alongside stakeholders
Conducting product and user research
Designing the full brand identity
Creating UX flows, wireframes, and interactive prototypes
Designing the web application and marketing website
Collaborating with developers and AI engineers to integrate intelligent features
Conducting usability tests and iterating based on feedback
As the sole Product Designer, I led the end-to-end design process. My responsibilities included:
Shaping the product vision alongside stakeholders
Conducting product and user research
Designing the full brand identity
Creating UX flows, wireframes, and interactive prototypes
Designing the web application and marketing website
Collaborating with developers and AI engineers to integrate intelligent features
Conducting usability tests and iterating based on feedback
As the sole Product Designer, I led the end-to-end design process. My responsibilities included:
Shaping the product vision alongside stakeholders
Conducting product and user research
Designing the full brand identity
Creating UX flows, wireframes, and interactive prototypes
Designing the web application and marketing website
Collaborating with developers and AI engineers to integrate intelligent features
Conducting usability tests and iterating based on feedback
Research & Discovery
1. User & Market Research I conducted user interviews and surveys with tech professionals actively seeking roles to understand their pain points. Key insights:
Many users reuse one resume for all applications
Tailoring applications feels overwhelming and time-consuming
Users want AI assistance, but it must feel personal and not robotic
1. User & Market Research I conducted user interviews and surveys with tech professionals actively seeking roles to understand their pain points. Key insights:
Many users reuse one resume for all applications
Tailoring applications feels overwhelming and time-consuming
Users want AI assistance, but it must feel personal and not robotic
1. User & Market Research I conducted user interviews and surveys with tech professionals actively seeking roles to understand their pain points. Key insights:
Many users reuse one resume for all applications
Tailoring applications feels overwhelming and time-consuming
Users want AI assistance, but it must feel personal and not robotic
2. User Surveys & Personas: I created surveys targeted at students and remote workers to understand their job application process, frustrations with current process and tools, and wishlist features. I didn’t just come about these Personas through surveys alone. I had virtual interviews with 3 participants and their stories led to the development of two primary personas: Aisha the recent university graduate and David, the Mid-Level Professional Pivoting Careers
2. User Surveys & Personas: I created surveys targeted at students and remote workers to understand their job application process, frustrations with current process and tools, and wishlist features. I didn’t just come about these Personas through surveys alone. I had virtual interviews with 3 participants and their stories led to the development of two primary personas: Aisha the recent university graduate and David, the Mid-Level Professional Pivoting Careers
2. User Surveys & Personas: I created surveys targeted at students and remote workers to understand their job application process, frustrations with current process and tools, and wishlist features. I didn’t just come about these Personas through surveys alone. I had virtual interviews with 3 participants and their stories led to the development of two primary personas: Aisha the recent university graduate and David, the Mid-Level Professional Pivoting Careers
3. Competitive Benchmarking I reviewed similar platforms like Teal HQ and Resume.io to analyze feature gaps and user complaints. These insights informed the initial feature set and value propositions.q
3. Competitive Benchmarking I reviewed similar platforms like Teal HQ and Resume.io to analyze feature gaps and user complaints. These insights informed the initial feature set and value propositions.q
3. Competitive Benchmarking I reviewed similar platforms like Teal HQ and Resume.io to analyze feature gaps and user complaints. These insights informed the initial feature set and value propositions.q
Resume.io |
Resume.io |
Resume.io |
TEAL HQ |
TEAL HQ |
TEAL HQ |
OpenResume |
OpenResume |
OpenResume |
Wireframing & Ideation
I sketched initial concepts on paper and quickly moved into low-fidelity wireframes in Figma, covering onboarding, resume optimization, and cover letter generation flows. These were reviewed with stakeholders and tested with early users.
I sketched initial concepts on paper and quickly moved into low-fidelity wireframes in Figma, covering onboarding, resume optimization, and cover letter generation flows. These were reviewed with stakeholders and tested with early users.
I sketched initial concepts on paper and quickly moved into low-fidelity wireframes in Figma, covering onboarding, resume optimization, and cover letter generation flows. These were reviewed with stakeholders and tested with early users.
Information Architecture
I structured the application around four key flows:
Resume Upload & Analysis
Job Description Matching
Cover Letter Generation
AI-Driven Job Application (coming soon)
I wrote out individual flows and used AI to combine and come up with a general visual representation of the Information Architecture.
I structured the application around four key flows:
Resume Upload & Analysis
Job Description Matching
Cover Letter Generation
AI-Driven Job Application (coming soon)
I wrote out individual flows and used AI to combine and come up with a general visual representation of the Information Architecture.
I structured the application around four key flows:
Resume Upload & Analysis
Job Description Matching
Cover Letter Generation
AI-Driven Job Application (coming soon)
I wrote out individual flows and used AI to combine and come up with a general visual representation of the Information Architecture.
Branding & Visual Design
Brand Identity I directed the visual identity to feel intelligent, trustworthy, and approachable; avoiding the overly corporate aesthetic of many job platforms.
JobAid AI was designed to evoke trust, professionalism, and innovation, which are the key values for a platform that handles sensitive career data and personal growth.
UI Elements
A clean, modular layout for readability
Contextual guidance for users as they edit
Clear success states to boost user confidence
Accessibility-focused color contrast and typography
Brand Identity I directed the visual identity to feel intelligent, trustworthy, and approachable; avoiding the overly corporate aesthetic of many job platforms.
JobAid AI was designed to evoke trust, professionalism, and innovation, which are the key values for a platform that handles sensitive career data and personal growth.
UI Elements
A clean, modular layout for readability
Contextual guidance for users as they edit
Clear success states to boost user confidence
Accessibility-focused color contrast and typography
Brand Identity I directed the visual identity to feel intelligent, trustworthy, and approachable; avoiding the overly corporate aesthetic of many job platforms.
JobAid AI was designed to evoke trust, professionalism, and innovation, which are the key values for a platform that handles sensitive career data and personal growth.
UI Elements
A clean, modular layout for readability
Contextual guidance for users as they edit
Clear success states to boost user confidence
Accessibility-focused color contrast and typography
Logo icon |
BRAND LOGO |
Color & Typography:
Primary Colors:
#00237A (Oxford Blue): Represents authority, intelligence, and reliability. This anchors the brand in a serious, professional tone which we thought is perfect for career-building.
#3C9EDB (Skye Blue): Adds freshness, modernity, and a sense of optimism. It gives the brand a tech-forward and friendly edge, counterbalancing the seriousness of the navy.
Primary Colors:
#00237A (Oxford Blue): Represents authority, intelligence, and reliability. This anchors the brand in a serious, professional tone which we thought is perfect for career-building.
#3C9EDB (Skye Blue): Adds freshness, modernity, and a sense of optimism. It gives the brand a tech-forward and friendly edge, counterbalancing the seriousness of the navy.
Primary Colors:
#00237A (Oxford Blue): Represents authority, intelligence, and reliability. This anchors the brand in a serious, professional tone which we thought is perfect for career-building.
#3C9EDB (Skye Blue): Adds freshness, modernity, and a sense of optimism. It gives the brand a tech-forward and friendly edge, counterbalancing the seriousness of the navy.
BRAND COLORS IDENTITY |
BRAND COLORS |
Typeface
Inter Tight: Sleek, space-efficient, and highly legible, ideal for dashboard-heavy interfaces and compact info layouts.
Plus Jakarta Sans: Rounded yet modern which gives the brand a slightly approachable, human feel without sacrificing clarity.
This combination was chosen to reflect the dual nature of JobAid AI: serious and reliable, yet innovative and supportive.
Typeface
Inter Tight: Sleek, space-efficient, and highly legible, ideal for dashboard-heavy interfaces and compact info layouts.
Plus Jakarta Sans: Rounded yet modern which gives the brand a slightly approachable, human feel without sacrificing clarity.
This combination was chosen to reflect the dual nature of JobAid AI: serious and reliable, yet innovative and supportive.
Typeface
Inter Tight: Sleek, space-efficient, and highly legible, ideal for dashboard-heavy interfaces and compact info layouts.
Plus Jakarta Sans: Rounded yet modern which gives the brand a slightly approachable, human feel without sacrificing clarity.
This combination was chosen to reflect the dual nature of JobAid AI: serious and reliable, yet innovative and supportive.
PRIMARY COLOR |
SECONDARY COLOR |
Design Execution
JobAid AI was envisioned as a seamless, AI-powered ecosystem that supports job seekers across various touchpoints:
Web Application: The core platform where users upload resumes, generate AI-enhanced cover letters, and manage application documents.
Marketing Website: Introduces the product, shares use cases, and allows user onboarding via free trials.
What ties the ecosystem together is its document intelligence pipeline — once a resume is uploaded, every other interaction is informed by that document. AI models pre-fill fields, suggest edits, and personalize content. The UX journey remains consistent: minimal friction, fast feedback, and clearly defined outcomes.
JobAid AI was envisioned as a seamless, AI-powered ecosystem that supports job seekers across various touchpoints:
Web Application: The core platform where users upload resumes, generate AI-enhanced cover letters, and manage application documents.
Marketing Website: Introduces the product, shares use cases, and allows user onboarding via free trials.
What ties the ecosystem together is its document intelligence pipeline — once a resume is uploaded, every other interaction is informed by that document. AI models pre-fill fields, suggest edits, and personalize content. The UX journey remains consistent: minimal friction, fast feedback, and clearly defined outcomes.
JobAid AI was envisioned as a seamless, AI-powered ecosystem that supports job seekers across various touchpoints:
Web Application: The core platform where users upload resumes, generate AI-enhanced cover letters, and manage application documents.
Marketing Website: Introduces the product, shares use cases, and allows user onboarding via free trials.
What ties the ecosystem together is its document intelligence pipeline — once a resume is uploaded, every other interaction is informed by that document. AI models pre-fill fields, suggest edits, and personalize content. The UX journey remains consistent: minimal friction, fast feedback, and clearly defined outcomes.
Web Application |
Handoff Snaphot |
Testing & Iteration
Prototypes were built and tested in Figma with 12 early access users. The testing covered key flows:
Resume upload and analysis
Cover letter generation
Document revision history
Onboarding
Prototypes were built and tested in Figma with 12 early access users. The testing covered key flows:
Resume upload and analysis
Cover letter generation
Document revision history
Onboarding
Prototypes were built and tested in Figma with 12 early access users. The testing covered key flows:
Resume upload and analysis
Cover letter generation
Document revision history
Onboarding
Key findings and improvements:
Users loved the auto-fill feature but wanted more control so I added a manual-fill option.
The resume analysis scores were unclear, so I redesigned the feedback layout for better clarity and breakdown.
Some users felt the onboarding was too long and overwhelming, so I streamlined the process by shortening the flow.
Each round of testing brought measurable improvements in task completion time and user satisfaction.
Users loved the auto-fill feature but wanted more control so I added a manual-fill option.
The resume analysis scores were unclear, so I redesigned the feedback layout for better clarity and breakdown.
Some users felt the onboarding was too long and overwhelming, so I streamlined the process by shortening the flow.
Each round of testing brought measurable improvements in task completion time and user satisfaction.
Users loved the auto-fill feature but wanted more control so I added a manual-fill option.
The resume analysis scores were unclear, so I redesigned the feedback layout for better clarity and breakdown.
Some users felt the onboarding was too long and overwhelming, so I streamlined the process by shortening the flow.
Each round of testing brought measurable improvements in task completion time and user satisfaction.
Challenges & Solutions
Like most fast-moving product builds, JobAid AI faced several real-world challenges which are both strategic and executional during the design and handoff process.
Like most fast-moving product builds, JobAid AI faced several real-world challenges which are both strategic and executional during the design and handoff process.
Like most fast-moving product builds, JobAid AI faced several real-world challenges which are both strategic and executional during the design and handoff process.
1. Simplifying complex AI interactions for non-technical users
Many users were new to AI-powered tools and didn’t fully understand how their input data was being analyzed or scored. Our goal was to offer an intelligent system without overwhelming users with technical jargon or rigid flows.
Many users were new to AI-powered tools and didn’t fully understand how their input data was being analyzed or scored. Our goal was to offer an intelligent system without overwhelming users with technical jargon or rigid flows.
Many users were new to AI-powered tools and didn’t fully understand how their input data was being analyzed or scored. Our goal was to offer an intelligent system without overwhelming users with technical jargon or rigid flows.
Solution:
We approached this by designing a clean, conversational interface that guides users step-by-step through uploading their resumes or job links. We replaced complex configuration options with intuitive toggles and smart defaults. Informational tooltips were added at critical points to explain the “why” behind our suggestions without requiring additional user effort. This struck a balance between transparency and simplicity.
Solution:
We approached this by designing a clean, conversational interface that guides users step-by-step through uploading their resumes or job links. We replaced complex configuration options with intuitive toggles and smart defaults. Informational tooltips were added at critical points to explain the “why” behind our suggestions without requiring additional user effort. This struck a balance between transparency and simplicity.
Solution:
We approached this by designing a clean, conversational interface that guides users step-by-step through uploading their resumes or job links. We replaced complex configuration options with intuitive toggles and smart defaults. Informational tooltips were added at critical points to explain the “why” behind our suggestions without requiring additional user effort. This struck a balance between transparency and simplicity.
2. Optimizing AI prompt engineering for accurate, relevant suggestions
To make JobAid AI genuinely useful, the AI had to not only analyze documents effectively but also generate tailored suggestions for improvement. Initial testing revealed that some outputs were too generic or missed contextual cues from the job descriptions.
To make JobAid AI genuinely useful, the AI had to not only analyze documents effectively but also generate tailored suggestions for improvement. Initial testing revealed that some outputs were too generic or missed contextual cues from the job descriptions.
To make JobAid AI genuinely useful, the AI had to not only analyze documents effectively but also generate tailored suggestions for improvement. Initial testing revealed that some outputs were too generic or missed contextual cues from the job descriptions.
Solution:
We invested time in refining our prompt structures, feeding the AI large sets of annotated CVs and job descriptions across different roles and industries. By fine-tuning prompts and layering contextual understanding, we boosted the relevance of AI-generated insights. This work helped us achieve an impressive 95% accuracy rate in analysis and recommendations, ensuring users felt like they were getting expert-level guidance.
Solution:
We invested time in refining our prompt structures, feeding the AI large sets of annotated CVs and job descriptions across different roles and industries. By fine-tuning prompts and layering contextual understanding, we boosted the relevance of AI-generated insights. This work helped us achieve an impressive 95% accuracy rate in analysis and recommendations, ensuring users felt like they were getting expert-level guidance.
Solution:
We invested time in refining our prompt structures, feeding the AI large sets of annotated CVs and job descriptions across different roles and industries. By fine-tuning prompts and layering contextual understanding, we boosted the relevance of AI-generated insights. This work helped us achieve an impressive 95% accuracy rate in analysis and recommendations, ensuring users felt like they were getting expert-level guidance.
Final Outcome
The platform successfully launched in private beta, receiving positive feedback for its ease of use and impact. Users reported getting more callbacks and expressed satisfaction with the tailored application process.
Early results showed:
95% accuracy in resume parsing
70% of users completed both resume + cover letter flows in one session
Strong retention due to perceived value in feedback quality
The platform successfully launched in private beta, receiving positive feedback for its ease of use and impact. Users reported getting more callbacks and expressed satisfaction with the tailored application process.
Early results showed:
95% accuracy in resume parsing
70% of users completed both resume + cover letter flows in one session
Strong retention due to perceived value in feedback quality
The platform successfully launched in private beta, receiving positive feedback for its ease of use and impact. Users reported getting more callbacks and expressed satisfaction with the tailored application process.
Early results showed:
95% accuracy in resume parsing
70% of users completed both resume + cover letter flows in one session
Strong retention due to perceived value in feedback quality
Key Learnings
AI Needs Human Context to Be Useful
Designing an AI-powered tool isn’t just about generating smart outputs, it's about making those outputs meaningful. We learned that users responded best to suggestions when they were clearly explained and contextually appropriate. This pushed us to focus on making feedback feel personalized, not generic.
Users Want Control, Not Just Automation
Job seekers want AI assistance, but they also want to feel in control of their documents. Features that allowed editing, toggling suggestions, or reviewing feedback in steps saw higher engagement than automated, one-click fixes. Design should empower and not replace the users.Designing for Trust is as Critical as Usability
Trust is everything when you’re asking users to upload personal documents. We found that visual clarity, clean UI, reassuring microcopy, and consistent flows significantly impacted how secure users felt. Building a professional, transparent experience was key to early adoption.Start Simple, Then Expand Based on Real Use
We initially scoped broad features, but narrowed down to solving just two key pain points: improving resumes and cover letters with actionable insights. This focused MVP approach helped us ship faster, gather feedback, and validate demand without overbuilding.Collaborating Across Functions Elevates the Product
Working closely with developers, product leads, and AI engineers allowed us to adapt quickly to technical realities (e.g., prompt tuning, data limitations). Constant iteration and feedback loops made the product stronger and easier to use.Microinteractions Matter More Than You Think
From hover states to loading animations, small UI details made the app feel smoother and more “alive.” Users mentioned these unprompted in feedback sessions — proof that polish can boost perceived quality and trust.Job Seekers Are Emotionally Invested; Design Should Reflect That
Applying for jobs is stressful, and users often feel vulnerable. We learned to be more intentional with tone, hierarchy, and UX writing, using calm, confident language to guide them, not overwhelm them.
AI Needs Human Context to Be Useful
Designing an AI-powered tool isn’t just about generating smart outputs, it's about making those outputs meaningful. We learned that users responded best to suggestions when they were clearly explained and contextually appropriate. This pushed us to focus on making feedback feel personalized, not generic.
Users Want Control, Not Just Automation
Job seekers want AI assistance, but they also want to feel in control of their documents. Features that allowed editing, toggling suggestions, or reviewing feedback in steps saw higher engagement than automated, one-click fixes. Design should empower and not replace the users.Designing for Trust is as Critical as Usability
Trust is everything when you’re asking users to upload personal documents. We found that visual clarity, clean UI, reassuring microcopy, and consistent flows significantly impacted how secure users felt. Building a professional, transparent experience was key to early adoption.Start Simple, Then Expand Based on Real Use
We initially scoped broad features, but narrowed down to solving just two key pain points: improving resumes and cover letters with actionable insights. This focused MVP approach helped us ship faster, gather feedback, and validate demand without overbuilding.Collaborating Across Functions Elevates the Product
Working closely with developers, product leads, and AI engineers allowed us to adapt quickly to technical realities (e.g., prompt tuning, data limitations). Constant iteration and feedback loops made the product stronger and easier to use.Microinteractions Matter More Than You Think
From hover states to loading animations, small UI details made the app feel smoother and more “alive.” Users mentioned these unprompted in feedback sessions — proof that polish can boost perceived quality and trust.Job Seekers Are Emotionally Invested; Design Should Reflect That
Applying for jobs is stressful, and users often feel vulnerable. We learned to be more intentional with tone, hierarchy, and UX writing, using calm, confident language to guide them, not overwhelm them.
AI Needs Human Context to Be Useful
Designing an AI-powered tool isn’t just about generating smart outputs, it's about making those outputs meaningful. We learned that users responded best to suggestions when they were clearly explained and contextually appropriate. This pushed us to focus on making feedback feel personalized, not generic.
Users Want Control, Not Just Automation
Job seekers want AI assistance, but they also want to feel in control of their documents. Features that allowed editing, toggling suggestions, or reviewing feedback in steps saw higher engagement than automated, one-click fixes. Design should empower and not replace the users.Designing for Trust is as Critical as Usability
Trust is everything when you’re asking users to upload personal documents. We found that visual clarity, clean UI, reassuring microcopy, and consistent flows significantly impacted how secure users felt. Building a professional, transparent experience was key to early adoption.Start Simple, Then Expand Based on Real Use
We initially scoped broad features, but narrowed down to solving just two key pain points: improving resumes and cover letters with actionable insights. This focused MVP approach helped us ship faster, gather feedback, and validate demand without overbuilding.Collaborating Across Functions Elevates the Product
Working closely with developers, product leads, and AI engineers allowed us to adapt quickly to technical realities (e.g., prompt tuning, data limitations). Constant iteration and feedback loops made the product stronger and easier to use.Microinteractions Matter More Than You Think
From hover states to loading animations, small UI details made the app feel smoother and more “alive.” Users mentioned these unprompted in feedback sessions — proof that polish can boost perceived quality and trust.Job Seekers Are Emotionally Invested; Design Should Reflect That
Applying for jobs is stressful, and users often feel vulnerable. We learned to be more intentional with tone, hierarchy, and UX writing, using calm, confident language to guide them, not overwhelm them.
Conclusion
JobAid AI pushed the boundaries of what job seekers can expect from digital tools. By combining intelligent document parsing, adaptive design, and user-driven improvements, we created a platform that’s not only useful, but empowering.
This case study reflects my ability to lead full-cycle product design, integrate with AI capabilities, and turn complex workflows into clean, approachable experiences. I'm proud of how JobAid AI came to life and even prouder of how it’s helping real people unlock better career opportunities.
JobAid AI pushed the boundaries of what job seekers can expect from digital tools. By combining intelligent document parsing, adaptive design, and user-driven improvements, we created a platform that’s not only useful, but empowering.
This case study reflects my ability to lead full-cycle product design, integrate with AI capabilities, and turn complex workflows into clean, approachable experiences. I'm proud of how JobAid AI came to life and even prouder of how it’s helping real people unlock better career opportunities.
JobAid AI pushed the boundaries of what job seekers can expect from digital tools. By combining intelligent document parsing, adaptive design, and user-driven improvements, we created a platform that’s not only useful, but empowering.
This case study reflects my ability to lead full-cycle product design, integrate with AI capabilities, and turn complex workflows into clean, approachable experiences. I'm proud of how JobAid AI came to life and even prouder of how it’s helping real people unlock better career opportunities.