PrepSmart
An AI interview coach that helps users uncover valuable insights from their past experiences, refine storytelling, and practice with realistic mock interviews. With real-time feedback and support, users can prepare anytime, anywhere—boosting your confidence and giving you a competitive edge in your job search.
Problem Statement
How might we make job search more efficient while helping people build confidence?
Problem Background
Navigating the job market is a challenging and time-consuming endeavor for many individuals. On average, it takes about 3 to 6 months to secure employment, with job seekers submitting approximately 10 to 20 applications to receive a single interview invitation. Moreover, the probability of securing a job interview from one application stands at 8.3%, and it often requires 10 to 15 interviews to obtain one job offer. (Parris, How long should a job search take in 2025?: FlexJobs 2024)
The process is further complicated by the scattered nature of job search resources, requiring applicants to invest significant time in researching companies, tailoring resumes, and preparing for interviews. This fragmented approach can lead to inefficiencies and prolonged periods of unemployment, adding stress to an already daunting process.
For newcomers to Canada, the challenges are even more pronounced. Despite possessing extensive experience and qualifications, many face difficulties securing employment due to a lack of Canadian work experience. This barrier often results in prolonged job searches, with some individuals taking up to nine months or more to find professional employment. (Cheong, I moved to Canada but struggled for months to get a job, even with years of experience. it shattered my confidence. 2025)
These statistics underscore the need for a more streamlined and supportive job search process, one that consolidates resources and provides tailored guidance to help job seekers navigate the complexities of securing employment efficiently.
Research Insights
User Pain Points
- The top 3 most difficult activities for job seekers are interview preparation, completing job-related assessments, and portfolio creation.
- Interview preparation is viewed as the most time-consuming activity, followed by portfolio creation.
- Although interview and portfolio preparation generates the most frustration, AI was deemed more helpful in text-based activities, such as generating and proofreading resumes or cover letters.
- Help with interview preparation was the most frequently requested assistance from AI in the qualitative responses.
Supporting Data
Survey responses confirm that interview preparation is the most challenging part of the job search process. A successful interview requires thorough research on industry trends, company news, and commonly asked questions. Job seekers must also refine their responses and find opportunities to practice through mock interviews. However, many job seekers already have full-time or part-time commitments, making it difficult to dedicate time to this process. Additionally, finding and booking mock interview sessions is another hurdle, as these resources become more specialized and harder to access at more senior levels. Without external support and a structured approach, navigating interview preparation can feel overwhelming and inefficient.
Landing on the Solution
Given that interview preparation is the most challenging and time-consuming aspect of the job search, our solution focuses on reducing research time and improving structured practice. Job seekers struggle to consolidate relevant information, refine their responses, and find mock interview opportunities.
Explanation of Solution
A website application that is a PrepSmart MVP, with a focus on implementing AI to provide feedback and enhance interview story scripts.
- Basic job search info: the welcoming page with two fields where the user provides the job title and job description of the job that they are interviewing for
- Question bank: the AI searches for the most frequently asked questions on the web and consolidates a question bank according to the user’s input in their job search info
- Story inventory: users can type their responses down as scripts before answering the practice questions in the question bank. The inventory is a form that breaks down each part of a response in the STAR format for clear structure and guidance.
- AI coaching and feedback: AI evaluates the user’s response in terms of how relevant it is to the job that they are interviewing for or the specific question that they are answering. The evaluation could be used on the pure text-based story inventory, or the user can start a mock interview session to practice delivering a story.
User Journey
Mockups
Future Steps
I would want to continue refining the design because design is a skill I want to continue learning. Since I am a developer, I would want to code this out after I refine the design as I also want to explore the power of AI APIs
Learnings
Product Manager Learnings:
Ruby Hu
- Diving deep into user pain points with research and surveys
- Creating wireframes from the MVP features
Designer Learnings:
Designer Learnings:
Jo Sturdivant
- Adapting to an Established Team: Joining the team in week 6 of 8 was challenging, as I had to quickly adapt to existing workflows, dynamics, and goals. This mirrors real-world situations where you often integrate into teams mid-project, and flexibility is essential.
- Work-Blocking for Efficiency: With only two weeks to complete the project, I learned the importance of a structured work-blocking system. This approach allowed me to manage my time effectively and meet deadlines under pressure.
- Making Data-Driven Design Decisions: Unlike my past projects, I had to rely on research conducted by others. This was a valuable experience in using pre-existing data to guide design decisions, helping me focus on the core insights without starting from scratch.
Developer Learnings:
Developer Learnings:
Vanady Beard
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As the back-end developer, I learned how important it is to create efficient and reliable systems that support the entire application. This experience also taught me the importance of optimising the database and ensuring the backend is scalable and easy to maintain.
Developer Learnings:
Stephen Asiedu
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As a back-end developer, I've come to understand the importance of being familiar with various database systems and modules. This knowledge enables me to build diverse applications and maintain versatility in my work. I've also learned that the responsibility for making the right choices rests on my shoulders, guided by my best judgement.
Developer Learnings:
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Developer Learnings:
Maurquise Williams
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- Process of Creating an MVP: Developing a Minimum Viable Product (MVP) taught me how to focus on delivering core functionalities balancing between essential features and avoiding scope creep.
- Collaboration in a Real-World Tech Setting: This experience taught me how to collaborate efficiently in a fast-paced tech environment, keeping the team aligned and productive, even while working remotely across time zones.
- Sharpening Critical Thinking and Problem-Solving Skills: This experience honed my ability to think critically and solve problems efficiently. By tackling challenges and finding quick solutions, I sharpened my decision-making and troubleshooting skills in a dynamic, real-world setting.
Developer Learnings:
Jeremiah Williams
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All in all this experience was very awesome I learned that in coding with others being transparent is key
Developers Learnings:
Justin Farley
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I learned how important communication is when working with a team. Communication provides understanding, advice, ideas, and much more. While working with the product team, I’ve found that communication keeps everything flowing smoothly. Working with a team also showed me that every member brings something different to the table and we all have to work together in order to align and meet our end goal.