Project Info
This project did not submit a demo video on Devpost.
Inspiration
In today’s competitive job market, it's alarming to see MBA graduates from top schools struggling to secure employment, while many immigrants find themselves overlooked despite their qualifications. For these candidates, landing a job can often hinge on a single interview—the make-or-break opportunity that can determine their future. Breaking into Product Management can feel like a formidable challenge without the right guidance. The role demands a deep, holistic understanding of business, collaboration across cross-functional teams, and a strong obsession on delivering an exceptional customer experience. There’s no one-size-fits-all formula to measure a candidate’s potential as a product manager, making the path even more intimidating for those without prior experience. Inspired by this gap, we’re driven to provide a solution—one where candidates can upload their resume and the job description of their desired role, and in return, receive a tailored interview experience that helps them effectively prepare, sharpen their skills, and build confidence for their actual interview.
What it does
Our product allows customers of all backgrounds to interview for PM roles and receive feedback on their interview. We also perform an analysis on the interview transcripts and give feedback on the interview on a scale of 1 to 10.
How we built it
We used Reflex for both the front-end and back-end web development, enabling us to build a fully functional web application entirely in Python without needing to handle separate frameworks or languages for the front-end. We built a Full stack app that uses vapi ai to record and transcribe interviews. We further exploit the capabilities of vapi to generate questions and follow ups to ask the user using gpt 4.0 model We also use the gpt4.0 model and prompt engineer the vapi app to analyse the transcribed interviews and rate the users feedback based on our defined rubric.
Challenges we ran into
Zeroing in on a voice agent was very difficult as there were a lot of options and each model had their own pros and cons. Prompt engineering to get the right feedback from the voice agent was hard to perform.
Accomplishments we're proud of
We were able to build an AI voice agent that can conduct product manager interviews, capable of providing target company based interviews. Gained exposure to voice agents and explored the prompt generation to enhance the accuracy of the ai model.
What we learned
We learnt about the extensive capabilities of vapi and voice agents in general. We learnt how to use AI agents to take interviews and give feedback to customers. Learnt the python reflex framework and used it develop the FE & BE.
What's next
We plan on analysing the performance of the candidate in the interview and assigning a schedule that he/she can follow to improve his interview score. Introduce a progress tracker that updates iteratively based on the mock interviews taken by the candidates. Use Prompt engineering to further refine the evaluation rubric iteratively to generate more accurate and authentic reports. Take in the resume and job description of the candidate into the knowledge base creating context for each user that interacts with the AI voice agent, and tailor interview experiences.
Prep.AI
Inspiration
Breaking into Product Management can feel like a formidable challenge without the right guidance. The role demands a deep, holistic understanding of business, collaboration across cross-functional teams, and a strong obsession on delivering an exceptional customer experience. There’s no one-size-fits-all formula to measure a candidate’s potential as a product manager, making the path even more intimidating for those without prior experience. Inspired by this gap, we’re driven to provide a solution—one where candidates can upload their resume and the job description of their desired role, and in return, receive a tailored interview experience that helps them effectively prepare, sharpen their skills, and build confidence for their actual interview.
What it does
Our product allows customers of all backgrounds to interview for PM roles and receive feedback on their interview. We also perform an analysis on the interview transcipts and give feedback on the interview on a scale of 1 to 10.
Analysis
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Metric
- 1
Figures cover GitHub contributors during the hackathon window. A co-authored commit counts in full for each author, so per-member totals add up to more than the whole-team figures.
Technology
- HTMLIn code
- PythonIn code
- TypeScriptClaimed
2 of 3 appear in the indexed code. 1 claimed on Devpost could not be matched to code, which may simply mean the tool leaves no trace in the repository.
AI coding agents
No AI coding agent signals were found in this repository.
Detected from committed agent config files and commit authorship. Absence of a signal is not proof an agent was unused.
Codebase size
Source size
45 KB
Source files
24
Counts recognized source files only; vendored directories, binaries and lockfiles are excluded, so this is smaller than the repository on disk.
Repository
anagha1999/AInterview
35 files · 25.7 MB · @ 4411078
Structure
Interface
11 files · 31%Screens, components and styles rendered to the user.
Application logic
13 files · 37%Domain rules, services and shared utilities.
Supporting
Layers are inferred from where files sit in the tree, not from reading the code. A project that names its directories unconventionally will read oddly here — open the file browser to check anything the diagram implies.
Languages
- Python92%
- HTML4%
- Markdown3%
Share of indexed source by file size. Binary and vendored files are excluded.
Dependencies
aiinterview-app/requirements.txt
pypi · 2- psycopg2-binary
- reflex
Declared in the repository’s manifests at the indexed commit. A declared package is not proof it is used, and runtime dependencies are listed first.
This project’s features have not been analysed yet.
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