Here is the video link, we used loom so that is why we cannot put in below in the video link spot : https://www.loom.com/share/3622495134b242468a96f1981b7fcadb?sid=51eab9a6-7ffb-4224-90cb-c90a3bfb9b10
Inspiration
Screening is often about résumés and connections, not skills. We wanted to make hiring fairer and more focused on performance.
What it does
TailorQ parses résumés to based on the resume text, generates custom interview questions using behavioral and technical agents, those agents review your responses send it to a scoring agent which ranks candidates based on their answers and effort.
How we built it
Frontend in HTML/CSS, backend in Flask with Python and AI-generated questions tailored to the job and to the resume with Google ADK agents.
Challenges we ran into
Handling messy résumé formats, setting fair scoring rules, preventing copy–paste answers, and syncing work across branches.
Accomplishments that we're proud of
A working end-to-end demo where applicants upload, answer, and recruiters instantly see ranked results.
What we learned
How to merge AI with product design, collaborate faster with Git, and rethink fairness in candidate evaluation.
What's next for TailorQ
Improving bias detection, adding recruiter customization, and scaling for real-world hiring pipelines.
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