iNTERVIEWBIO

One-pager
Next steps
Risks and mitigations
Business model

Inspiration

Interview anxiety is a problem our team faces during internship interviews as students. Particularly now, companies adopt AI and algorithms to screen candidates, but candidates have yet to understand how they are screened. In the current landscape, two main categories of interview technology are context analysis and live-interview AI assistants. As target users, our team believes the existing interview technology does not address our pain points. Firstly, our interview performance does not depend solely on our content; it also depends heavily on how stressed we are at the moment. We can deliver better content when we have a comfortable level of stress. Secondly, employers are not looking for AI-generated answers from candidates; they are looking for authentic responses that reveal a candidate’s personality. That leads us to our problem statement: How can we help candidates showcase their best, authentic selves from a biological perspective?

What it does

We created iNTERVIEWBIO, a system that uses real-time biometric data to monitor candidates’ stress levels during interviews. With the usage of the Presage SDK, it captures biological signals: heart rate ( BPM) and respiration rate ( resp/min), and translates them into real-time stress-level feedback, hinting at the candidate to calm down with a blue background when reaching a threshold, and post-interview insights with a stress heatmap, helping users to better understand and manage their interview performance.

How we built it

We developed an iOS application using Swift and Xcode on macOS. Our project is built on the Presage SDK, which enables real-time analysis of biometric data such as heart rate and breathing.

Challenges we ran into

Our team does not have a member with biological expertise. It is challenging for us to understand and relate the biometrics we can use to manage stress, as well as our interview performance. In this process, we learn more knowledge about the biological metrics and how they are strongly related to our public speaking confidence.

Accomplishments that we're proud of

It is the first time our team has done Swift programming or used the Presage SDK. Our team challenges itself to a steep learning curve within 24 hours to learn two new tech stacks and complete this project. We learn to start with a specific metric we are looking for, then follow the starting point to a deep dive and understand the code with the help of AI, which enables us to debug and achieve our expected outcomes.