I Built a Feedback Loop for Job Interviews

Before AI, interview improvement was mostly manual. I would finish an interview, open my notebook, reconstruct questions from memory, and only later realize better answers. The workflow worked, but it was slow, inconsistent, and dependent on recall.

So I built Interview OS, it converted every interview into structured training data. I paste a transcript and the job description (JD), and the system starts with context modeling: role intent, company direction, likely jobs-to-be-done, success criteria for year one, and the capability gap the role is meant to close.

Because the assistant is trained on my resume variants and prior interview history, it can map my profile to that gap, extract all questions asked, evaluate my responses, highlight what I missed, and generate stronger alternatives grounded in research. It also surfaces reusable response patterns that already work well in my interviews.

The highest-leverage layer is the notes intelligence. Over time, the system identifies canonical themes: where I consistently underperform and where I reliably create signal. That gives me a living improvement map instead of isolated post-interview reflections.

The product is designed mobile-first, then desktop. I can review a transcript in transit, ask follow-up questions inline, request jargon explanations in plain language, save polished answers, and keep building a searchable interview journal that compounds across opportunities.

Mobile-first workflow

• Paste transcript + JD after interview
• Get question extraction + response quality analysis
• Save improved answer variants with notes
• Track strength themes vs improvement themes over time