After 47 applications, the problem was never my CV. It was which jobs I picked.
Earlier this year I found myself job hunting again after 8+ years in product, most recently as a Product Owner in UK banking.
A job hunt gives you almost no feedback. You apply, nothing comes back, and you are left guessing whether the role was never a fit, the application was weak, or the ad was never real. With nothing to go on, everyone reaches for the same two moves: rewrite the CV, or apply to more jobs.
I did both, for months. Neither was the problem.
WorkstationAI is a workspace for running the whole hunt, and last week I shipped the part that closes the loop.
When a role goes cold, you send it back and ask what happened. You get one verdict, not advice. Fit gap, writing lesson, or noise. Most declines turn out to be noise, and being told so is worth more than another rewrite, because it stops you fixing something that was never broken.
Once a few of those exist, the retrospective reads your whole search at once, across every application rather than one at a time.
Mine, across 47: roles where I had a gap in the core requirement converted at 8%. Roles where I did not, 71%. Same person, same background, 9x difference. My aim was the problem, and no single application could ever have shown me that.
So what it is built to do is help you apply to fewer jobs and choose them better. Since I started running it on myself, volume is down about a third and the share of well-matched applications went from roughly 20% to 65%.
The build problem I am still chewing on: the retrospective has to say something useful at 3 applications, not 20. Cold start is where anything like this dies.
Two questions, and I reply to everyone:
When an application goes silent, what do you actually change next?
Has anything ever told you to apply to fewer jobs? Would you have believed it?
Li You, building WorkstationAI
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