AI cannot fix a bad resume. It can only polish it.

Here is the issue we ran into while building CareerButler V2:

We built this powerful AI to tailor resumes to job descriptions. But we noticed that if the user uploaded a weak resume to start with, the AI couldn't save it.

If your resume says "I led a team to success" without any numbers, the AI doesn't know what 'success' means. It can't optimize what isn't there.

So it either:

Polishes the fluff (still useless).

Starts hallucinating and making up numbers (dangerous).

We realized we couldn't just build a 'tailor'. We had to build a 'fixer' first.

It's called Resume Critique.
Before we begin optimizing your resume for a specific job, we force a deep scan. It parses your resume section-by-section to fix the foundation.

We actually highlight the specific bullet points that are weak, right on the screen, and give you tips on exactly how to improve them.

And I don't mean generic advice like 'Add more metrics.' That is lazy.

I mean specific, actionable pushes.

Here is the difference between Generic Advice vs. CareerButler V2:

Generic Tool: "You should add more metrics."
CareerButler: "In your role as Project Manager, you listed 'Led a team to success.' This is vague. Consider adding the team size and a specific outcome. For example: 'Led a team of 10 engineers to deliver the product 2 weeks ahead of schedule.'"

Generic Tool: "Your summary is too long."
CareerButler: "Your professional summary is currently 6 sentences long. Recruiter heatmap research shows they stop reading after sentence 2. We recommend cutting this down to a highlight reel of your top 3 hard skills."

It’s like having a career coach proofread your resume before you start applying. It stops you from optimizing a bad resume.

Fix the foundation first. Then tailor it.
We are launching V2 on Product Hunt this Sunday. Come see if your resume passes the check.