Polish your resume with AI in 30 seconds

Why your current resume isn't getting responses

Most of the time, the problem isn't your experience — it's how the experience is written.

The natural way most people write resume bullets is the past-tense shorthand that feels obvious to whoever lived through the work: "Worked on the backend API," "Helped with the migration," "Was responsible for QA." That phrasing is almost invisible to ATS systems and almost meaningless to a recruiter scanning eighty resumes in an afternoon. The signal is there — you did the work — but the framing doesn't extract it.

The second problem is that most people send the same resume to every role. The wording was probably calibrated to the first jobs you applied to, or to no role in particular. When you apply to a Google L4 posting that mentions "distributed systems" and "API design" in every bullet of the JD, and your resume says "back-end development experience," you're not technically wrong — but the ATS keyword parser doesn't see a match, and the system filters you out before a human ever reads your name.

Polish solves both: it rewrites the language to be specific and active, and it reweights keyword choice to fit the JD you're actually applying to.

Built from real recruiting flows, not generic resume tips

The polish AI was trained on the ATS heuristics, recruiter-attention patterns, and bullet-framing signals used at companies like Google, Meta, and Amazon — by engineers who reviewed candidate resumes inside those companies. The output isn't "use action verbs" boilerplate. It's a calibrated rewrite for a specific company and level. A bullet polished for Google L4 reads differently from the same bullet polished for Meta E3, because the two ATS configurations weight keywords differently and the bar for senior-vs-junior signal lives in different cues. The system encodes that difference.

Per-JD tailoring, not template stuffing

There's a kind of "ATS optimization" that doesn't actually work: copying the JD's keywords into every corner of your resume. ATS systems aren't fooled. They check not just whether a keyword exists, but the context it appears in. "Kubernetes" inside a bullet describing a project where you led the architecture transmits a different signal than "Kubernetes" sitting in a skills list at the bottom.

Applr's tailoring goes a layer deeper than keyword presence. After you paste the JD, the system identifies the highest-weight keywords in this specific posting — words that appear in must-have skills, that recur across core responsibilities, that cluster in the role description — and maps your actual experience to those keywords semantically.

If you wrote "microservices architecture" and the JD uses "distributed systems," the system recognizes the semantic overlap and aligns your phrasing to the JD's vocabulary — only when the underlying experience genuinely corresponds. If there's no match, it doesn't force one. That's a JD-analysis and cover-letter problem, not a polish job.

Tailoring also shows up in bullet ordering. If the JD is a backend-heavy senior role and your resume currently leads with frontend experience, polish promotes the most JD-relevant work to the top. Not magic — what a good career counselor would do, in thirty seconds, with a match score to validate the rearrangement.

The result is a resume calibrated to one specific JD, not a generic template that's mediocre against every posting. Each application gets its own polish. At thirty seconds per polish, that's not a meaningful overhead — and the gap between a generic resume (typically 40-50% match) and a tailored polished version (typically 75-90%) is the gap between getting filtered by ATS and reaching a human screener.