AI Resume Polish

Polish your resume with AI in 30 seconds

Paste your resume + any JD. AI rewrites every bullet for ATS and JD-match — never invents experience. See a before/after diff and a JD-match score. First polish is free.

  • ~30 seconds
  • First one free
  • UK & US roles
  • Voice interview + report

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.

Not sure what's wrong with the resume you have? Run it through the free ATS resume checker first — score, five-dimension breakdown, no account.

Check my resume free →

Written against the posting, not against a list of resume tips

The output isn't "use action verbs" boilerplate. Every rewrite is anchored to one posting: the words that JD actually asks for, in the places a screener reads first. A bullet polished for a Google L4 posting reads differently from the same bullet polished for a Meta E3 posting, because the two postings ask for different things and the junior-versus-senior signal sits in different cues. That difference comes from reading the JD in front of it — not from any claim to know what a company scores internally.

§ 01How it works

Three steps. Thirty seconds.

01

Paste your resume + the JD

No formatting required. Drop in your current resume text or a PDF, plus the job description for the role you're targeting. The AI builds a gap analysis: which of your experiences map directly, which need re-framing, and which JD signals are missing or under-weighted in your draft.

02

AI rewrites every line, JD-aware

Each bullet is rewritten against three tests — ATS parseability, keyword match in the right context, and impact framing. No bullets get fabricated. If a metric is missing, the AI asks for it instead of inventing one.

03

Get a JD-match score and the PDF

Line-by-line diff showing what changed and why. A match score against this specific JD versus the original. Download a clean PDF, ready to submit.

§ 02What polish actually does

Rewrites your bullets — never your experience.

Before

Worked on backend API development; helped with system migration project.

Vague verb, no scope, no metric. ATS extracts 'backend' and 'system migration' but signals nothing about seniority.

After

Designed and shipped a distributed order API serving 12k req/s; led migration from monolith to 4 services, cutting p95 latency from 320ms to 190ms.

Verb pulled from your actual scope. Metrics referenced from your profile history, not invented. ATS now sees 'distributed', 'p95 latency', 'monolith to services'.

§ 03 · Hard constraint

Every word traces back to something you already wrote.

Hand a generic LLM a resume and ask it to make the bullets stronger — it will, by inventing a number that sounds plausible. The polished resume reads better. The content is partly fiction.

For OPT/CPT students this is not a stylistic concern. Background checks pull employment dates, project details, and titles. There are documented cases on 一亩三分地 of offers rescinded pre-start because the resume diverged from what verification turned up. The cost of fabrication is not a re-do — it is the offer.

Applr's polish has a hard rule: every word in the output must trace to something you provided. Need a number? It pulls from your profile history, or asks. Never invents. The diff view shows the source of each change so you can audit it line by line.

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 written against one specific posting rather than a template that is mediocre against all of them. Each application gets its own polish, and at half a minute each that is not a meaningful overhead.

§ 04Validation

A match score against the actual JD — not a generic checklist.

Generic resume

Same resume sent to every role. Most signals technically present, none weighted to this specific posting's must-haves.

Polished for this JD

The score counts how many of the signals this JD asks for show up in the right context — inside bullets describing relevant work, not stuffed into a skills list. You see the score for your own resume; we don't publish an average, because one wouldn't mean anything across the jobs people apply to.

§ 05Tradeoffs

Where Applr fits among the options.

Setup timeJD-awareWon't fabricateCostMatch score
Applr AI Resume Polish~30 secondsYes — per-JD keyword mappingYes — every word traces to your inputFree first polish, $9/mo afterScored per JD, shown to you
ChatGPT roleplay3-5 min (write your own prompt)Partial — no scoring or extractionNo — invents metrics on request$20/moUnscored, prompt-dependent
Resume.io / Kickresume10-20 min (templates)No — template-basedYes (you write everything)$3-$10/moFormat-only, no keyword tailoring
Career coach1-2 week wait, $150-$300/hrIf they know your target companyYesHighHigh when coach knows the company

A coach with first-hand knowledge of your target company is more accurate than any tool. The constraint is time and cost — for international students managing 30+ applications, per-application coaching isn't viable. Applr fills the gap between templates and experts: structured ATS knowledge applied per-JD, in seconds.

§ 06By company

Polish your resume by company

Each company has its own ATS keywords, format preferences, and cultural signals. Pick your target for company-specific resume strategy.

§ 07Questions

What people ask before their first polish

What does "polish" mean — does it rewrite my resume from scratch?

Polish is an editor, not a generator. It takes what you already have — your existing bullets, projects, dates, and numbers — and rewrites the language and structure so they land better with ATS systems and match the specific JD you're targeting. Nothing gets invented. If you wrote "helped build a feature," it becomes "developed and shipped a feature that reduced load time by X%" — where X is a number you already told us. If you haven't provided a number, it asks you rather than making one up.

Will it change facts — add projects I didn't do, or inflate my numbers?

No. The AI is constrained to what you give it. It won't add projects, invent titles, change dates, or generate numbers out of thin air. This matters for international students in particular: fabricating experience on a resume used in visa-related job searches carries real legal risk. Applr's AI is an editor. Everything in the output came from you — reframed, not fabricated.

Can it tailor my resume to a specific job posting?

Yes, and that's the main reason it exists. Paste the JD alongside your resume and the AI weights keyword selection, bullet prioritization, and skills emphasis toward what that particular JD is actually asking for. If the JD mentions "distributed systems" four times, those words appear in the most relevant bullets — not stuffed, but embedded in the context of experience you actually have. You get a JD-match score before and after so you can see the delta.

How is this different from pasting my resume into ChatGPT?

Three things ChatGPT can't do here: (1) ATS-aware keyword extraction — ChatGPT doesn't know which keywords in a JD are actually parsed by applicant tracking systems versus which are marketing fluff. Applr's system distinguishes them. (2) JD-match scoring — ChatGPT has no structured way to score how well your resume matches a JD before and after editing. (3) No hallucination guardrails — ChatGPT will invent experience if you ask it to improve a weak bullet. Applr refuses to generate content that isn't grounded in what you've told it.

Will employers detect my resume as AI-written?

The content — your experience, your projects, your numbers, your timeline — is entirely yours. What AI polishes is the writing style: sentence structure, verb tense, active voice, keyword density. Recruiters aren't running AI detectors on resumes; they're looking for relevant keywords and clear impact statements. What they might notice is that your bullets are tighter and clearer than they used to be. That's the point.

Is there a free tier?

Yes. Your first resume polish is free — no credit card required. After that, paid plans start at $9/month. The free tier lets you run one full polish with JD-match scoring and a before/after diff, so you can evaluate the output before committing to anything.

Paste a JD, let it rewrite the rest

Every line rewritten against the posting, every word traced back to something you wrote. The first one is free.

Polish your resume free →Then practise the interview