AI Resume Humanizer
Turn your real experience into clear CV bullets. Choose your target role and supply achievements without inventing qualifications or figures. Try the same tool available in your dashboard, then carry your input, options and result into your account.
Tailor your application using your actual experience, achievements and qualifications.
Add at least 10 words to your letter or cv section.
Nothing is invented: every date, title and figure comes back exactly as you supplied it.
What a recruiter recognises, in about four seconds.
Every one of these was good advice once. Universal adoption is what turned them into signals.
- The same eight action verbs everybody usesSpearheaded, orchestrated, leveraged, streamlined, championed, drove, executed, delivered. Every model reaches for these because every CV guide recommends them, and a recruiter reading two hundred applications sees the same eight words in the same positions. They are not wrong; they have simply stopped carrying information.
- Achievements with no number in themSignificantly improved efficiency. Substantially increased revenue. These read as achievements and contain nothing checkable. A model writes them because it does not know your numbers, and the gap between that and a real bullet is the entire difference between a CV that works and one that does not.
- A profile summary that could head anybody's CVResults-driven professional with a proven track record of delivering value in fast-paced environments. Interchangeable across industries, seniority and decades. If the top four lines of your CV would suit a different applicant unchanged, they are spending the most valuable space on the page saying nothing.
- Every bullet the same lengthModel output settles at a middle length and holds it, so a generated CV has six bullets of near-identical shape per role. Real experience is lopsided — one thing you did mattered far more than the others and deserves two lines while the rest get one.
- Skills lists assembled from the job postingTailoring is right and copying the posting's vocabulary wholesale is visible. A skills section listing every technology in the advertisement, including ones absent from your experience, is the pattern recruiters check first, because it is what an interview immediately exposes.
The thing that fixes a CV is the thing a rewrite cannot supply.
Worth putting plainly rather than discovering after you have used this. Everything above is fixable by editing. The thing that actually decides whether your application progresses is not.
A strong bullet contains something checkable. A number, a name, a constraint, a result. “Reduced deployment time from forty minutes to four” works because it is specific and defensible. “Streamlined deployment processes to drive efficiency” is the same sentence with the information removed.
A model does not know your numbers. That is why it writes the second version, and it is why asking one to strengthen a bullet is dangerous — most will supply a plausible percentage rather than admit they cannot. Those invented figures read exactly like real ones and you will be asked about them in an interview.
So the work is recall, not writing. For each role, what actually changed because you were there. How many, how long, compared to what, instead of what. Most people find this genuinely hard and it is the only part that matters, because it is the part nobody else can put on your CV.
And if there is no number, there is usually still a specific. The name of the system, the size of the team, the thing that was broken when you arrived. Specific beats quantified beats vague, and vague is where a generated CV lives.
What applicant tracking systems actually reject.
Most advice about this is years out of date and it makes people do strange things to documents that would have parsed fine. The short version: modern systems read ordinary documents well, and what still breaks them is layout rather than wording.
What causes real problems. Multi-column layouts, where the parser reads across the columns and interleaves two roles into nonsense. Tables and text boxes, whose contents are often skipped entirely. Contact details in a header or footer, which several systems do not read. Text stored as an image, which is invisible. Unusual section headings, where “Where I’ve Been” parses worse than “Experience”.
What does not. Bold text, bullet points, a PDF rather than a Word file, a second page, a sensible font. Standard formatting is fine and has been for years.
Keyword matching is real and cruder than people fear. Systems do screen on terms from the posting, which is a reason to use the employer’s vocabulary where it genuinely describes your work — if they say Kubernetes and you wrote container orchestration, say Kubernetes. It is not a reason to list technologies you have not used, because the interview arrives before the job does.
Nothing here fights an ATS. A single-column document with plain headings and honest terminology parses reliably everywhere, and that is the whole requirement.
CVs drafted with ChatGPT, Gemini, DeepSeek, LLaMA and Perplexity.
ChatGPT resume humanizer
The source of most AI-written CVs, and the most recognisable to anyone hiring.
It produces the textbook CV: the results-driven summary, the eight action verbs, six evenly weighted bullets per role. As a template it is correct, and correct templates are what everybody now submits, which means executing the form perfectly no longer distinguishes an application.
Its specific danger is invention. Asked to strengthen a bullet, ChatGPT supplies plausible numbers — a percentage improvement, a team size, a budget figure — that read exactly like the ones you provided. Anything quantified in a CV has to be a number you can defend in an interview.
Gemini resume humanizer
Its structural instinct suits a CV better than most writing.
A CV is genuinely a structured document, so Gemini's preference for headings and bullets is closer to right here than in prose. Formatting is where it goes wrong instead: tables, columns and text boxes that look tidy and parse badly in applicant tracking systems.
If a Gemini draft arrives with a two-column layout or a skills matrix in a table, rebuild it as a single column of plain headings and bullets before submitting anywhere.
DeepSeek resume humanizer
Strong on technical detail and needs its opening checked.
For engineering and research CVs its precision is an advantage: it describes what a system did accurately, without the vague impact language that weakens most technical bullets.
Reasoning-model output also sometimes carries a preamble discussing how to approach the CV. On a document a recruiter opens in a stack of two hundred, that is fatal in a way it is not in a draft nobody sees before you fix it. Check the top of the file.
LLaMA resume humanizer
The case to read line by line, because errors here are expensive.
LLaMA is weights rather than a product, so quality varies with the deployment. Smaller ones drift on specifics — a date that shifts, a job title that changes between the summary and the history, a company name spelled two ways.
An internal inconsistency on a CV reads as carelessness at best and as dishonesty at worst, and neither survives a screening call. Check dates and titles against your own record rather than trusting the draft.
Perplexity resume humanizer
Genuinely useful for the research, wrong for the document.
Because it searches, it can tell you what a role usually involves, what a company has said publicly, and what the standard terminology is in a field you are moving into. That is real value in a job search and it is research rather than writing.
What it should not do is draft the CV, since it can introduce descriptions of responsibilities from job postings it found rather than from your experience. A CV describing a role you did not quite have is a problem an interview surfaces immediately.
AI resume questions.
Do recruiters use AI detectors on CVs?
Rarely, and it is the wrong thing to worry about. A recruiter reading two hundred applications recognises a generated CV in seconds from the shape — the same summary, the same eight verbs, evenly weighted bullets with no numbers in them. That filter is faster and less forgiving than any software, and it is the one that decides your application.
So what actually fixes an AI-written CV?
Specifics, and a rewrite cannot supply them. What separates a strong bullet from a generated one is a number, a name, a constraint, a result you can defend in an interview. This tool cuts the AI-sounding phrasing and varies the shape; the facts have to come from you, which is why the section on what it cannot do is on this page.
Will it invent achievements or numbers?
No. Every figure, date, job title, company and technology comes through exactly as you wrote it. Worth knowing that drafting directly in an assistant is different: asked to strengthen a bullet, most will supply a plausible percentage or team size, and those read exactly like real ones. Anything quantified on a CV has to be defensible in an interview.
Does this break applicant tracking systems?
No, and the ATS advice circulating online is mostly out of date. Modern systems parse ordinary documents fine. What still breaks them is layout rather than language: multi-column designs, tables, text boxes, graphics, headers and footers containing your contact details, and text stored as an image. A single-column document with plain headings parses reliably everywhere.
Should I tailor my CV to each job posting?
Yes, and there is a line. Reordering your experience so the relevant work comes first, and using the employer's vocabulary where it genuinely describes what you did, is what tailoring means. Copying every technology from the advertisement into a skills list including ones you have not used is the pattern recruiters check first, and an interview exposes it in one question.
Is using AI on a CV dishonest?
Using it to write more clearly is editing, and most people already had help from a template or a friend. The line is factual: the experience, the numbers and the dates have to be true and defensible. A CV whose claims you cannot substantiate in conversation is a problem regardless of what produced the sentences.
Can it humanize CVs drafted with ChatGPT or Gemini?
Yes, and the sections above cover what each leaves behind. ChatGPT produces the textbook CV and supplies plausible invented numbers; Gemini's formatting instinct creates tables and columns that parse badly; DeepSeek sometimes leaves a planning preamble at the top of the file.
How long should a CV be?
One page for early career, two for most people, longer only in academia and some technical fields where publications or projects are expected. AI drafts run long because models pad, and the second page of a padded CV is usually where the material that would have won you the interview gets pushed out of sight.
What about the profile summary at the top?
Either make it specific or delete it. Four lines of results-driven professional language occupy the most valuable space on the page and say nothing. A summary naming what you actually do, at what scale, in which domain, earns that space. If you cannot write that, the space is better spent on your most recent role.
Is my CV stored?
The document is attached to your account so you can return to it, and you can delete it whenever you like. It is not used to train anything and it is never sent to any employer.