Scan CV bullets, resume summaries, cover letters, and LinkedIn About sections before ATS systems and hiring managers see them. Sentence-level highlights flag the ChatGPT-rewritten lines, so you can replace them in your own voice and keep the polish underneath. Built for solo job seekers running a search, not for HR teams. Free to try. No card.
Solo job seekers running a search across two-page corporate resumes, longer academic and executive CVs, cover letters, LinkedIn About sections, and portfolio descriptions. One person doing the writing, often a fresh search after a layoff or a planned move, mixing AI assistance with their own voice.
A job search in 2026 is a writing project as much as it is a networking project. The resume gets read by Workday or Greenhouse before any human sees it, the cover letter gets opened by a hiring manager who already has thirty other applications in the queue, and the LinkedIn profile gets skimmed by a recruiter inside a saved-search alert. Every surface is short, every surface is read fast, and every surface now competes against candidates who use the same AI tools you do.
The two-page corporate resume and the longer academic or executive CV share the same writing risk: bullets that all read alike, summaries that lift the same opening line, and accomplishment statements that compress real work into ChatGPT phrasing. Scan the document and the highlights show which bullets to rewrite first.
Cover letters are the most personal genre in the packet and where AI flavour hurts the most. LinkedIn About sections are the second most read surface in a recruiter pipeline and the one most candidates leave on autopilot. Both benefit from the same workflow: scan, rewrite the flagged sentences, keep the personal voice underneath.
Designers, product managers, engineers, and writers all run portfolios that include short prose descriptions of each project. Hiring managers read these descriptions before opening the work. Generic AI-flavoured project blurbs reduce the click-through rate to the actual work, and the writing gets judged before the work does.
Through 2025 the major applicant tracking systems added AI-content signals to their candidate review layers. The flag does not always auto-reject, but it does land the application in a lower review tier where a recruiter spends less time on the document before triaging.
Workday added a writing-quality flag on the recruiter view in early 2025 and expanded it through the year. Greenhouse and Lever both surface third-party AI scores through their integration partners, which most enterprise customers have switched on. iCIMS does similar at the enterprise tier. The flag does not always mean a rejection, but it does mean the application enters a lower review tier where a recruiter spends ninety seconds instead of three minutes on the document.
ATS-side detection looks at sentence rhythm, action-verb vocabulary, and the templated opening summary that ChatGPT defaults to. The signals overlap with what hiring managers themselves notice once they have read enough applications, so the ATS flag is increasingly aligned with the human read. Pre-scanning your bullets and rewriting the flagged ones is the workflow that keeps you out of the lower tier.
Senior candidates and executives applying for VP and above roles get a closer human read, which means the templated-summary opening is even more visible in that pool. Retained search firms screening executive CVs spend ten to twenty minutes per document and notice voice issues fast. The fix is the same on every level of seniority: scan, rewrite the flagged sentences, keep the actual experience underneath.
Most job seekers complete a full search on Free or Starter. Pro is the right fit if you are also rewriting LinkedIn and a stack of cover letters across a long search. Business is built for hiring teams, not for individuals. Full details on the pricing page.
Billed $89.88/year — Save $30
Billed $179.88/year — Save $60
Billed $359.88/year — Save $120
Yearly billing saves 25%. View full pricing →
CV and resume bullets, summary paragraphs, cover letters, LinkedIn About sections, and portfolio descriptions. Each genre has its own register and its own AI-flavour failure mode. The scan workflow stays the same across all five.
The single accomplishment line under a role. Ten to twenty words, action verb up front, a number if there is one. AI rewriters compress real work into a small set of action verbs and lose the specific texture. The fix is to keep the AI-suggested structure but swap the verb and add the actual detail the role required.
The three-to-four-line opening summary is the most templated surface on a resume. Every candidate uses one and most candidates run it through ChatGPT. The result is hundreds of summaries that read alike on a recruiter's screen. Scan the summary on its own and rewrite the lines that flag. A summary that sounds like you raises the read-rate on the rest of the document.
The most personal genre in the packet. Three to five short paragraphs of motivation and connection to the role. AI flavour hurts the most here because the cover letter is the one place a hiring manager expects to hear your actual voice. The opener and the closing paragraph carry most of the risk, and the middle paragraphs about your fit for the role are where the templated language stacks up.
The first paragraph of your LinkedIn profile, read by every recruiter who lands on your page. Most candidates leave this on default or copy the resume summary into it. A distinct About section drafted in your own voice raises the inbound-message rate from recruiters running saved searches in your category.
For designers, PMs, engineers, and writers running a portfolio site, the short prose under each project entry is read before the work itself. A generic AI-flavoured blurb reduces click-through to the actual project. Scan each description and keep the voice consistent across the portfolio.
AI-rewritten resumes tend to use the same small set of action verbs across every bullet, regardless of what the role actually involved. Drove, spearheaded, leveraged, optimized, orchestrated, championed, streamlined. The narrow vocabulary is one of the clearest tells on a resume, and the easiest to fix.
ChatGPT and similar models were trained on resume samples that themselves used a narrow set of action verbs, and the model defaults to that pool when generating resume bullets. Run a hundred different bullets through any general AI rewriter and the verb distribution collapses onto roughly twenty words. A real writer with actual experience varies the verb across the role: shipped, debugged, hired, escalated, killed a feature, rebuilt a system, lost a deal, recovered an account. The vocabulary stretches because the work is specific.
The scan highlights bullets where the verb-and-structure pattern reads as templated. The fix is not to remove the structure but to swap the verb for one that fits the actual work and add the concrete detail that the AI compressed out. Bullets that score clean after a rewrite read as both polished and specific, which is the combination that lands interview invitations.
A common piece of resume advice is to add numbers to every bullet to make it specific. The advice is correct but not sufficient. A bullet that reads "spearheaded a 40 percent improvement in pipeline velocity" still reads templated even with the number, because the verb and the framing are the AI signal. Swap "spearheaded" for the specific verb that describes what you actually did, then keep the number.
A hiring manager reading the seventh application of the morning notices the third identical opening summary in a row. The reaction is not always conscious, but the interview-invitation rate drops on resumes that read templated. Authentic voice raises the call-back rate even when the underlying experience is identical.
The same opening line across applicants. The same three action verbs under every role. The same rhythm of bullet length. The hiring manager does not always articulate the pattern, but the read is the same: low effort, low investment in the application, possibly low investment in the role itself. The cover letter that follows compounds the impression if it reads templated as well.
Reply rates and interview invitations drop on resumes that read fully AI-rewritten, even when the candidate experience is strong. The signal the hiring manager takes from a templated resume is that the candidate did not invest the time to write their own materials. Rebuilding the voice in three or four key bullets and the cover letter opener restores the call-back rate without changing the underlying experience.
For senior and executive roles the review is higher, the document is longer, and the patterns are subtler. Retained search consultants reviewing executive CVs notice voice issues fast, because they read enough CVs in the candidate pool to calibrate against the templated baseline. Executive search firms are explicit about looking for genuine voice on the document and in the first conversation, because the role pays for judgement and judgement shows up in writing.
AI as an outline tool, a brainstorming partner, a grammar pass, or a way to surface missing accomplishments is fine and most candidates use it that way. The point is that the final document sounds like you, not like the model. The scan tells you which sentences still need that rewrite before submission.
Write your own draft of the resume and the cover letter first, in your own voice and from your own notes. Run the draft through AI for grammar, structure feedback, or to surface accomplishments you might have left out. Scan the result. The flagged sentences are where the AI pass over-rewrote and pulled the voice towards the templated baseline. Rewrite those sentences in your own register and resubmit.
Not a guarantee of being hired. A clean Authenticity Score on the resume does not get you the interview on its own, and the resume is one input among several into the hiring decision. What the scan does is remove the avoidable downside of a templated AI flavour landing your application in a lower review tier before a human reads the actual experience.
A hiring manager spends ninety seconds on a templated application and three minutes on one that reads as written by the candidate. The extra ninety seconds is where the experience gets read, the questions get formed, and the interview invitation happens. The scan-and-rewrite cycle is what shifts you from one read-mode to the other.
The other side of the table: ATS integration, audit log, EEOC-aware framing for hiring teams.
For recruiters →Applications, essays, and case writing for MBA candidates running an admissions or job search.
For MBA students →Rewrite flagged resume bullets and cover letter passages in a more authentic voice.
Use the AI rewriter →Free, Starter, Pro, Business. Yearly billing saves 25%. Most job seekers run on Free or Starter.
See pricing →Free to try. No card. Most job seekers run a full search on Free or Starter, with the Chrome extension for scanning inside LinkedIn and Gmail.