AI resume checker with a recruiter's verdict

How the AI resume checker scores a resume

Most resume checkers either count keywords, as an applicant tracking system's search does, or ask a chat model for an opinion that reads well and changes when you ask twice. This one does both, separately, and says which number came from where.

Keywords, matched the way a search box matches

The checker pulls the terms a posting would be filtered on (tools, certifications, named methods, short phrases from its requirements) and looks for each one in your resume, word for word. Requirements count double; the “About us” paragraph and the benefits list are skipped, because nobody screens on them. Matching is literal on purpose: a recruiter who types “SQL” into their ATS does not get back a resume that only says “PostgreSQL”. Plurals are folded and true abbreviations count, so “K8s” finds Kubernetes.

Five judgments a recruiter would make

The resume and the posting then go to Jev, a judgment model from TypeSafe AI that answers typed questions with calibrated probabilities instead of generating text. Five questions, one call: how closely your experience matches the core responsibilities, how many required skills you clearly demonstrate, whether your level fits, how well your bullets show measurable results, and whether a recruiter screening for this job would move you to interview.

Format checks a parser needs

Last come plain rules that need no model: an email address and phone number in the body text, standard section headings (Experience, Education, Skills), dates on each role, a sensible length, achievement bullets and how many carry a number, and icon-font glyphs that some parsers turn into question marks. It takes a PDF, a DOCX or plain text up to 4 MB (export .doc or Pages files to PDF), and a PDF gets one more check: if it has no text layer, you find out here, before an employer's system reads it as a blank page.

The match score is 30% keyword coverage, 40% experience match and 30% skills. Impact and format are shown beside it, not inside it: they describe the resume rather than the fit, and a well-written resume can still be wrong for the job. Read 80 and above as a strong match, and under 40 as a sign the posting wants something the resume does not show.

We tested it before it shipped

On 26 September 2026 we ran a strong backend-engineering resume and a deliberately vague version of it against three postings. Scores are on the 0–4 rubric; the last column is the probability of an interview.

The same resume went from a 90% chance of interview to 2% when the job changed, and to 11% for a role two levels above it. Rewriting the bullets vaguely — same jobs, no numbers — dropped the skills score from 3.85 to 0.42, because a skill you do not demonstrate is a skill the reader cannot see.

Ask Jagent follow-up questions

Under every report is a conversation: ask any yes-or-no question about the resume and get a probability back, not a paragraph. “Does this resume show leadership experience?” came back 0.93 for the strong resume, which mentions mentoring three engineers, and 0.21 for the vague one. PCI DSS experience, never mentioned, got 0.10.

The agent judges; it does not write. “Rewrite my summary” still gets a number from the model, 0.79 when we tried, and that number means nothing, so the checker declines instead of charging for it. To improve a bullet, write two versions and use Compare.

Compare two versions of a bullet

Compare asks which version a hiring manager for this job would find more compelling, twice, with the order swapped, and averages the answers. On a close pair, the version listed second won 0.90 to 0.10; reversed, the other version edged it 0.53 to 0.47. Jev, like most judges, leans towards the last option it read, so asking both ways cancels the lean, and when the orderings disagree you are told it is too close to call.

How to act on the report

• Add missing keywords only where they are true. A term you cannot discuss in an interview costs more than it gains.
• Put the posting's exact job title in your headline or summary if it describes you; titles are the first thing recruiters search.
• Quantify at least half the bullets: scale, money, time saved, percentages.
• Send a text-based PDF or a DOCX, and check the uploaded text here: if lines come out scrambled, a parser scrambles them too.
• Run the check again after editing; each run judges the new text afresh.

What an applicant tracking system does with your resume

An ATS is the database a hiring team works from. When you apply, it parses your file into fields — contact details, jobs, dates, education, skills — and recruiters search and filter them by title, skill, years of experience and knockout questions such as work authorisation. None of that reads your resume the way a person does, so this resume checker keeps keyword matching and judgment as separate numbers: the first decides whether a search finds you, the second whether the person who opens the file wants to talk to you. No checker can see one company's private filters, so aim for a file that parses cleanly and reads well everywhere.

Credits and privacy

Three runs need no account; a check and a follow-up each count as one. After that, sign in: accounts get a monthly credit allowance, and top-up packs are one-time payments that never expire. A credit is 1,000 input tokens, so a typical check costs 2 credits and a follow-up usually 1.

Questions