Free AI Research Starter Kit: 4 workflows + 4 promptsGet the Starter Kit →
Home / Tools / Job–Resume Matcher

Evidence-grounded matching / v3.1

Match Your Resume to a Job Using Evidence, Not Keyword Guessing

Upload or paste a job description and resume. The matcher converts each requirement and resume bullet into an auditable evidence vector. It compares demanded facets, actions, context, depth, and source strength—without inventing experience or treating adjacent tools as exact matches.

Demanded facetsDepth profilesDirectional transferRole-linked evidenceConfidence scoringHuman correction

Private by default

Job–Resume Evidence Matcher v3.1

Text stays in this browser
Step 01

Add the job advertisement

Include responsibilities, required qualifications, and preferred qualifications.

Upload job adPDF, DOCX, or TXT · 5 MB max

No file selected

Best results: 300–2,500 words0 words
Step 02

Add the resume

Use the version you would actually submit for this role.

Upload resumePDF, DOCX, or TXT · 5 MB max

No file selected

Scanned PDFs require manual paste0 words
What the score means: document readiness, not candidate quality or hiring probability.
Initial production scope: professional and knowledge-work roles. Clinical licensing, trades, public-safety, and clearance-heavy roles require manual verification.
File parsing: pasted text and TXT are dependency-free. PDF and DOCX require an internet connection to load pinned parser libraries; your document text remains in the browser.
Parsing requirements and roles, resolving demanded facets, scoring evidence depth, and building an auditable match map…

Document alignment and evidence map

!

Pending

    This is a document-based warning, not a prediction. "Not shown" does not necessarily mean "not qualified."

    Review

    Minimum experience, education, credentials, and eligibility conditions

    Evaluated before keyword fit
    Document alignment0%

    Required coverage0%

    Coverage of must-have qualifications.

    Depth alignment0%

    Autonomy, complexity, scope, repetition, and outcomes compared with the job expectation.

    Demanded-facet coverage0%

    Only facets actually requested by the employer are included in the denominator.

    Evidence depth0%

    Strength of role-linked evidence, weighted by source type, recency, scope, and outcomes.

    Employer language0%

    Accurate use of the employer's terminology where the evidence supports it.

    Analysis confidence0%

    Confidence in requirement extraction, document parsing, and evidence linkage.

    Requirement-level contribution ledger

    Every point links to source evidence

    Largest strengths

    Requirements contributing the most readiness points.

    Largest deductions

    Highest weighted points currently unavailable and why.

    Requirement-by-requirement comparison with demanded facets, depth profiles, and source confidence

    Human-in-the-loop: Open any requirement to confirm a defensible match, reject an incorrect inference, or ignore a requirement that is not relevant. Scores recalculate immediately.
    Manual corrections are active. Exported reports include them.
    01

    Wording gaps

    Relevant evidence exists but uses different terminology.

    02

    Depth gaps

    Intensity or cluster strength is below the job's requirement level.

    03

    Qualification gaps

    No defensible support found. Do not solve by inserting keywords.

    Demanded-facet breakdown

    Only employer-requested facets are scored. Green = direct evidence, blue = transferable evidence, gray = unsupported.

    Transferability connections used

    Directional same-family or adjacent skills that supplied limited—not exact—credit.

    Composite skill decompositions

    Multi-concept requirements broken into component skills.

    Unmapped dynamic phrases

    Repeated phrases in the job text not absorbed into the skill graph.

    Truth check: Revision impact is the maximum document-readiness points recoverable from existing evidence—not a prediction of ATS or hiring results. Add facts only when verifiable.

    Employer concepts, reference frequency, and evidence quality

    Repeated words do not automatically increase fit. Independent job references establish demand; role-linked accomplishments establish proof.

    Employer conceptJob demandResume proofFitRecommended action

    Requirements and evidence become auditable vectors—not keyword piles.

    v3.1 separates the exact facet an employer requests from related tools in the same family, then explains every score through job-demand frequency, resume-proof strength, depth, context, and employer language. Revision priorities consume the same requirement records, so every recommendation links back to a source sentence and a measurable deduction.

    1Parse

    Job clauses and resume bullets retain their source, role, date, action, context, and priority.

    2Resolve

    Demanded facets match first; semantic equivalents, same-family tools, and adjacent skills receive progressively less credit.

    3Profile

    Evidence is scored across exposure, autonomy, complexity, scope, outcomes, recency, and source strength.

    4Correct

    Every conclusion is source-linked and can be confirmed, rejected, or ignored before export.

    This tool improves documents. It does not evaluate people.

    No ATS simulation

    Employer systems, configurations, and human review practices differ.

    No rejection probability

    Warning flags only explicit filters. No prediction of employer decisions.

    No candidate ranking

    Designed for an applicant comparing their own documents.

    No invented experience

    Missing qualifications stay visible. Revision suggestions require truthful completion.

    Before you use the report

    A demanded facet is the specific sub-competency named by the employer. If the job requests AWS, only AWS is scored as the direct facet. Azure or GCP may receive limited transferable credit, but they cannot be labeled exact.

    The depth profile compares exposure, autonomy, complexity, scope, repetition, and outcomes. A resume can therefore show strong execution but still have a scope gap, or strong outcomes but insufficient evidence of ownership.

    The matcher distinguishes semantic equivalents, same-family tool transfer, and broader adjacent skills. Transfer is directional and capped. A related tool or neighboring competency may reduce a gap, but it never proves the exact requested skill.

    The job ad contains an explicit minimum or eligibility condition that the resume appears not to support or falls below. It is a prompt to verify the requirement—not a claim that an ATS or employer will reject the application.

    Supported files are read in the browser. TXT and pasted text work without an external parser. PDF and DOCX parsing requires an internet connection to load pinned PDF.js and Mammoth libraries; document text is still processed in the browser and is not uploaded or stored. Analytics events never contain document text, names, or filenames.

    Turn the match report into a truthful application workflow

    Sovereign OS adds the four core systems — Research, Studio, Content, and Multi-AI — including prompt design, document revision, and interview preparation workflows. One payment, permanent access.

    Free AI Research Starter Kit

    4 workflows + 4 reusable prompts

    Build better source-grounded AI workflows beyond one application. Free, instant access.

    No spam. Unsubscribe anytime.
    Your next step

    Continue the job-search workflow

    Need the complete professional workflow library? Explore Sovereign OS →

    Trust layer

    Privacy and responsible AI use

    Resume and job-posting text can contain personal or confidential information. Remove unnecessary identifiers and use approved environments for sensitive employment records.