One structured career record.
Turn PDF and DOCX resumes into a canonical profile that preserves the experience, education, skills, and evidence every later step can reference.
Career intelligence · Evidence before polish
The right language. The same experience.
Tailor the application. Keep every claim true.
Turn a resume and job description into a more relevant application without letting the model rewrite reality. Every tailored bullet starts with source evidence and passes a deterministic claim check before it reaches the final document.
Intended user or buyer
For candidates and talent-support teams preparing role-specific application materials.
Designed for people who want a more relevant resume and cover letter while keeping employers, titles, tools, dates, outcomes, and metrics grounded in the source career record.
Job candidatesCareer coachesWorkforce programsTalent mobility teams
Workflow problem
Application tailoring creates pressure to sound more relevant without changing the facts.
Candidates must interpret a job description, identify the strongest evidence, rewrite clearly, and assemble multiple documents while preventing unsupported claims from entering the application.
A controlled application workflowAlignment at every layer. Evidence underneath it all.
The portal treats a career history as a factual record, then uses AI where it is strongest: finding relevance, improving language, and assembling a coherent application around evidence that already exists.
Product interface preview
A realistic view of the work in progress.
The illustrative workspace uses representative content to show how evidence, state, and review decisions stay together.
Target roleProduct Operations
Claims verifiedSafe tailoring review
Original bullet
Partnered with operations leaders to build weekly SQL reports.
Tailored bullet
Built weekly SQL reports in partnership with operations leaders.
Employer preservedTitle preservedTools sourcedNo metric added
Claim-aware application workflowIllustrative workflow
Customer outcomes
What changes for the people doing the work.
Tailor the application. Keep every claim true.
Requirements with real weight.
Extract the capabilities a job actually asks for, assign explicit importance, and compare them with deterministic ATS-style subscores.
Sharper wording, checked claims.
Let the model rewrite existing bullets for relevance, then run deterministic verification before any tailored language moves forward.
Core workflow
One run. Four evidence-aware stages.
The application moves from source review through final documents while keeping fit evidence, versions, and outputs available for inspection.
Parse the evidence
Read a PDF or DOCX resume and build the canonical profile that becomes the factual boundary for the run.
Read the role
Extract and weight the job requirements, then calculate deterministic ATS-style subscores against the profile.
Propose, then verify
Use the model to rephrase relevant existing bullets and deterministically reject wording that introduces unsupported claims.
Render and preserve
Produce the resume and evidence-checked cover letter in PDF or DOCX while retaining run status, timing, scores, versions, and downloads.
Capabilities
From source resume to reviewable application kit.
Source review, role matching, tailoring, claim checking, and document preparation work as one traceable experience rather than disconnected prompts.
Canonical resume parsing
Normalize PDF and DOCX resumes into one structured profile instead of asking each downstream step to reinterpret the source.
Weighted requirement extraction
Identify role requirements and their relative importance so tailoring focuses on meaningful alignment rather than keyword volume.
Deterministic fit signals
Score the profile against the role with inspectable ATS-style subscores that remain consistent throughout the product workflow.
Claim-verified tailoring
Rewrite only existing resume evidence, then check the proposed bullet against the canonical profile before accepting it.
Evidence-checked documents
Generate a tailored resume and cover letter without adding unsupported career history, achievements, or personal context.
Review history that stays together
Keep the source profile, role requirements, proposed rewrites, verification results, fit review, and generated files together.
Example product output
The model can improve the language. It cannot change the history.
Proposed rewrites are checked against the canonical profile. Unsupported details are treated as violations—not creative improvements.
- Employers, titles, dates, and tools remain source-grounded.
- Metrics, team sizes, and outcomes are never added without resume evidence.
- Company praise, mutual connections, and personal backstory are not invented.
- A failed claim check blocks unsafe wording before rendering.
Verification ledgerTailored bullet · Review 01
PassEmployerAtlas RetailUnchanged
RoleOperations analystUnchanged
ToolSQLFound in source
MetricNone introducedClear
OutcomeNo new claimClear
Every accepted phrase must map back to the canonical profile.
AI Career Portal · NeoTechSource