How ATS parsing reads your CV

An application form may fill in fields after you upload a CV. Check the result: a surname can appear in the wrong field, a job title can be missing or employment dates can transfer incorrectly. Compare the extracted information with your document and correct errors where the form permits it.

Parsing extracts information such as qualifications and employment history into structured fields. Research on resume parsing identifies differences in document structure, style and format as challenges for accurate extraction. Review the fields the application form displays, including entries that look complete at first glance.

An application can involve uploading a document, extracting information, completing required fields and reviewing the submission. Check the information at each step available to you. A readable uploaded file and accurate form fields give the employer a consistent account of your experience.

If you find an incorrect field, compare it with the CV and amend it where possible. Check the uploaded document in the preview if one is available. Follow the employer's submission instructions and look for a separate status message or email to establish the application outcome.

A portal that shows no decision gives you limited information about the employer's process. Record any visible error, status message or feedback. If a field transferred incorrectly, fix that field. Explaining a rejection would require evidence about the screening or review that followed.

A preparation tool's clarity or fit score reflects that tool's assessment. In VitAI, role-fit feedback helps you review your documents against the role. Use it to identify wording or examples that need attention. The feedback cannot establish an employer's score or predict whether you will receive an interview.

Before submitting, open the exported file and read it from top to bottom. Check that names, dates and section boundaries are clear. Review the file you will upload, since its layout may differ from the editor preview. Keep contact details and employment history easy to locate.

Use a clear reading order and familiar section labels, such as employment history and education. Place contact details in the main document. Check that the information remains understandable when decorative elements are removed. Choose a layout that lets a reader follow your experience in order.

The National Careers Service recommends clear headings, concise bullet points and checking spelling and grammar. Its CV guidance also emphasises relevant experience. Apply that guidance when editing your document: label each section and keep the examples that relate to the vacancy easy to find.

Use a file format and size accepted by the application form. Requirements vary between employers, so check the instructions for each submission. Compare editable fields with the original CV, especially names, dates, contact details and qualifications. Save the submitted version with your application record.

Use the advert's terms where they describe experience you can support. For example, put 'project coordination' beside an account of the project, the tasks you coordinated and the people involved. Remove terms that you cannot connect to work you performed.

Harvard's guidance on AI-assisted applications stresses accuracy and retaining ownership of the final document. Review each suggestion against your experience. Correct wording that changes your level of responsibility and remove references to tools you have never used. Keep only statements you can explain in an interview.

Before sending, check the document's reading order, the accuracy of the form fields and the relevance of your examples. Correct any inconsistencies between the file and the form. If the employer provides submission confirmation, save it alongside the document you sent.

The UK Information Commissioner's Office found that lower-scoring candidates sometimes received little or no substantive review. Managers relied on scores when deciding whom to progress or reject. Parsing supplies structured information; screening tools may evaluate that information. Both can form part of an employer's recruitment process.

The ICO gathered evidence from over 30 employers through voluntary engagement between March 2025 and January 2026. It found that many participating employers were likely making recruitment decisions without meaningful human involvement. The engagement had a different scope from an audit or investigation. Its findings concern those participants and cannot establish a worldwide rate of automated rejection.

Harvard Business School’s 2021 research with Accenture examined exclusion through hiring processes in the US, UK and Germany. Accenture was a commercial collaborator. OECD analysis explains that algorithmic screening can reduce some forms of bias or reproduce and amplify others, depending on how tools are designed and used.