A resume parser is software that automatically reads CVs and converts them into structured fields: name, contact details, work experience with periods, education, skills, languages, certificates. Classic parsers use rule-based approaches; modern solutions rely on LLMs and embeddings to handle unusual layouts.
Within an ATS the parser is a central component. It decides which information enters the database and thus indirectly whether an application moves further. Poorly parsable PDFs (scanned images, exotic fonts, multi-column layouts) cause data loss and therefore filter failures.
For candidates this means: clean structure, clear headings, standard formats and a text-based PDF improve the chances that all key information is captured. Own terminology should at least be linked once with industry-standard terms so the parser can attach to the semantic context.
Lunigi profiles are also processed in a structured way – a tidy CV pays off in semantic matching.