Not all AI resume parsers share the same architecture — and the gap in outcomes is significant. Rule-based extractors eliminate chaos but miss context. NLP engines understand language but need ML to predict fit. Semantic ontology layers close the synonym gap. Integration pipelines turn parsed data into action. The parsers that win combine all four layers in sequence, not as standalone modules.

AI parsers don't keyword-match — they build semantic maps. A job description that fails to use consistent terminology, clear section headers, and standardized skill language feeds noise into the model. The result is mismatched candidates, inflated time-to-hire, and automation that produces worse outcomes than manual screening. Fix the source document first.