Natural Language Processing is the branch of artificial intelligence concerned with enabling machines to work with human language at scale. NLP encompasses a range of tasks: information extraction (pulling specific data points from unstructured text), classification (categorising text by topic, sentiment, or intent), summarisation (generating concise representations of longer documents), question answering (retrieving specific information from a corpus in response to a query), and generation (producing new text that is coherent and contextually appropriate).
In the context of startup evaluation, NLP enables the core value proposition of AI-native pitch deck analysis: the ability to read a 40-slide deck and extract structured signals that would take a human analyst 30–45 minutes to process. Key NLP tasks in this pipeline include: named entity recognition (identifying company names, founder names, market sizes, revenue figures), claim extraction (identifying specific assertions made in the deck), and consistency checking (comparing claims made in different sections of the same document).
Modern large language models (GPT-4, Claude, Gemini) represent the current state of the art in NLP and have dramatically improved the quality of information extraction from pitch decks relative to earlier rule-based or smaller model approaches. Their ability to reason over ambiguous language, handle non-standard deck structures, and generate investor-grade commentary on specific sections has made AI-native pitch deck analysis genuinely useful rather than a parlour trick.