Natural Language Processing (NLP)

Natural language processing (NLP) is a branch of AI that enables computers to read, understand, and generate human language, forming the technical foundation of AI assistants, chatbots, search engines, and document analysis tools. When you type a question into Claude or ask a voice assistant for the weather, NLP is the layer that converts your words into something a computer can interpret and respond to.

What does NLP do?

NLP breaks human language into structured information that software can work with, handling tasks like intent detection, entity extraction, sentiment analysis, and language generation. Key NLP tasks that appear in business tools:

• Intent classification: determining what a user wants to do from their message (“book a meeting” vs. “cancel a subscription”)
• Entity extraction: identifying specific pieces of information in text (dates, company names, dollar amounts, product references)
• Sentiment analysis: determining whether text is positive, negative, or neutral — used in customer feedback tools and review monitoring
• Summarization: condensing a long document into key points — used in CRM tools, email assistants, and meeting note tools
• Translation: converting text between languages — built into most major productivity suites

According to Stanford’s 2025 AI Index, NLP-related AI deployments in business grew 3.5x between 2022 and 2024, driven primarily by large language model adoption across customer service, document processing, and content generation.

How does NLP power the AI tools businesses use daily?

Most AI features inside business software rely on NLP to function — from the spam filter in your inbox to the AI that summarizes documents in Notion to the lead scoring model in your CRM.

Specific examples in tools Canadian SMBs commonly use:

• Claude and ChatGPT: entirely NLP-based — language understanding and generation are the core function
• HubSpot Breeze AI: uses NLP to classify emails, summarize deal notes, and generate follow-up drafts
• Notion AI: applies NLP to summarize long documents, autofill database fields, and answer questions about page content
• n8n AI nodes: use NLP models via API to classify, extract, or transform unstructured text as a workflow step
• Gmail Smart Reply and Compose: NLP generates short reply suggestions and drafts

What is the difference between NLP and large language models?

NLP is the field; large language models (LLMs) are the current dominant technology within that field. Before LLMs, NLP relied on separate specialized models for each task: one model for sentiment analysis, a different one for translation, another for summarization. LLMs replaced most of these specialized models with a single architecture that handles all language tasks by learning from enormous volumes of human text.

For practical purposes, the distinction matters mostly for developers building AI systems. For business users, NLP is what makes AI tools understand what you mean and respond coherently — and LLMs are what made that capability accessible, accurate, and fast enough for everyday use.