One-Line Definition
Natural Language Processing (NLP) is a branch of Artificial Intelligence (AI) that enables computers to understand, interpret, analyse, and generate human language. It allows software to work with written text and spoken language in a way that feels natural to people, powering applications such as chatbots, search engines, virtual assistants, translation tools, and document analysis platforms.
Businesses generate enormous volumes of unstructured information every day.
Emails, customer support tickets, contracts, meeting notes, social media conversations, product reviews, and internal documents all contain valuable business insights, but manually analysing this information is time-consuming and expensive.
Natural Language Processing enables organisations to automatically understand and process human language at scale.
Rather than treating text as simple words, NLP identifies meaning, context, relationships, sentiment, and intent, allowing businesses to extract actionable insights from large collections of data.
Modern AI assistants, enterprise search platforms, and conversational AI solutions rely heavily on NLP. It helps organisations improve customer experiences, automate repetitive work, accelerate decision-making, and make better use of their knowledge assets.
As Generative AI and Large Language Models continue to evolve, NLP remains one of the foundational technologies that enables AI to communicate naturally with people.
Natural Language Processing combines computational linguistics with machine learning and deep learning techniques to help computers understand language in a way that resembles human communication.
A typical NLP workflow involves several stages.
The system receives text or speech from a user.
Examples include:
The NLP system breaks the content into words, sentences, and grammatical structures while identifying relationships between them.
It determines:
Rather than matching exact keywords, modern NLP understands how words relate to one another within a sentence.
Using machine learning models, the system interprets the meaning of the input.
For example, if a customer writes:
“My order hasn’t arrived yet and I’m disappointed.”
The NLP engine identifies:
This enables downstream systems to take appropriate action automatically.
Once the information has been understood, the AI generates an appropriate response.
This could include:
Modern enterprise AI combines NLP with Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) to generate responses that are conversational, context-aware, and based on trusted business knowledge.
NLP allows software to understand everyday language instead of relying on rigid commands or keyword matching.
Businesses can provide faster, more accurate responses through AI-powered chatbots, virtual assistants, and customer service platforms.
NLP can analyse contracts, invoices, reports, policies, emails, and other business documents in seconds.
By analysing customer feedback, surveys, reviews, and conversations, NLP helps organisations identify trends, recurring issues, and opportunities for improvement.
Rather than searching for exact keywords, NLP understands user intent and returns more relevant search results across business knowledge bases.
Modern NLP solutions can translate content, understand multiple languages, and enable organisations to communicate effectively with global customers.
Natural Language Processing powers many AI applications used every day, including:
These applications help organisations automate knowledge-intensive tasks while improving efficiency and accuracy.
Imagine an insurance company receiving thousands of customer emails every week.
Without NLP, support teams would need to manually read each email, identify the issue, categorise the request, and assign it to the appropriate department.
With Natural Language Processing, the system automatically analyses each email, recognises the customer’s intent, detects urgency, identifies policy numbers or claim references, and routes the request to the correct team.
If the enquiry is straightforward, an AI assistant can even generate a personalised response instantly, reducing response times from hours to seconds while improving customer satisfaction.
At Evangelist Apps, we use Natural Language Processing as a core capability within our enterprise AI solutions.
We build intelligent applications that understand business language, interpret customer queries, analyse documents, and automate knowledge-intensive workflows.
Our NLP-powered solutions are integrated with Large Language Models (LLMs), Retrieval-Augmented Generation (RAGRetrieval-Augmented Generation (RAG) is an AI architecture that combines a large language model (LLM) with an external knowledge source, allowing AI systems to retrieve relevant information before generating a response. This approach helps deliver answers that are more accurate, up-to-date, and grounded in trusted business data. More), Microsoft Copilot, Dynamics 365, APIs, and enterprise knowledge repositories to deliver accurate, context-aware AI experiences.
Whether it’s developing AI-powered customer support assistants, intelligent document search platforms, enterprise knowledge portals, or workflow automation solutions, we use NLP to help organisations unlock the value hidden within their business data.
Our focus is on creating AI solutions that improve productivity, enhance decision-making, and deliver measurable business outcomes.
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Although often used together, NLP and LLMs are not the same thing.
In simple terms:
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