Ticket and email routing
Classifying requests by type, urgency and team.
Technology / Generative and Language
Natural Language Processing
Natural language processing lets software work with human language: reading, sorting, searching, extracting and generating text. It powers everything from ticket routing to semantic search, and has been transformed by large language models.
What is Natural Language Processing?
Short answer
Natural language processing (NLP) is a field of artificial intelligence that enables computers to process, analyse, understand and generate human language in text or speech, covering tasks such as classification, named entity recognition, information extraction, sentiment analysis, semantic search, summarisation, translation and question answering.
NLP has progressed from rule-based pattern matching, through statistical methods, to deep learning and transformers. Pre-trained language models learn rich representations of meaning, and large language models can perform many NLP tasks from instructions alone.
Practical NLP still involves choices. Compact transformer models fine-tuned for a task can be faster and cheaper at high volume. Large language models handle complex, varied tasks with little training data. Search systems combine keyword and semantic retrieval. In India, multilingual and code-mixed text adds specific requirements for model selection and testing.
Related concepts
- LLMs
- Embeddings
- Semantic search
- Entity extraction
- Speech to text
- Indic languages
- Generative AI
How it works
How does Natural Language Processing work?
-
Text processing
Text is cleaned and split into tokens the model can process.
-
Representation
Models convert tokens into numerical representations capturing meaning and context.
-
Task modelling
A classifier, extractor, retriever or generator is applied to those representations.
-
Output handling
Results such as labels, entities or answers are validated and formatted.
-
Integration
Outputs update systems, route work, feed analytics or respond to users.
Applications
Where is Natural Language Processing useful?
Common applications across industries. Suitability always depends on the specific data, process and constraints.
Feedback analysis
Identifying topics and sentiment in reviews and surveys.
Semantic search
Finding documents by meaning, not only keywords.
Information extraction
Pulling names, dates, amounts and clauses from text.
Call and chat analytics
Analysing transcripts for topics, quality and compliance.
Multilingual services
Understanding and responding across Indian and international languages.
Limitations
Limitations and when not to use it
Every technology has boundaries. Knowing them early avoids expensive disappointment.
- Sarcasm, ambiguity and domain jargon can confuse models.
- Low-resource languages and dialects may have weaker support.
- Code-mixed text such as Hinglish needs specific testing.
- Categories and language use change over time.
- Sensitive personal information in text requires careful handling.
Common misconceptions
LLMs make traditional NLP obsolete.
Smaller, specialised models remain cost-effective and controllable for many high-volume tasks.
Sentiment analysis is always accurate.
Sentiment depends heavily on context and domain; evaluation on your data is needed.
NLP only works in English.
Multilingual models support many languages, though quality varies and should be tested.
Comparison
NLP approaches compared
Different approaches suit different volumes, accuracy needs and budgets.
| Approach | Strengths | Limitations | Good fit |
|---|---|---|---|
| Rules and patterns | Precise, transparent, cheap | Brittle with varied language | Fixed formats, IDs, simple patterns |
| Fine-tuned compact models | Fast, accurate for defined tasks, low cost | Need labelled data, less flexible | High-volume classification or extraction |
| Large language models | Flexible, few examples needed, handles complexity | Higher cost and latency, variable output | Complex or low-volume varied tasks |
| Hybrid pipelines | Balance cost, accuracy and control | More engineering effort | Production systems at scale |
Decision guide
How to evaluate Natural Language Processing for your organisation
Questions worth answering before investing. They form the basis of our feasibility and proof-of-concept work.
| Question to ask | Why it matters |
|---|---|
| What languages and writing styles are involved? | Model support and accuracy vary by language and informality. |
| What volume and speed are required? | Determines whether compact models or LLMs are practical. |
| Are labelled examples available? | Affects the choice between fine-tuning and prompting approaches. |
| How costly is a misclassification? | Guides thresholds and human review. |
| Where will outputs be used? | Integration determines value delivered. |
AARHIT services
Services that apply Natural Language Processing
How we put this technology to work for organisations.
Hybrid Intelligence
Natural Language Processing
Classify, extract, search and summarise text across languages, including Indian language requirements where relevant.
Explore service AutomationIntelligent Document Processing
Extract, classify and validate information from documents with confidence scoring and human review queues.
Explore service Hybrid IntelligenceConversational AI and Virtual Assistants
Create assistants for customers or staff that answer from approved knowledge and hand over to people when needed.
Explore service Hybrid IntelligenceGenerative AI and Large Language Models
Build grounded generative AI applications using large language models, retrieval, evaluation and guardrails.
Explore serviceFAQ
Natural Language Processing FAQs
What is natural language processing?
NLP is the branch of AI that enables computers to understand, analyse and generate human language, used for tasks like classification, extraction, search, summarisation and translation.
How is NLP different from large language models?
NLP is the broad field. Large language models are a powerful recent technology within NLP that can perform many language tasks through instructions.
Does NLP support Indian languages?
Yes. Multilingual and India-focused models support Hindi and many regional languages, though accuracy varies, especially for informal or code-mixed text, so testing is important.
What are embeddings?
Embeddings are numerical representations of text that capture meaning, allowing similar texts to be found through vector similarity for semantic search and clustering.
What is named entity recognition?
Named entity recognition identifies and labels items such as people, organisations, locations, dates and amounts within text.
Next step
Exploring Natural Language Processing?
Tell us what you want to achieve. We will help you judge whether this technology is the right fit and how to test it with your own data.