What is NLP and how is it different from generative AI?

NLP or Natural Language Processing is the broader field of AI that deals with understanding and processing human language. It includes tasks like sentiment analysis, entity extraction, classification, and translation. Generative AI is a subset that focuses on creating new content. Many NLP applications use both understanding and generation capabilities.

What languages do your NLP solutions support?

We build NLP solutions supporting major world languages including English, Hindi, Spanish, French, German, Chinese, Japanese, Arabic, and many Indian regional languages. Modern transformer models provide good multilingual support, and we can fine-tune for specific language requirements.

Can NLP analyze customer feedback and reviews automatically?

Yes, sentiment analysis is one of the most common NLP applications. We build systems that analyze customer reviews, social media mentions, survey responses, and support tickets to extract sentiment, identify themes, detect issues, and track trends over time at scale.

How accurate is NLP for document classification?

Accuracy depends on the clarity of categories and training data quality. Well-defined classification tasks with good examples routinely achieve 90 to 95% plus accuracy. We use techniques like active learning and human in the loop to continuously improve accuracy on edge cases.

Can NLP extract specific information from documents?

Yes, Named Entity Recognition and information extraction identify specific data points like names, dates, amounts, addresses, and custom entities from unstructured text. This automates data entry from contracts, invoices, emails, and reports.

What is the difference between keyword search and semantic search?

Keyword search matches exact words while semantic search understands meaning. Semantic search finds relevant results even when different words are used. For example, searching for vehicle would also find results about car or automobile. We implement semantic search using embeddings and vector databases.

Natural Language Processing

Extract Insights and Automate Understanding from Text

Your business generates massive amounts of text data. Customer reviews, support tickets, emails, documents, social media mentions, and more. Buried in this text are insights about customer sentiment, emerging issues, competitive intelligence, and operational patterns.

NLP unlocks this value. We build systems that automatically analyze, classify, extract, and understand text at scale. From sentiment analysis that tracks brand perception to document processing that eliminates manual data entry, our NLP solutions turn text into actionable intelligence.

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NLP Development Natural Language Processing

50+
Languages

95%
Accuracy

Why NLP

Transform unstructured text into structured insights and automated actions.

Understand Customer Voice

Analyze thousands of reviews, surveys, and social mentions to understand what customers really think. Identify sentiment trends, emerging issues, and feature requests automatically.

Automate Document Processing

Extract key information from contracts, invoices, emails, and forms automatically. Route, classify, and process documents without manual intervention. Reduce processing time and errors.

Enable Smart Search

Build search that understands intent, not just keywords. Semantic search finds relevant results even when users use different terminology. Dramatically improve search quality and user experience.

NLP SERVICES

Comprehensive text intelligence solutions

01

Sentiment
Analysis

Analyze customer reviews, social media, surveys, and feedback to understand sentiment at scale. Track trends, identify issues, and measure brand perception automatically.

02

Text
Classification

Automatically categorize documents, emails, tickets, and content. Route to appropriate teams, apply labels, and organize information without manual sorting.

03

Entity
Extraction

Identify and extract names, dates, amounts, locations, products, and custom entities from unstructured text. Automate data entry from documents and forms.

04

Semantic
Search

Search that understands meaning, not just keywords. Find relevant results across documents, knowledge bases, and content regardless of exact wording.

05

Text
Summarization

Automatically summarize long documents, articles, reports, and conversations. Extract key points and generate concise summaries for faster consumption.

06

Language
Translation

Machine translation for multilingual content, customer support, and document processing. Support for major world languages and Indian regional languages.

Technology Stack

Modern NLP technologies and frameworks

Transformer Models

BERT, RoBERTa, XLNet for understanding tasks. Hugging Face Transformers for state of the art NLP models. Fine-tuning for domain-specific accuracy.

NLP Libraries

spaCy for production NLP pipelines, NLTK for text processing, Gensim for topic modeling, and custom model development with PyTorch.

Search & Embeddings

Elasticsearch for text search, vector databases for semantic search, OpenAI and Cohere embeddings for meaning representation.

NLP Use Cases

Text intelligence across industries

Customer Experience

Review analysis, survey insights, social listening, feedback categorization

Support Operations

Ticket classification, routing, priority detection, response suggestions

Legal & Compliance

Contract analysis, clause extraction, risk identification, document review

Finance

Invoice processing, financial document extraction, news sentiment for trading

Healthcare

Clinical notes processing, medical coding, symptom extraction, research analysis

Media & Publishing

Content tagging, moderation, trend detection, article summarization

Frequently Asked Questions

Common questions about AI automation for NLP

What is NLP and how is it different from generative AI?
NLP or Natural Language Processing is the broader field of AI that deals with understanding and processing human language. It includes tasks like sentiment analysis, entity extraction, classification, and translation. Generative AI is a subset that focuses on creating new content. Many NLP applications use both understanding and generation capabilities.
What languages do your NLP solutions support?
We build NLP solutions supporting major world languages including English, Hindi, Spanish, French, German, Chinese, Japanese, Arabic, and many Indian regional languages. Modern transformer models provide good multilingual support, and we can fine-tune for specific language requirements.
Can NLP analyze customer feedback and reviews automatically?
Yes, sentiment analysis is one of the most common NLP applications. We build systems that analyze customer reviews, social media mentions, survey responses, and support tickets to extract sentiment, identify themes, detect issues, and track trends over time at scale.
How accurate is NLP for document classification?
Accuracy depends on the clarity of categories and training data quality. Well-defined classification tasks with good examples routinely achieve 90 to 95% plus accuracy. We use techniques like active learning and human in the loop to continuously improve accuracy on edge cases.
Can NLP extract specific information from documents?
Yes, Named Entity Recognition and information extraction identify specific data points like names, dates, amounts, addresses, and custom entities from unstructured text. This automates data entry from contracts, invoices, emails, and reports.
What is the difference between keyword search and semantic search?
Keyword search matches exact words while semantic search understands meaning. Semantic search finds relevant results even when different words are used. For example, searching for vehicle would also find results about car or automobile. We implement semantic search using embeddings and vector databases.

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Cartoon Mango - UI/UX Design and Application development Company from Coimbatore and Bangalore, India

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