Natural language processing (NLP)

Leverage natural language processing to automate processes, gain quantifiable insights and improve operational efficiency. Luxoft’s natural language processing services combine advanced 3D graphics engineering and unrivaled machine learning expertise.



Our natural language processing services


Sentiment analysis


Gain an accurate, quantified view of your customers' thoughts about your brand with NLP-powered sentiment analysis. Our NLP services can also help refine stock price predictions with automated news analysis in real time.

Fraud detection


Reduce losses from fraudulent claims with improved warranty claim monitoring and advanced data and intent analysis. With our natural language processing services, you can detect fraud more precisely, improving operational efficiency and customer satisfaction.




Enable self-service customer support and enhance digital customer experiences with advanced chatbots communicating like human representatives. We can develop and train a natural language model tailored to your needs in our NLP services framework.

Automated document processing


Accelerate processing times, improve accuracy and boost customer satisfaction with intelligent document processing automation. Our natural language processing services can remove unwanted manual labor from the input workflow.

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Leveraging sentiment analysis for more accurate stock predictions

Analyze stock-related news and historic price data to predict future positions.

Features of our NLP solutions


● Support for all hardware platforms and frameworks used by AI/ML ecosystems

● Modern CI/CD, MLOps and test automation

● Cloud-agnostic deployment to support Azure, AWS, GCP and hybrid solutions

● Up-to-date online documentation and tutorials


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Leverage cutting-edge technology with Luxoft’s Natural Language Processing services

Why choose Luxoft for natural language processing development?


Luxoft's natural language processing expertise


graphics and machine learning professionals


completed projects


success rate

Our toolkit





News and insights

Unleashing the power of automation with conversational AI


Unleashing the power of automation with conversational AI

Anomaly detection using recurrent neural network autoencoders


Anomaly detection using recurrent neural network autoencoders

Mastering MLOps practices for a trading bot


Mastering MLOps practices for a trading bot




Natural language processing (NLP) is an AI-powered technology that makes natural language (i.e., human communications) intelligible for machines. NLP can analyze speech or written text, categorize it, extract insights or generate natural-language responses.

Natural language processing is an evolving interdisciplinary area of research. It combines linguistics and machine learning (a branch of artificial intelligence) to identify patterns in natural language, including contextual nuances.

To fulfill these purposes, the NLP solution can be trained to complete the following tasks:

  • Speech recognition — converting speech into machine-understandable text 
  • Grammatical tagging — identifying the part of speech for a word 
  • Word sense disambiguation — selecting the right meaning for a word 
  • Co-reference resolution — determining when two words refer to the same entity 
  • Sentiment analysis — pinpointing attitudes, emotions and subtext 
  • Natural language generation — producing natural language text or speech 

A natural language processing solution relies on a neural network or another machine learning model. But to achieve its primary goals, algorithms should ingest training data in large quantities with expected outputs (i.e., tags). The algorithms then identify patterns leading to every tag and make predictions based on those rules.

Before running text or speech through the ML algorithms, NLP solutions need to preprocess it. This preprocessing involves:

  • Tokenization — breaking speech down into smaller units (tokens) 
  • Stemming — removing affixes from a word 
  • Lemmatization — reducing a word to its base form (lemma) 
  • Stop word removal — removing words that bear little to no meaning, such as articles and prepositions 

Then, the preprocessed text uses machine learning algorithms trained to accomplish specific tasks. For instance, these are analyzing sentiment, recognizing and responding to speech, detecting spam, etc.

Some common use cases for natural language processing include the following:

  • Virtual assistants. Typically used in smart speakers, virtual assistants process and respond to spoken requests. Examples: Siri, Alexa 
  • Predictive text. NLP algorithms can predict the next word users will type in the search bar or message field. Examples: Gmail’s autocomplete, iOS’s typing suggestions 
  • Brand sentiment analysis. In this case, NLP categorizes social media posts or reviews based on the opinions expressed in them (positive, negative, neutral and everything in between). Examples: Idiomatic, TalkWalker
  • Customer feedback analysis. An NLP tool can sort customer feedback into relevant categories based on their subject. Then, it routes customer support tickets to appropriate teams. Examples: Retently, Zendesk
  • Chatbots. These applications deliver self-service customer support 24/7. Chatbots easily handle routine queries while rerouting complex ones to the support team. Examples: Lemonade’s Maya, Domino’s Order with Dom