Natural Language Processing (NLP) Development

We create Natural Language Processing to allow computers to comprehend decipher and produce human language which implies the creation and application of models.

Our development improves communication, increases productivity, and enhances decision-making in people's lives.

Let’s discuss

What is NLP?

NLP stands for Natural Language Processing. It is a branch of artificial intelligence (AI) that focuses on the interaction between computers and human language. It entails the creation of computational models and algorithms that allow machines to comprehend, interpret, and produce human language in a way that is both meaningful and pertinent to the context.

Why is NLP important?

NLP is important because it enables computers to understand and respond to human language. By automating language related tasks, NLP speeds up time consuming processes, improves global connectivity by removing language barriers, and helps create more personalised digital experiences.

Our NLP development

Machine translation

We build machine translation powered by the state of the art Artificial Intelligence. Our machine translation employs advanced neural network models, ensuring unparalleled accuracy and context- aware translations across a spectrum of languages.

Web Development

Our text classification utilises cutting edge Artificial Intelligence algorithms to analyse, categorise, and organise vast amounts of textual data. Whether dealing with customer feedback, user queries or content moderation, we ensure that every piece of information is classified accurately and with precision.

Sentiment analysis

We develop sentiment analysis to get a better understanding of how people feel about something. We use machine learning to figure out what people think and how they feel about certain things. Create to look at all kinds of text from customer reviews to social media posts to news articles to product descriptions. We help to understand the opinions of people for better results.

Text summarization

We develop text summarization to avoid information overload. Use the best techniques to condense a text while retaining the most crucial detail. We use a variety of NLP techniques to find the most crucial information in a text by automated text summarization systems which then produce a more concise summary.

Speech recognition

We develop speech recognition like Siri and Alexa. Turn spoken language into text for ease of use and fast response. With the help of a variety of machine learning techniques to identify the most likely words and phrases in a given speech input after training them on large datasets of speech and text data.

Natural language generation

Generate top-class content or reports in minutes with the help of our Natural Language Generation (NLG). Develop to transform data into text for better-producing data. We use NLG systems to employ a variety of machine learning techniques and a large dataset of text and data to produce accurate and grammatically correct data.

NLP development process

The NLP development process can be divided into the following phases:



We try to understand the people's needs and requirements for the NLP systems. For example, identify the specific tasks that the system needs to perform, the data that will be used to train and evaluate the system, and the metrics that will be used to measure the success of the system.


Functional POC

In this phase, we involve developing a prototype of the system to demonstrate its feasibility and to validate the people's needs. This prototype should be able to perform the specific tasks that were identified in the discovery phase.


Rapid prototyping and design

After POC, we are involved in iteratively developing and refining the NLP system based on feedback from users and stakeholders. In this phase, we focus on developing a user-friendly, efficient, and effective system.


Development and QA

Here we start to develop the final version of the NLP system and test it thoroughly to ensure that it meets all of the requirements and fulfills all the checklist. Develop documentation and training materials for the system for easy use.


Deployment and maintenance

In the final step, we deploy the NLP system to production and provide ongoing support and maintenance to solve any problems. Monitor the system’s performance and make improvements as needed from time to time.

Specific industries we focus

Real estate
Tour & Travels


The effectiveness, profitability, and efficiency of businesses could all be significantly increased by NLP. NLP can help businesses to enhance customer service, boost sales, cut costs, improve decision making, and create new goods and services by enabling computers to comprehend and process human language.

Follow the steps below to start a NLP project:

  • Discover the business needs.
  • Develop a prototype of the system to develop its feasibility.
  • Develop and refine the NLP system.
  • Test the version thoroughly and develop a final version.
  • Finally, deploy the NLP system and provide ongoing support.

  • Use to retrieve and summarise complex data.
  • Making the brand more user friendly and feasible.
  • Understand customer queries and auto generate responses

Listed below are the system requirements to operate an NLP.

  • Hardware- Powerful CPU
  • Software- Strong GPU, and RAM.
  • Libraries and frameworks- TensorFLow, and PyTorch.
  • Operating system- Windows, Linux, or macOS.

TensorFlow, PyTorch, and Hugging Face Transformers.

The main methods of NLP development can be categorised into

  • 1. Rule based
  • 2. Statistical
  • 3. Deep learning

Search engines, chatbots, voice assistants, and more.

The programming languages used for NLP are Python, Java, and R.

Improved communication, increased productivity, enhanced decision making, and more.

Machine learning is a field of computer science that allows computers to learn without being explicitly programmed. Whereas, NLP deals with the interaction between computer and human language.

Text summarisation, question answering, machine translation, text classification, speech recognition, sentiment analysis, and natural language generation.

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