Transform business capabilities with NLP services and expertise

Partner with experts to gain market leverage across natural language processing (NLP) projects

Enhance the accuracy of your apps with NLP services

Joining forces with an NLP service provider can help you achieve your business goals, whether you’re looking to unlock hidden insights, scale text and audio, or extract value from vast amounts of complex data.

Access top engineering talent to build NLP software faster

Use NLP solutions to find patterns, extract meaning and improve performance

Acquire bespoke machine learning skills. Boost the performance and usefulness of your ML models and solutions
Save time with skilled external experts. Capitalize on the skills of your NLP service partners to learn and add business value
Scale with security, ease, and speed. Leverage customized and proven NLP processes, designed to change with your needs
Create and train ML models faster. Use automated processes like data labeling to maximize performance and accuracy

Training machine learning model to find illegal contractual clauses

Natural Language Processing model that protects consumers form unfair contracts.

Poland’s Office of Competition and Consumer Protection wanted to create an automated process that would alert consumers by highlighting suspicious parts of the text to protect consumers from abusive clauses.

This required creating a tool that can analyze the language of complex legal texts, detecting abusive clauses before the consumer signs the agreement.

Netguru role:

Creating a system to detect abusive clauses
Training a machine learning-powered Natural Language Processing (NLP) model to classify contractual terms

Read Case Study

We’ve had a long-term relationship with Netguru. Netguru is a great and super-professional service provider, which brought new technologies, new methodology, and a fresh perspective to our project.
Assaf Davidi
VP Product at temi

Utilize NLP technologies to create business value

Extract actionable insights from data using natural language processing techniques

Text classification and data labeling. Analyze text and assign a set of predefined tags or categories based on the content
Named entity recognition. Scan text, identify fundamental entities (nouns & verbs) & classify into predefined categories
Information extraction. Retrieve specific info about a selected topic from one or more bodies of text
Sentiment analysis. Scan relevant data to establish whether it’s positive, negative or neutral (aka opinion mining)
Text summarization. Condense information in a large body of text into a smaller form, for quicker consumption
Improved search results. Use contextual meaning and sequence similarity to return better, more advanced matches

We’re experts in streamlining NLP model creation

At Netguru, our Natural Language Processing services are built upon extensive expertise and robust processes, refined over years of experience. Our data science and NLP experts have successfully developed applications spanning a variety of industries, so whatever your goals are, you can be sure your project is in good hands.

Project setup. Gather information and define the goals of the project.
Research. Collect internal and external insights and identify possible solutions.
Development. Create the codebase for data processing and model training.
Setting a baseline. Establish an initial level of performance with the first model.
Iterative experimentation. Use different models and approaches to improve performance.
Deploying the model. Release a model that achieves the project’s goals.

What are NLP models?

Contents

Researchers and developers create NLP models to make it easier for computers to understand and communicate with us. If you want to build NLP applications, language models are vital. However, building them from scratch takes time, which is why some people use pre-trained language models. Here are some of our top picks:

Bidirectional Encoder Representations from Transformers (BERT)
XLNet
Robustly Optimized BERT Approach (RoBERTa)
ALBERT (another BERT-modified model)
StructBERT (the latest extension of BERT– so far)
ELECTRA
GPT
LayoutLM
XLM
Perceiver
Linformer
BigBird
T5

NLP technologies

NLP, a branch of artificial intelligence (AI), enables computers to understand natural human language – the words and sentences we use – and create value from it.

NLP technologies are used as part of intelligent document processing (IDP) to extract actionable data and insights from unstructured text and semi-structured data, and info streams like social media captions.

Natural Language Processing services utilize a number of techniques, all of which are offered at NLP consulting companies like Netguru.

Information extraction (IE)

This automated data processing technique retrieves specific info relating to a selected topic from one or more bodies of text. Using IE, you can extract information from unstructured and semi-structured data, and structured data that contains machine-readable text.

Information extractions spans automatic annotation, content recognition, and data extraction from images and video. For the purposes of natural language processing, IE is primarily used to extract structured data from unstructured data.

Text generation and summarization

This condenses info in a large body of text into a smaller or shorter form that’s quicker to read or consume. Extractive text summarization identifies the most important sentences and joins them to create a summary. Abstractive summarization picks out the most important parts, interprets their context, and reproduces them in a new way.

Named entity recognition

When thinking about document processing analysis, entities are the main components of a sentence, and they include nouns and verbs. Broadening that, named entity recognition is an NLP technique that automatically scans a body of text, picks out fundamental entities, and then places them into predefined categories.

It processes large volumes of text and identifies entities like names, dates, times, locations, companies, and monetary values, helping you organize data more efficiently.

This natural language processing technique is also known as entity identification, entity extraction, or entity chunking. Uses range from instantly extracting relevant information about candidates during a recruitment process to classifying content for news channels.

Text classification

Aka texting tagging or text categorization, this uses natural language processing to automatically analyze text and assign it a set of predefined tags or categories based on the content.

Text classification is an efficient and effective alternative to manual data entry and processing, one of the foundations for sentiment analysis, and plays a role in topic detection and language detection.

Text similarity

This NLP technique establishes how close two pieces of text are in both word construction (lexical similarity) and meaning (semantic similarity).

Question answering

An important NLP technique, question answering allows you to ask a question to context text, and then your ML model finds the most appropriate answer from that context text – if it exists.

Relationship extraction

This is an extension of named entity recognition. It extracts semantic relationships from a body of text, typically between two or more entities of a certain type such as a person, organization, or location.

These entities fall into semantic categories such as “married to”, “employed by”, or “lives in”. Relationship extraction is used when listing found entities isn’t enough, and you need to know the relationship between them.

Sentiment analysis

Also known as opinion mining, this NLP technique scans relevant data to establish whether the overall view is (very) positive, (very) negative, or neutral.

For example, entity sentiment analysis is used to monitor consumer opinions of products and services, satisfaction levels regarding a company’s customer support, and how well a brand is perceived on social media platforms.

Additional nuances include feelings and emotions, and levels of intent and urgency. Sentiment analysis relies on sophisticated machine learning algorithms and intelligent automation.

Machine translation (MT)

This technique automatically converts one natural language into another. By doing that, you preserve the meaning of the input text while producing fluent text in the output language.

Data labeling or tagging

Essential to making your data preparation worthwhile, this assigns info labels or tags to each raw data sample such as an image and text. The labels are allocated according to the content and context in question and they’re used to advise machine learning models. Three of the most popular data labeling types for image, text, and audio are:

Natural language processing (NLP) tools like entity annotation and linking, text classification, and phonetic annotation
Computer vision techniques like image classification, image segmentation, object detection and pose estimation that help machines understand visual data
Audio processing to identify and tag background noise and develop a transcript of the recorded speech (using NLP algorithms)

Semantic search

Also known as context or semantic analysis, this is a search performed via NLP models that evaluates context and text structure to accurately decipher the meaning of words that have more than one definition.

What are tools used in NLP?

At NLP companies like Netguru, we use many NLP tools, ranging from a host of machine learning libraries to a selection of pre-trained models. The most popular and fastest-growing programming language is Python. Why? It’s uber-versatile, meaning we can use it for NLP, data science, machine learning, and many more.

In terms of NLP libraries, we use spaCy, NLTK (Natural Language Toolkit), Hugging Face transformers, Gensim, and Spark NLP.

And what about transformers and language models? We rate BERT, RoBERTa, XLNet, and ELECTRA. When it comes to machine learning libraries, we use Pyro, XGBoost, LightGBM, Scikit-Learn, and in terms of deep learning libraries, we’re all about Keras, TensorFlow, and PyTorch.

Give Temi – the personal assistant robot – a wave

Using natural language processing to ensure Temi’s communication skills. The aim of Temi? To lead the home robotics market. Building Temi effectively (hardware, design, and software) required extensive research and the best possible technology.

Our operating system and apps for controlling the robot were well-received, with first-rate feedback from industry experts.

Using advanced machine learning models to offer a state-of-the-art experience, Temi was met with rounds of applause at events across the US and Europe.

Read Case Study

Audio recognition with Baby Guard

Monitoring your baby’s sleep remotely from another room.

A free, user friendly application that replaces electronic baby monitors. The app works over WiFi, not the Internet, so your baby’s data isn’t shared with any third-party servers.

To achieve a high performance, we used custom audio processing algorithms and neural networks to handle the classification of the signal. The system can detect a baby’s cry rapidly and accurately. Our designers handled the UX to make the app easy and intuitive to use.

Read Case Study

Personalized shopping with Countr

Accurate product recommendations in a social shopping app. Countr is a personalized shopping app that enables its users to shop with their friends, receive trusted recommendations, showcase their style, and earn money for their taste – all in one place. Using machine learning models, we delivered recommendation and feed-generation functionalities and improved the user search experience.

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Companies about our digital services

Netguru has been the best agency we've worked with so far. Your team understands Kelle and is able to design new skills, features, and interactions within our model, with a great focus on speed to market.
Adi Pavlovic
Director of Innovation at KW
My experience working with Netguru has been excellent. Outstanding software teams are resilient, and our developers at Netguru have certainly proven to be that. Our Netguru friends have become as close to team members as possible, and I am grateful for the care and excellence they have provided.

Gerardo Bonilla
Product Manager at Moonfare
Working with the Netguru Team was an amazing experience. They have been very responsive and flexible. We definitely increased the pace of development.
Marco Deseri
Chief Digital Officer at Artemest

15+
Years on the market
400+
People on Board
2500+
Projects Delivered
73
Our Current NPS Score

Delivered by Netguru

We are actively boosting our international footprint across various industries such as banking, healthcare, real estate, e-commerce, travel, and more. We deliver products to such brands as solarisBank, PAYBACK, DAMAC, Volkswagen, Babbel, Santander, Keller Williams, and Hive.

$47M
Granted in funding. Lead generation tool that helps travelers to make bookings
$20M
Granted in funding. Data-driven SME lending platform provider
$28M
Granted in funding. Investment platform that enable to invest in private equity funds
$5M
Granted in funding. Self-care mobile app that lets users practice gratitude

Everything you need to know about our approach to NLP services

Here are some insights regarding work with NLP models

How much data do I need to start an NLP project?

It depends - in a lot of cases we can create NLP solutions with limited domain data, thanks to pre-trained models.

There isn’t a single answer here. In short, it depends. But in many cases, it’s certainly possible to create natural language processing solutions with limited domain data, thanks to pre-trained models.

Can I start an NLP project in a language other than English?

For sure, that’s not a problem. NLP covers additional key languages such as French, Spanish, or Chinese. However, some languages can be more challenging than others, especially those less popular like Urdu, Farsi, and Arabic, which require more effort and data to create an actionable NLP model.

Why do I need to use NLP in my business?

If your business deals with data in text format (all businesses do) you can automate your processes with NLP solutions.

If your organization deals with data in text format (what business doesn’t?) you can automate your business processes with NLP solutions. That means you can improve performance and scale with speed and ease. And if you partner with a trusted natural language processing company like Netguru, you save time by working with experts and can acquire useful NLP and ML skills to use (and share) going forward.

How can NLP help my business?

Natural language processing enables the automation of processes, meaning less manual work, freeing up human workers to focus on more creative and relevant tasks.

Document AI solutions like form filling automation or named entity extraction (NER) can help reduce bias and human error. Lower number of human mistakes helps businesses to speed up document processing and prevent being fined for improperly filled government declarations.