Unlock Your Data’s Full Potential with Our Experienced NLP Engineers

Can NLP help you sell more or spend less? Can an NLP engineer help you keep customers happy? Yes, they can. And below, we’ll explain how this becomes possible and share several use cases. You’ll also learn that hiring a dedicated team of NLP specialists can be easy and cost-effective. 

What Is Natural Language Processing Development?

Natural Language Processing (NLP) technology lets AI understand humans and perform various tasks quickly and accurately. It empowers our voice assistants, grammar checkers, search engines, audience segmentation, etc. And like a computer vision expert teaches machines to see, an NLP developer helps computers read, write, listen, and reply. ChatGPT3, Alexa, Siri, and Google Assistant are viral NLP-powered tools. 

Companies have lots of textual data around them. Industry news, client feedback, official documents, customer service records, and social media posts are just a few examples. And the value behind this unstructured data can be significant. Still, it can be revealed only when the qualification of NLP specialists performing the analysis is high.  

NLP engineers work with big data, splitting large texts into smaller elements called tokens. Then they label them, find interrelations, and prepare training datasets. Machines learn from such arrays of data and later perform required actions. The four basic NLP analysis approaches are:

  • Lexical — grouping series of letters or sounds.
  • Syntactic — parsing grammar units. 
  • Semantic — identifying relationships between words.
  • Pragmatic — determining the meaning of a sentence. 

What Skilled NLP Specialists Do

As we’ve stated above, the role of an NLP engineer is critical for getting helpful insights from unstructured data pools. These specialists deal with data analysis, algorithms, and predictive models. They use various frameworks and libraries to code in Java, R, or Python programming languages. These specialists can apply statistical analysis and choose optimized methods depending on the task.

They have extensive expertise in processing human language, designing algorithms, and utilizing machine learning techniques for data analysis. NLP specialists also have strong skills in text representation techniques. The creation of effective architecture and analysis of data structures are also what NLP consultants do. 

Natural Language Processing Services We Deliver

The quality and accuracy of NLP models depend on the engineers working on them. And we’re proud to tell you why our offshore natural language processing services are of top quality. 

  • You hire highly-skilled NLP engineers with us. We search for engineers with proficiency in developing NLP models and applications. That’s because the job descriptions we advertise have a few must-have items like strong programming skills, knowledge of NLP libraries, and competence in NLP frameworks. We also require experience in deep learning development. However, we always craft the list of skills necessary to meet clients’ specific needs.
  • You leverage our 12+ years of industry experience. Our company has built development teams for more than a decade. So we have lots of experience finding the best-match professionals, and NLP specialists are among them. Dedicated recruiters are ready to assemble teams of any size and composition. Our background dictates that staying flexible is one of the success factors, so we’re always open to your proposals.
  • You have shared working hours with your team. Staying synchronized with your remote NLP development team is vital. A common schedule allows team members to get answers on the same day, arrange video calls in case of urgency, or make decisions in real time. Since our NLP teams are based in Kyiv, companies from Western Europe will have only a 1-hour difference. The US customers can also have at least 4 hours to work together. 
  • You can easily communicate with every team member. An effective NLP team assumes all its members communicate freely. And to make this happen, we search for natural language processing experts who speak English. For this, we require each candidate to have at least a B1 English level. Also, our recruiters switch to English during the interview to ensure applicants can express their opinion and maintain a conversation. This way, we prevent teams from communicating issues. 
  • You stay focused on work while we handle admin tasks. Our cooperation will have another benefit for you. We let you work on your NLP project without distracting you with organizational issues. We arrange everything to get your team to work. It’s our responsibility to find a comfortable office and buy the necessary equipment for the NLP experts you hire. Also, we sign cooperation agreements with each programmer, arrange salary payments, transfer taxes, and submit reports. 

Benefits of Expert-Driven NLP Service 

Here are a few main advantages of utilizing NLP to unlock the full potential of your data.

  • Cutting costs on customer service. NLP-powered chatbots and call centers can process simple customer queries and transfer more complicated cases to humans. In addition, AI can share the query details with managers, giving them the context of the communication. This way, companies can hire fewer customer support agents and, thus, optimize expenses. 
  • Analyzing clients’ feedback. Natural language processing experts can teach algorithms to analyze hundreds of posts and comments related to your product. AI-powered text processing software can identify the mood of your buyers. Moreover, it can classify the sentiment as negative or positive, showing you the overall impression of your brand. Such data can bring valuable and profit-growing insights. 
  • Better targeting. Since Artificial Intelligence can monitor social media, businesses can meet their prospects there. NLP methods will be able to identify potential buyers at different stages of their customer journey. This allows brands to target more audience segments with personalized offers. Still, the key benefit here is accessing users with buyer intent. 
  • Adding new features to your product. Your existing and potential buyers can not only help you fix issues with your product or service. They can be your brainstorming partners and idea generators in an add-new-features game. And while you won’t be able to check 2,000 messages right away, an NLP-driven tool will do that for less than a minute. 
  • Improving communication with colleagues and clients. With top-notch translation software, international companies can step towards more effective communication. From Slack chats to emails — NLP tools can speed up information exchange. This functionality is even more topical when applied to contacting prospects and customers. So if you sell in many countries, writing in the local language can generate more leads.
  • Saving time on processing documents. Automation is inevitable when your employees have to read through hundreds of pages daily. But only a sophisticated natural language processing expert can help you. Firstly, that’s because creating information retrieval algorithms requires accurate indexing of databases. Secondly, it’s critical to ensure relevant matches to users’ queries.   

Key Techniques an NLP Development Company Embraces 

Below are the methods NLP-powered AI uses to understand text and speech data. 

  • Sentiment analysis (opinion mining). This technique allows companies to identify emotions behind buyers’ comments, posts, and conversations. The classification can be as straightforward as very negative, negative, neutral, positive, or very positive, which are used by e-stores. However, there are more detailed approaches based on keywords. For instance, aspect-based analysis helps to track customer experience with certain products. The intent-based technique lets brands detect the buyer intent by finding words like “discounts” or “prices.”
  • Topic modeling. When you have megabytes of unclassified text in various documents, topic modeling can help. It’ll analyze the texts and organize them into clusters. For instance, AI will split the files into invoices, agreements, etc. This technique includes two methods: latent semantic analysis (LSA) and latent Dirichlet analysis (LDA). The first allows NLP experts to group documents under similar topics. The LDA identifies the percentage of topics in each document.
  • Entity recognition. This method extracts words or phrases that refer to a separate category. For instance, simple algorithms would classify Google and Coca-Cola as brands or companies. Among other categories can be names of individuals, organizations, locations, time, etc. Search engines, recommendations, HR management, healthcare, customer support, and other industries actively use this method. It helps them cut processing time and reduce workload. 
  • Language translation. Machine translation doesn’t need a human to return well-structured and semantically correct text. Modern algorithms explore words, phrases, and their interconnections to avoid word-by-word translation. And this NLP method is viral across many domains. Multinational companies use AI-powered translation tools to communicate with clients and employees. Or use this software to communicate with customers in chatbots.
  • Summarization. Artificial Intelligence can create short yet informative summaries of texts using two methods. The first is an extractive summary approach that identifies the keywords and phrases and combines them without any changes. Consequently, such a text includes grammatical errors. The second is an abstractive approach that involves paraphrasing initial sentences while keeping the grammar correct. The abstraction-based summarization is more human-like, but it requires more working hours of an NLP software engineer. 
  • Keyword extraction. With this approach, you get the most relevant and frequently used words and phrases in a text. Businesses need to process thousands of texts for various purposes. And natural language processing developers have created many tools to meet this demand. These instruments identify keywords in multiple languages and industries. For instance, sentiment analysis described above uses specific keywords to analyze customer experience. 
  • Speech recognition. Voice recognition technology is an integral component of our lives. We use Alexa and Siri to get precise answers and are sure they understand what we mean. That’s because the Spoken Language Understanding algorithms power them. They include speech-to-text processing that simply transcribes the audio. The second element is Natural Language Understanding and Generation, which helps AI understand our queries and reply accordingly.

Below are the techniques used in AI-driven text pre-processing:

  • Tokenization. This is the first and the most critical process in NLP. Here, algorithms split the unstructured text into the smallest pieces, making it readable for machines. The “scale” of these elements will depend on the task set to an NLP scientist/engineer. So, in the end, they get an array of characters, subwords, words, or sentences that can be analyzed. 
  • Lemmatization and stemming. Stemma is the root form of a word. When algorithms do stemming, they cut all the endings and prefixes. Often these are incorrectly spelled words, like “studi” for “studies.” This, for instance, helps us get a greater number of relevant search results. Lemmatization goes further and returns words to their initial forms — lemmas. So, the lemma for “studies” will be “study.” 
  • Stop word removal. The stop words in NLP have a bit in common with spam words in email marketing. They include little to no relevant information and don’t let readers get to the core information right away. So they need to be removed. Specific ML projects may have their own or domain-specific lists of stop words. However, typically these are prepositions, articles, and conjunctions. 

How NLP Services Work: 3 Use Cases 

Natural language processing services & solutions offer valuable insights to hundreds of businesses. They transform the way brands communicate with clients and close deals. And here are just a few real-world examples.

Text Summarization for Banks 

JPMorgan partners with ML scientists from the Machine Learning Center of Excellence (MLCOE). It helps the bank deploy innovations and build scalable solutions with  AI-powered technologies. They include NLP, Reinforcement Learning, Speech Recognition, and Time Series. 

Banking clients often need to sign long-to-read documents, which requires significant time. And the challenge JPMorgan faced was helping their customers sign documents faster. So top-level NLP consultants from MLCOE decided to run an experiment. They used quantum computing to summarize information for customers. 

The algorithms had to extract the most relevant data using a text processing method called extractive summarization. The program selects informative sentences and packs them in a limited stack, avoiding redundancy. The research was successful; still, the Head of Global Technology says the solution needs further improvement. 

Speech Recognition for Healthcare 

Doctors in hospitals are often overloaded. Many organizations still have to manually add information to medical records. This usually happens during the patient’s visit, which takes time for both. However, many clinics have implemented NLP-powered speech recognition tools. They allow doctors to dictate the necessary information and have it automatically converted into text. 

Also, from the times of Covid, remote patient monitoring has become critical. In addition to allowing video calls and messaging, some of these tools enable voice IoT control in patient rooms. Nurses can control temperature, lighting, and TV with their voice commands. And they can check all the parameters on informative dashboards.  

Patients also can use Amazon Alexa or Google voice assistants to ask for help. For instance, if seniors need help to use the bathroom or therapy transport, they just say it. Such a message will immediately reach the right caregiver through interconnected devices. Patients can see their request statuses on mobile or screens.

Searching Legal Documents 

Legal advisors and corporate departments spend hours searching for relevant data in contracts, terms and conditions, letters of credit, etc. Manually preparing all the necessary documents for a court session can take months. So unsurprisingly, NLP in law evolves rapidly. Many companies can hire one natural language processing engineer or a team of NLP developers to speed up searching.

Data mining experts create NLP models to handle data-heavy tasks like document review and data management. Customized searching features allow algorithms to highlight relevant information within minutes. So AI offers speed and accuracy no matter the document population. Some algorithms enable visualization of the researched data. Others even allow legal advisors to check how a certain court may rule. 

You’ll also find NLP-powered models that introduce continuous learning technologies and innovative categorization capabilities. Still, even deep learning models aren’t always effective in classifying legal texts or their interpretation. So this field has where to grow, and NLP experts keep working on delivering more nuanced search results. 

Correcting Grammar Mistakes

Making grammar mistakes or misspelling words isn’t easy these days. And that’s due to NLP algorithms. Free grammar-checking tools highlight easy-to-detect punctuation and spelling errors, while commercial software goes much further. Market leaders like Grammarly can suggest rephrasing or restructuring entire sentences or paragraphs. This helps readers better understand the writer’s ideas.

Additionally, this NLP-driven tool gives explanations or reveals additional information about its correcting suggestions. However, a user can either accept or dismiss it. This writing assistant can also detect the tone of your text. So Grammarly is beneficial for business correspondence, academic writing, content creation, email marketing, sending posts to social media, etc.

Grammarly uses innovative NLP-driven algorithms that treat your writing as a foreign language that needs to be translated into English. Deep neural nets learn from qualified linguists and professional literature. Moreover, deep learning engineers and computational linguists help the system learn from the choices of millions of users. So Grammarly suggestions don’t make your texts simply grammatically correct — they become clear and powerful. 

How We Customize NLP Expert Solutions for You

You only need to make the first step — send us a web form — to get a high-quality natural language processing service. We’ll build you a team of NLP experts covering projects of any size and complexity. For 12+ years on the market, we’ve customized solutions for tens of clients, and below are a few factors of our success. 

  • Collaborative approach. We follow specific procedures while communicating with clients. Along with that, our managers approach each customer’s request individually. They work closely with project managers to understand their unique data requirements. We often explore project goals, tasks, deadlines, and your wishes before finalizing the NLP team composition. Such collaboration helps us find the best-match candidates. 
  • Customizable NLP solutions. Whether you need to extract data from financial reports, legal documentation, or political news, we know how to get there. The NLP specialists we hire know all about extracting valuable insights from various domains and industries. NLP-driven chatbots, sentiment analysis of reviews, natural language processing customer service — you name it. 
  • Optimized costs. NLP-powered analytics lets our customers cut costs on processing unstructured data. And we help them get those best-fit solutions cost-effectively. We contract top NLP experts from Ukraine, saving our partners up to 20% of project costs. That’s because the annual salary of a natural language processing developer in Ukraine is significantly lower than in the USA or EU (due to a lower living index).
  • Minimal terms. We know time is precious, especially for AI-driven solutions. So we do our best to equip you with NLP professionals within a few weeks. Our recruiters streamline candidates’ search through partner networks, popular job boards, LinkedIn, and their own talent pools. They use advanced tools to screen resumes automatically, ensuring each potential teammate meets your requirements. 
  • Responsibility. We accept the responsibility for each NLP research engineer we hire for you. So our recruiters consider every detail about an applicant — from preferred working schedule to fitting the company’s environment. This approach provides long-term and mutually beneficial cooperation with your developers. And once your new team member doesn’t fit in your workflow for valid reasons, we’re ready to replace this person. 

Hire an NLP Specialist with Us: 3 Cooperation Models 

We’re glad to offer several formats of cooperation for various projects. And 12+ years of experience prove that many companies have leveraged this flexibility. 

  1. Hire an NLP Coach or NLP Consultant 

Part-time cooperation might work best for you if you run a relatively small NLP project and don’t have tight deadlines. Also, this option will work for one or two mid-level NLP engineers who need support from a top professional. We can arrange coaching sessions to train your specialists on the current project tasks. 

This way, your team will gain experience through applying the best NLP approaches. You choose the schedule and only pay for the hours you need. Do you want a consultant for just a few hours a week? Or are you planning to engage the specialist for 3-4 hours daily? Let’s discuss your needs and find the optimal solution together. 

  1. Hire an NLP Certified Specialist Full-Time

Try the full-time format once you want to enhance your data science team with a Python developer with NLP expertise. For a fixed fee, you can set tasks to a remote team member and get a top talent busy the whole day. Try this option if your NLP project doesn’t require more than 40 hours a week.

A dedicated specialist, like, for example, an Android developer NLP, will complete tasks accurately and within timelines. So the topic discovery, content categorization, machine translation, speech-to-text conversions, and other assignments will be in professional hands. We’ll hire NLP developer with relevant experience and tech stack. And once you need to extend your team, we’ll be glad to help you.

  1. Hire Remote NLP Developers as Your Dedicated Team

A dedicated NLP software development team will fit new and ongoing NLP projects with a long-term perspective. Once you need to launch a new AI-driven instrument for text processing, our specialists are here to help. They’ll explore your needs and advise on your case’s most effective team composition. 

The engineers will run the statistical analysis, launch test models, and develop ML algorithms. They’ll be able to work with advanced NLP frameworks like Spark and Hadoop. Knowing the NLTK, Gensim, CoreNLP, spaCy, and other libraries are also among the requirements we set. Our recruiters will find candidates proficient in text clustering and classification using R, Java, and Python. 

Final Words

NLP technology can level up your business with extracted insights from social media, relevant search results, accurate text translation, speech recognition, etc. But unlocking the full potential of your data is only possible with experienced NLP developers. And we’re here to help you hire them promptly, effortlessly, and cost-effectively. You can rely on our 12+ years of expertise, advanced recruiting techniques, and collaborative approach. 

Level up Your Data Analysis. Hire NLP Developers. Get in Touch Now.