Natural language processing services

Natural language processing (NLP) is a machine learning (ML) technology that enables computers to understand and work with human language. Through natural language processing work, machines can process language, reorganize and analyze text, or extract specific information to make it useful—for example, by summarizing documents, categorizing topics, or identifying key phrases.

Key business NLP applications include applications that boost automation, enhance customer engagement, and support data-driven decisions.

Here are some of the primary NLP use cases for businesses

1/ Virtual assistants and chatbots

Many businesses use virtual assistants like Siri and Alexa, or chatbots on their websites, to answer customer questions, provide support, and assist with sales. These tools let customers get quick answers on their own, which improves their experience and reduces the workload on customer service teams.

2/ Sensitive data redaction

Industries like insurance, legal, and healthcare deal with a lot of private information, such as medical records and financial details. Using natural language processing, they can automatically find and hide sensitive details—like names, policy numbers, or social security numbers—quickly and accurately. This automated process is faster and more reliable than having people review and redact information by hand.

3/ Entity sentiment analysis

is an NLP technique that identifies specific entities (like people, brands, or products) in text and analyzes the sentiment linked to each one. By examining reviews, social media posts, or emails, businesses can gauge sentiment—positive, negative, or neutral—helping them understand customer attitudes, adjust marketing, and monitor brand reputation in real time.

4/ Spam detection

NLP technology helps with automatically filtering out unwanted or harmful emails. This keeps employees focused on legitimate messages and protects the business from security threats.

5/ Named entity recognition (NER)

Natural language processing can automatically identify important details in text, like names, places, and dates, and sort them into categories. This way, you can pull useful information for tools like CRM systems, reports, or market analysis, organizing data easily and making better, faster decisions based on this information.

6/ Machine translation

Natural language processing can translate text accurately between languages while keeping the original meaning clear. This is key for global businesses because it allows them to communicate consistently and support customers in different languages—without losing important details or meaning in translation.

Natural language processing uses specific techniques—like breaking down text into individual words (tokenization), reducing words to their base forms (stemming), and identifying parts of speech (part-of-speech tagging). They help transform messy, unstructured text into organized data that artificial intelligence (AI) and machine learning models can easily analyze and understand on a large scale.

Practical examples of NLP use cases across 10+ industries and business functions

Virtual assistants and chatbots

NLP-powered virtual assistants and chatbots handle routine customer interactions across different industries, making processes faster and easier:

• E-commerce: Chatbots help customers find products, check order statuses, and answer return questions, creating a quicker, smoother shopping experience.
• Banking: Virtual assistants answer common banking questions about balances, transactions, and loans, allowing human agents to focus on complex requests.
• Healthcare: Medical assistants manage tasks like scheduling appointments, answering symptom-related FAQs, and providing follow-up instructions, reducing admin tasks in clinics.

Sensitive data redaction

Natural language processing automatically hides personal information in documents to protect privacy and improve efficiency:

Healthcare: Personal details like patient names and record numbers are redacted in shared documents to meet HIPAA privacy requirements and speed up processing.
Insurance: NLP masks policy numbers and addresses in claims data to protect privacy when sharing information with third parties.
Legal services: Law firms use NLP to redact client information in case files sent to external partners, saving time and ensuring confidentiality.

Sentiment analysis

Natural language processing analyzes customer emotions and reactions to help businesses make better decisions:

• Retail: Sentiment analysis tracks social media reactions to new products, allowing the marketing team to adjust campaigns based on real-time feedback.
• Media: Media companies monitor audience responses to shows through social media and reviews, guiding content decisions based on how viewers feel.
• Hospitality: Hotels analyze guest feedback to identify satisfaction trends, using these valuable insights to improve services and strengthen loyalty programs.

Spam detection

Natural language processing helps identify and filter out spam or suspicious messages to protect users and improve efficiency:

• Telecommunications: An NLP system blocks spam and phishing messages, protecting customers from scams and enhancing service quality.
• Financial services: Banks use NLP to detect and flag suspicious emails, reducing phishing risks and keeping customer accounts safe.
• Human resources: NLP filters out irrelevant or spam job applications from recruitment platforms, enabling hiring managers to focus on qualified applicants.

Named entity recognition (NER)

NER lets businesses automatically identify and categorize important information within text:

• Real estate: NLP extracts details like property addresses, price ranges, and seller names from listings, helping real estate firms compile and organize property data for easy client access.
• Publishing: A publishing company scans manuscripts for character names, places, and dates, making it easier to catalog and tag for search and reference.
• Travel: Airlines identify travel details, such as destinations and departure dates, from customer emails, allowing them to respond more quickly and provide better service.

Machine translation

Natural language processing translates text accurately across languages, preserving important context and meaning:

• Manufacturing: Translates technical manuals and safety guides for global teams, ensuring clear, accurate information in multiple languages.
• Customer support: An e-commerce company translates customer inquiries into agents’ native languages, allowing for quicker, clearer support worldwide.
• Legal services: Law firms translate contracts and legal documents, maintaining important language nuances to ensure consistency and compliance across different regions.

What is natural language processing as a service?

NLP as a service (NLPaaS) is a cloud-based offering. It provides NLP tools businesses can use online without having to create or manage their own systems. By using cloud-based natural language processing services, companies can easily access functions like text analysis, sentiment tracking, translation, and entity recognition through APIs.

This model is valuable because it lets companies quickly add advanced NLP features to their applications without needing specialized in-house teams or infrastructure.

Popular NLPaaS providers include Amazon Comprehend, Google Cloud’s Natural Language API, and Microsoft Azure’s Text Analytics. These platforms offer scalable and flexible NLP solutions for various business needs, such as processing customer support tickets
and analyzing real-time data. Yalantis has significant experience working with these tools, especially Amazon Web Services (AWS), which enables us to create custom NLP solutions with quick setup, strong security, and dependable performance.