7 Real-World Applications of NLP You’re Already Using
Natural Language Processing (NLP) may sound like a technical buzzword, but chances are you're using it every single day—often without realizing it. From voice assistants to spam filters, NLP is powering some of the most common tools we rely on to work, communicate, and stay informed.
In this blog, we'll explore seven real-world NLP applications you encounter in daily life. Plus, for automation engineers and no-code builders, we’ll include ideas on how you can integrate these into your own workflows using tools like Make.com or OpenAI.
1. Voice Assistants (Alexa, Siri, Google Assistant)
What it does: NLP enables voice assistants to understand your speech, convert it to text, detect intent, and provide relevant responses or actions. It combines automatic speech recognition (ASR), syntactic analysis, and intent detection to provide natural-feeling conversations.
Real-world examples:
- Asking Siri: "Remind me to call John at 5 PM"
- Asking Alexa: "What’s the weather in New York?"
- Google Assistant creating events, sending messages, or performing smart home tasks
Automation Tip: Use Make.com to create a webhook that logs every voice command to a Google Sheet for review or triggers an automation (e.g., setting a calendar event).
🔄 Template coming soon — stay tuned!
2. Chatbots for Customer Support
What it does: Customer support chatbots use NLP to answer FAQs, guide users through product issues, or escalate tickets to human agents. They combine language understanding with decision trees or AI to reduce human workload.
Real-world examples:
- Shopify or Zendesk chatbots answering shipping or refund questions
- Bank bots helping you check balances or reset passwords
- Airline bots checking flight statuses and rebooking tickets
Automation Tip: Use OpenAI with Make.com to create a custom chatbot that answers specific business-related queries using your internal knowledge base.
🔄 Template coming soon — stay tuned!
3. Email Spam Filters
What it does: NLP helps classify incoming emails as spam or legitimate by analyzing text, formatting, metadata, and even emotional tone. Modern filters use machine learning combined with linguistic rules to adapt over time.
Real-world examples:
- Gmail’s Promotions and Spam tabs
- Outlook’s focused inbox feature
- Enterprise filters flagging phishing emails based on tone or urgency markers
Automation Tip: Build an email classification tool using Gmail + Make.com + sentiment analysis with OpenAI or AWS Comprehend.
🔄 Template coming soon — stay tuned!
4. Smart Search Engines (Google, YouTube, Shopify Search)
What it does: Modern search engines don’t just match keywords—they understand user intent, natural phrasing, and context through advanced NLP. This includes semantic search, voice queries, and personalized ranking based on past behavior.
Real-world examples:
- Typing "how to fix leaking pipe" returns results like "plumbing repair tips"
- YouTube suggesting related videos based on conversational queries
- Shopify stores with AI-powered internal search recommending relevant products
Automation Tip: Add intelligent search to your internal databases by using embeddings from OpenAI to make semantic search queries.
🔄 Template coming soon — stay tuned!
5. Language Translation Apps (Google Translate, DeepL)
What it does: NLP translates text from one language to another while attempting to preserve meaning, tone, and context. Neural machine translation (NMT) models like DeepL use attention mechanisms and context modeling to achieve high accuracy.
Real-world examples:
- Translating entire websites for multilingual audiences
- Scanning restaurant menus via camera and translating in real-time
- Businesses using translation APIs to communicate with global clients
Automation Tip: Auto-translate customer emails using DeepL API + Make.com, then store translated responses in your CRM.
🔄 Template coming soon — stay tuned!
6. Sentiment Analysis in Product Reviews
What it does: Sentiment analysis detects whether content expresses positive, negative, or neutral emotions. It's used for brand monitoring, customer feedback analysis, and automated decision-making.
Real-world examples:
- Amazon highlighting “Most Positive” or “Most Critical” reviews
- Feedback widgets rating your support experience
- Airlines tracking negative tweets during travel disruptions
Automation Tip: Scrape product reviews using Apify or Browserbear, run sentiment analysis with OpenAI, and send insights to Notion dashboards.
🔄 Template coming soon — stay tuned!
7. Text Summarization Tools
What it does: NLP condenses long articles, reports, or meetings into short summaries for quick reading. Modern summarization models combine extractive and abstractive techniques for optimal results.
Real-world examples:
- Email tools like Superhuman summarizing threads
- TL;DR plugins for blogs or YouTube transcripts
- Meeting assistants like Otter.ai providing summarized notes
Automation Tip: Use Make.com + GPT to summarize incoming emails or Slack threads and send a daily digest to your phone or email.
🔄 Template coming soon — stay tuned!
Bonus Use Case: Resume Screening in HR
What it does: Recruiters use NLP to scan thousands of resumes and job descriptions, matching candidates with open roles based on skills, experience, and keywords.
Real-world examples:
- LinkedIn job match scoring
- ATS platforms filtering resumes automatically
Automation Tip: Combine Google Sheets + Make.com + OpenAI embeddings to auto-rank resumes from Google Drive folders.
🔄 Template coming soon — stay tuned!
Final Thoughts
These NLP-powered tools are no longer experimental — they're powering how we work, shop, learn, and automate. For automation engineers, consultants, and founders, understanding these applications helps identify where AI can save time and unlock efficiency.
As NLP models become more accessible through APIs and no-code platforms, there’s never been a better time to start experimenting. Even a basic integration using Google Sheets + Make.com + OpenAI can deliver real business value.
From smart assistants to smart recruiting, NLP is redefining how we interact with data. Don’t just consume it—start automating with it.
Coming Up Next:
Blog 3: How Businesses Use NLP to Automate and Scale Operations — with case studies and automation workflows included!
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Have an NLP use case in mind? Contact Lumifyre for a free consultation on AI automation solutions.