6 Benefits of Creating an NLP App Using OpenAI

If you are looking to develop an NLP app using OpenAI, you should know how the NLP app helps to enhance user experience.

OpenAI is a research lab that specializes in AI. Founded in 2015, it has attracted a series of high-profile investors, including Microsoft and Amazon Web Services. In fact, according to Statista, OpenAI ranks among the most funded machine-learning startup firms with funding of over 1 billion U.S. dollars as of January 2023.

Since OpenAI has three different models such as:

  • GPT-3 for performing natural language tasks
  • Codex for translating natural language to code
  • DALL.E to create and edit original images

Developing apps with these OpenAI APIs can help you to automate the process with reduced cost. You have landed on the right blog if you are looking to know the benefits of creating an NLP app using OpenAI.

Being a leading provider of OpenAI-based app development solutions, we understand the business benefits of developing an NLP app using OpenAI. So, we have listed 6 important benefits that help to improve customer experience.

Let’s get started.

6 Core Benefits of Creating an NLP App Using OpenAI

Here are the 6 important benefits that you can leverage if you are planning to develop an NLP app using OpenAI.

  1. NLP Provides Context-based Response to Customer Questions

    Developing an NLP app using OpenAI requires less human intervention to solve queries on a real-time basis. If you develop a chatbot using OpenAI, it understands the intent and context behind customer questions to offer personalized responses. This leads to improved customer experience and satisfaction as they receive quick and relevant answers to their inquiries.


    • NLP app solutions are available 24/7 and can handle multiple queries simultaneously
    • Reduces wait times and increases efficiency.
    • Results in increased growth through better customer retention and less staff turnover.
    • Trained to answer industry-specific questions, providing error-free and consistent responses.

    For example:

    If a customer visits a clothing store’s website and has a question about the return policy. They type in the question in the chatbot box on the website. The NLP-powered chatbot analyzes the customer’s query, understands the intent and context behind the question (about return policy), and provides a personalized, context-based response to the customer’s question in real time.

    The customer receives an instant, accurate, and helpful answer without waiting for a human representative. This results in improved customer satisfaction and a positive brand image for the clothing store.

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  3. NLP Finds Actionable Insights from Vast Amounts of Data

    The app utilizes NLP techniques to analyze and process large volumes of unstructured data and extract valuable information that can be used to drive business decisions. The insights generated by the NLP app can be used in various industries such as finance, healthcare, and retail, among others.

    By leveraging NLP algorithms and machine learning techniques:

    • The app can quickly identify trends, patterns, and relationships in the data, helping organizations make data-driven decisions with greater accuracy and efficiency.
    • This results in improved outcomes, increased efficiency, and cost savings for the organization.

    For example: Suppose a company has a vast amount of customer data, including information about customer interactions, purchasing history, and demographic information. An NLP (Natural Language Processing) app solution can be used to analyze this data and extract actionable insights.

    The app solution might identify common customer pain points, reveal correlations between customer demographics and purchasing behavior, and provide insights into which marketing campaigns are most effective. These insights can then be used to inform business decisions and drive growth.

  4. NLP Analyze Customer Sentiments and Build a Positive Brand Image

    Sentiment analysis is an NLP technique for classifying content as positive, negative, or neutral. By processing large amounts of text data from various sources such as chat conversations, social media posts, customer feedback surveys, etc, NLP applications can help companies understand their customer’s requirements, likes, dislikes, and overall sentiment towards their brand.

    By analyzing customer feedback, NLP apps can identify common customer pain points and cater to their concerns, preventing negative reviews or experiences from spreading and causing harm to the brand’s reputation.

    For example, a software company struggles to monitor and react to these opinions due to the high volume of posts. The company uses an NLP app to analyze customer feedback and identify any common complaints or issues with its product. This allows the company to take proactive measures to address these issues before they escalate and result in negative reviews or customer churn.

  5. NLP Apps are Quick to Develop with OpenAI-trained Models

    Developing NLP applications can be challenging, especially for businesses that do not have an in-house technical team. The complexity of natural language processing and the vast amount of data involved make it difficult to build NLP applications that work effectively. However, OpenAI has made it possible to simplify the process of building NLP applications.

    OpenAI has trained NLP models like ChatGPT3, DALL.E, and Codex that businesses can use to develop their own applications. These models are trained on large datasets which enables them to

    • To understand and process natural language in a way that resembles human language processing.
    • Perform different tasks like question answering, sentiment analysis, text generation, and more to answer easily to customer queries.

    This is why businesses are increasingly using the pre-trained model rather than coming up with their models from scratch. It makes the applications reliable for a bigger variety of use cases to reduce the cost and increase its speed of operation.

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  7. NLP helps in Identifying User Journeys & Provides a Personalized Experience

    NLP has the ability to understand the intent behind the user’s query and provide context-based responses. This helps in identifying the user’s journey and tailoring the experience accordingly.

    This is done by analyzing the inquiries and interactions of customers with the business’s chatbot, website, and other digital mediums. eCommerce businesses need to keep the customers hooked onto their website long enough to take the purchase call. A combination of previous purchases, search history, context, and more are used by NLP to enrich product recommendations.

    For example, if a user is searching for “men’s running shoes,” the website can suggest running shoes specifically for men and show them relevant product pages. Similarly, if the website has previously recorded that a user has bought running shoes, it can suggest complementary products like running shorts or athletic socks for a more personalized shopping experience.

    By using OpenAI technology in ecommerce, businesses can increase their revenue potential. And integrating this feature into a shopping app or website can not only enhance the user experience but also improve the chances of converting visitors into customers.

  8. NLP Ensures Data-security and Fraud Detection

    Conventional fraud detection methods are based on rule-based systems which may fail to detect new types of fraud.

    NLP-powered fraud detection, on the other hand, employs advanced machine learning algorithms that can analyze unstructured and structured data to identify fraudulent activities. Fraudulent activities like phishing, account takeovers and unauthorized transactions are becoming more sophisticated and harder to detect.

    The NLP algorithms can understand the context, sentiment, and tone of customer interactions with the bank, e-commerce websites, and other financial institutions. This helps in detecting any suspicious activity and also reduces false positives which are a common issue with rule-based systems.

    For example, a bank uses NLP algorithms to analyze customer transactions and detect unusual patterns that might indicate fraudulent activity. The system can be trained on historical data to learn what constitutes normal behavior and then use this information to flag transactions that deviate from the norm. This helps the bank quickly detect and prevent fraud, reducing its financial losses and protecting its customers.

Frequently Asked Questions About Developing NLP App Using OpenAI

What kind of NLP applications can be built using OpenAI?

There are various NLP applications that can be built using OpenAI, some of which include:

  • Chatbots and virtual assistants: OpenAI can be used to build conversational AI systems like chatbots, virtual assistants, and customer support systems that can understand and respond to customer queries.
  • Sentiment analysis and opinion mining: OpenAI can be used to implement sentiment analysis algorithms that can classify text as positive, negative, or neutral.
  • Text classification and categorization: NLP applications built with OpenAI can assist with text classification and categorization to understand customer requirements, likes, and dislikes.
  • Question-answering systems: OpenAI can be used to build Q&A systems that can answer customer questions in an error-free manner.

Which programming languages are supported by OpenAI?

OpenAI provides API support for several programming languages including

  • Python
  • Swift
  • Ruby
  • Node.js
  • JavaScript

However, the most commonly used programming languages for building NLP applications with OpenAI are Python and JavaScript.

What is the feasibility to integrate OpenAI NLP models into the existing application?

Yes, you can integrate OpenAI NLP models into your existing application. OpenAI provides APIs and pre-trained models that can be easily integrated into your application through various programming languages, such as Python and others. By using these APIs and models, you can add NLP capabilities to your application and provide features like text classification, sentiment analysis, and machine translation.

Is OpenAI scalable for large-scale NLP projects?

Yes, OpenAI is designed to be scalable for large-scale NLP projects.

OpenAI’s GPT models are designed to handle large amounts of data and can be fine-tuned to adapt to specific use cases. Additionally, OpenAI’s cloud platform provides easy access to the models and provides scalability to handle the computational demands of NLP tasks.

Let’s Develop NLP App Using OpenAI

OpenAI provides access to the latest and most advanced NLP models, which can help companies extract actionable insights from vast amounts of text data, understand customer sentiments and provide personalized experiences, detect frauds, and secure sensitive data. With OpenAI’s support for various programming languages and the ability to integrate its NLP models into existing applications, companies can quickly and easily build NLP applications that meet their specific needs.

At Space-O Technologies, we help you take advantage of these benefits. Get guidance from an experienced AI consultant and support as you develop NLP apps using OpenAI. Get in touch with us.

Bhaval Patel

Written by

Bhaval Patel is a Director (Operations) at Space-O Technologies. He has 20+ years of experience helping startups and enterprises with custom software solutions to drive maximum results. Under his leadership, Space-O has won the 8th GESIA annual award for being the best mobile app development company. So far, he has validated more than 300 app ideas and successfully delivered 100 custom solutions using the technologies, such as Swift, Kotlin, React Native, Flutter, PHP, RoR, IoT, AI, NFC, AR/VR, Blockchain, NFT, and more.