How to Build a Chatbot with Deep NLP?

how to build a chatbot

Last Updated on September 5, 2023 by Team Experts

If you talked about smart cars, AI and trained robots a few years ago, people would not take you seriously. It sounded too good to be true and not practical. Look at how technology has improved since then. However, what we need to understand is that people will only embrace technology if they understand it.

Chatbots are an excellent example of technology that uses AI and NLP to make our life easier. Dr. Joseph Weizenbaum, a professor at MIT, developed the first chatbot in 1966. This chatbot was named ELIZA. Companies have created advanced chatbots since the early 2000s.

These advanced chatbots offer a variety of benefits for companies. What are the benefits?

  • Contextual AI-driven support for website visitors
  • Improved customer data analysis
  • Increased customer engagement and sales
  • Better lead generation, qualification, and nurturing
  • Time savings
  • Chatbots can be applied to various industries
  • Lays the foundation for conversational marketing strategy

As per Business Insider, global spending via customer retail chatbots will rise to $142 billion in 2024 from $2.8 billion in 2019. Chatbot development is critical because customers in various industries like healthcare, wellness, banking & finance demand 24/7 assistance. Furthermore, 18.55% of businesses that use chatbots generate more high-quality leads.

However, the chatbot development cost can be high. Developing a custom chatbot can cost you anywhere between $-12,000 and $160,000 or more in some cases. This blog will discuss how to build a chatbot with deep NLP.

What is NLP (Natural language processing)?

Natural language processing is a branch of computer science that incorporates aspects of mathematical linguistics, machine learning, and AI. With this technology, computers understand voice and text like humans. Along with interpreting the spoken/written material, it also tries to understand human emotions and objectives.

Examples of NLP include voice-enabled GPS systems, speech-to-text software, digital assistants, customer service chatbots, and others. NLP also plays an integral role in developing solutions that aim to streamline business operations, boost employee productivity, and simplify indispensable business procedures.

Applications of NLP

  • Natural language understanding – Human language is complex with flexible rules. NLP helps computers comprehend human language by feeding it with information related to syntax, structures, sentences, etc. Such information is compiled by observing and documenting languages. Hence, when a human asks questions or types a query into a form, he gets answers from the bot.
  • Sentiment analysis – By conducting a sentiment analysis, the bot understands human emotions. Such an analysis is helpful for businesses to enable social listening on the internet. Wherever businesses introduce new products/services, NLP helps them know how people feel about their offerings.
  • Machine translation – Through machine translation, you can get complex text translated into another language using a translation app. Many translation apps are so good that they translate every word in the text without losing its meaning/essence.
  • Chatbots – Chatbots aka virtual assistants that you can use at events and websites to automate transactions with visitors while saving time, money, and resources. Custom chatbot development costs can be high but they are very effective.
  • Semantic parsing – Machine learning and NLP parse human language into a form that machines understand.

Types of Chatbots

Chatbots can be of different types. The chatbot used for the healthcare industry is vastly different from the one used for the banking industry. Bear in mind the different types as you build these chatbots.

  • Menu/button-based – These bots answer FAQs and customer support queries.
  • Linguistic bots (rule-based) – Such rule-based chatbots use if/then logic to create conversational flows. By creating language conditions, the computer learns words, their order, synonyms/antonyms, and how humans normally frame questions. Based on the input, the computer answers questions accurately.
  • Keyword recognition-based – These chatbots use customizable keywords, plus an AI application & NLP, and deliver an appropriate response to the user’s question.
  • Machine learning chatbots – The machine learning chatbots also known as contextual chatbots learn constantly from user responses and grow themselves. With contextual awareness & self-improvement features, these chatbots analyze user queries and how they ask them.
  • Hybrid model – Combine basic rule-based chatbots with complex AI chatbots and what do you get? – hybrid chatbots.
  • Voice bots – These AI-powered chatbots allow customers to navigate an IVR system using their voice. Interestingly, there is no need to listen to menus and press corresponding numbers on their device keypads.

Deep NLP Chatbots – Custom Learning Vs. Ready-made Solutions

When it comes to creating NLP Chatbots, you have two options —custom Learning and Ready-made solutions. You need to decide which one suits your business requirement perfectly. You will be able to take the decision perfectly once understand the difference between the two. Let’s explore Custom Learning vs. Ready-made solutions. 

  1. Readymade Solutions

Using chatbot platforms, you can easily develop a chatbot. A readymade solution is ideal for those who do not require complex and advanced technological solutions.

The main advantages of these readymade solutions are that they are quick, easy and require no coding experience. At the same time, the chatbot development cost is minimal and offers easy integration with other applications. If we talk about the downside, they can be hard to customize and in some cases, they may not offer optimal functionality.

  1. Custom Chatbot Development

To create a complex Chabot with personalized API integration, you need to create solutions with custom logic and specific features that cater to your business requirements. How do you build a chatbot using NLP? Let’s find out.

Custom chatbot development has its advantages and disadvantages. Through custom chatbot development, you can create customized chatbots with the ideal features while considering your target audience and their needs.

Choose a team with expertise in a particular technology so that they can create the best. Don’t think your responsibilities are over after developing the chatbot because maintenance is also essential. With the right team, you can ensure proper chatbot development, maintenance and testing.

By employing such a proactive approach, the chatbot remains bug/virus free while delivering optimal results even after technical upgrades.

On the downside, custom chatbot development requires more time and resources. It can take a few hours or many weeks to develop a custom chatbot. Unlike ready-made tools that only require fees, each NLP chatbot feature requires considerable money.

The cost increases further if you don’t have the time or resources to develop a custom chatbot. You may have to outsource the task to an agency that has the required skills and expertise.

How to Build a Chatbot with Deep NLP?

There are various platforms in the market to develop voice chatbots. You can use tools like MindSay, ReplicantVoice, Ideta, Agara, etc. Alternatively, you can use WeChat, Skype, Slack, and Telegram to build text-based chatbots.

Which tech stack would you choose for bot development? The most preferred and widely used technologies are as follows:

  1. Python – Python uses many libraries like NLTK, Spacey, etc. You can use this programming language to build an architecture for your future chatbots.
  1. Pandas – Pandas is a software library developed for Python programming which facilitates data manipulation and analysis.
  1. Twilio – Using Twilio’s API tools, software developers can make/receive calls, send text messages, and communicate with people.
  1. TensorFlow – With TensorFlow’s valuable library, developers can accomplish machine learning and neural network tasks.
  1. SpaCy – SpaCy is an open-source software library that is perfect for advanced natural language processing.
  2. Telegram, Viber & Hangouts – These are useful tools to connect chatbots to your messenger and websites.

Development and NLP Integration

Creating a machine learning chatbot comprises two steps. The first step includes developing a client-side bot and then connecting it with the provider’s API. After the development phase, developers can add NLP chatbots through AI integration.


After developing the chatbot, it is time to test it. Ask questions that the bot has been programmed to answer. In the absence of multiple scenarios, you can ask any number of questions. We need to understand that manual testing has its limitations.

Don’t neglect the testing phase because it will determine how your NLP chatbot will perform. With AI chatbots, you can save time, boost productivity and ensure optimal use of resources. Moreover, more users will visit your website and when they get an excellent user experience, your profits will soar through the roof.

Chatbot Development Cost

What does the estimated chatbot development cost? It depends on your requirements plus the integration and features included in the chatbot. We have come a long way from the basic chatbot that Weizenbaum developed in 1966. Chatbot technology is constantly evolving as we speak. As the complexities increase, costs will also increase.

Will you hire freelancers or build a team of in-house chatbot developers? Do you plan to outsource the task to a chatbot development agency? How many features will you incorporate into the Chabot?

Read more: How To Develop AI-Based Application – A Step By Step Guide


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Anil is an enthusiastic, self-motivated, reliable person who is a Technology evangelist. He's always been fascinated at work especially at innovation that causes benefit to the students, working professionals or the companies. Being unique and thinking Innovative is what he loves the most, supporting his thoughts he will be ahead for any change valuing social responsibility with a reprising innovation. His interest in various fields and the urge to explore, led him to find places to put himself to work and design things than just learning. Follow him on LinkedIn

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