Building Chatbot with NLP – The Ultimate Guide for 2025

Previously chatbots used to be a gimmick with no practical benefits, but just a digital tool to experiment with. Traditional chatbots were complex and more robotic. However, what we see is an NLP chatbot, which can understand and conduct complex human conversations with their users.

Powered by NLP (Natural Language Processing) and AI technologies, these chatbots can conduct flexible conversations to achieve a goal – like troubleshooting a technical solution or selling a product – instead of interacting based in a brittle questionnaire style. Developing and maintaining an NLP based chatbot is of course a money, time, and effort-draining job. But they are essential to ensure that your customers always have access to relevant information.

Let’s discuss more about chatbots and NLP in the following blog post.

What is an AI NLP chatbot?

An NLP chatbot is an AI-powered conversational software solution that can mimic human-like conversations with the users. These chatbots are either voice-based and text-based using the NLP technology to understand the intent of a message, extract necessary information from it, and generate a helpful response for the users.

The NLP based chatbot we see nowadays are LLM agents – software provided by LLMs – and – customized by a chatbot builder. By using LLMs like OpenAI’s GPT, it is much easier to build a GPT chatbot for your business.

What is the key difference between an NLP chatbot and a rule-based chatbot?

The key difference between an AI NLP chatbot and a traditional chatbot is they use AI technology to mimic human conversation – while traditional chatbots do not use AI with less flexibility.

Rule-based traditional chatbots are engineered to follow conversational rules set up by their builders strictly to generate responses. If a user inputs a specific command, then it will provide a performed response. However, if the query falls outside of these rules, the rule-based chatbots will be unable to resolve the query.

While an NLP chatbot can,

1. Understand Natural Human Language

Unlike rule-based traditional chatbots, an NLP based chatbot can understand and interpret natural human language.

It will let users send a message like they are communicating with another human, while chatbots using NLP decipher the meaning of it, which includes:

• Determining whether the message is an intention or a question.
• Understand the grammatical mistakes and typos.
• Register the emotions of the users based on their language and tone.

It makes the NLP-powered chatbots closer to the sphere of natural human interaction, while a rule-based traditional chatbot can accurately respond to a set of number of questions.

2. Do more than just answer questions

If a user asks a question to a traditional chatbot and it consists of any unexpected inputs, it can lead to a conversational dead-end.

Since traditional rule-based chatbots are based on their strict programming, conversing with them often feels like questionnaires like “How can I help you today?”, “What is your budget?”, or “Which model are you interested in?”

While an NLP based chatbot can adapt to natural conversational cues to hold a full and complex conversation with users by mimicking human tones.

3. Improve continually

Traditional rule-based chatbots can only improve if their programmers or builders add more rules to them. While an NLP chatbot can improve itself using the data provide by its users.

This feature can make NLP-powered chatbots better at understanding different ways to generate intent or questions. The more they converse with users, the better they get at holding conversations and understanding user queries.

Understanding the NLP chatbot – what are they made of?

Understanding chatbots using NLP come with a wide array of acronyms – although they all are related but indicating a specific aspect of communication between humans and machines.

They are as follows:

1. Natural Language Processing (NLP)

NLP (Natural Language Processing) is a branch of artificial intelligence (AI) focusing on the natural language interactions between humans and machines.

The NLP technology in an NLP chatbot enables it to interpret and respond to natural human language in a useful and meaningful way.

2. Natural Language Understanding (NLU)

NLU (Natural Language Understanding) is a subfield of NLP, focusing on the machine’s ability to understand the intent behind the inputs made by the users.

NLU covers tasks like recognizing the intent, extracting entity, and analyzing the sentiment component – allowing the NLP based chatbot to understand the text written by a human.

3. Natural Language Generation (NLG)

NLG (Natural Language Generation) is another subfield of NLP, focusing on making the machine’s response as contextually coherent and appropriate as possible.

NLG includes tasks like determining the content (deciding on how to respond to the queries made by the users), planning sentences, and generating the final text output to respond to the users.

An expert guide on how to develop an NLP chatbot for your business

Building a chatbot for your business process is of course a time an effort-draining process. But that does not mean that businesses should not leverage them for bettering their procedures.

Tokenizing, normalizing, identifying entities, parsing dependency, and generating responses are the 5 initial stages for an AI NLP chatbot to read, interpret, understand, create, generate, and send a response to the users.

The following is how NLP should be integrated in chatbots:

1. Business Logic Analysis

This stage is essential for the chatbot development team to comprehend the requirements of their clients.

It involves the following tasks:

• Conducting a discovery phase
• Examining the competitive market
• Defining the necessary features for your future chatbot
• Constructing the business logic for your future chatbot product

2. Technology Stack and Channel

For building an NLP chatbot, it is preferable to use Twilio as a basic channel. However, if you are planning to construct text chatbots, Viber, Telegram, or Hangouts would be the best channels.

The following are the most prominent and widely used technologies for developing chatbots with deep NLP tools:

• Pandas A software library for chatbot analysis and data processing using Pandas for the Python programming language.

• Python A programming language to develop NLP architecture for your chatbot.

• Twilio A web service API allows your chatbots to pragmatically make and receive phone calls, perform other communication tasks, and send and receive text messages.

• SpaCy A sophisticated open-source library for NLP allows your chatbot to clarify the user intent using a more comprehensive language library.

• TensorFlow A frequently used library for various tasks that involve machine learning and neural networks to allow your chatbots to interpret interactions better.

• Viber, Telegram, or Hangout APIs Tools to integrate your NLP based chatbot with your websites and messaging apps.

3. Chatbot Development and NLP Integration

Building the client-side chatbot and connecting it to the provider’s API are the primary 2 phases in developing a machine-learning chatbot for your business.

Once you are done with it, you can integrate the AI and NLP technologies with it to expand its knowledge through ech and every interaction with humans and making it an AI NLP chatbot.

Here’s how:

• Tokenizing This phase involves breaking up the texts into small chunks (aka “tokens”) and deleting punctuation.

• Normalizing The bot normalizes the texts by searching for common typos, misspellings, or slang in the text.

• Recognizing Entities Upon normalizing the texts, the NLP chatbot attempts to understand what is being said in the texts. Thus, it can recognize “North America” as a “region,” “Google” as a “firm,” or “72%” as a “proportion.”

• Parsing Dependency Then, the chatbot will divide the sentence into nouns, objects, verbs, common phrases, and punctuations.

• Generating the Response Lastly, the chatbots generate a number of responses depending on the data gathered in the previous phase and choose the most appropriate ones to respond to the users.

4. Testing the Chatbots

This is the final phase, where the chatbots are asked questions that it has been taught using the NLP technology to answer once it is ready. If required, the chatbots might be tested manually to ensure that they provide more accurate response by gathering more data. It can further help you to figure out if your NLP based chatbot development process is at par.

5. Deploying the Chatbots

One of the best aspects of developing NLP-driven chatbots is that you can deploy them across any messaging channels or platforms.

You can even deploy your customized NLP-powered chatbots on your social media channels to provide your customers with the same experience or service across multiple channels and platform-specific assistance.

How can investing in an NLP chatbot benefit your business?

Implementing a chatbot powered by AI (Artificial Intelligence) and NLP (Natural Language Processing) can help your business attract more customers, improve the status of your website, and save you time.

NLP has a long way to go, but it already holds a lot of benefits for business with its applications in chatbots. Here’s how:

1. Free Translation

The capabilities of an AI NLP chatbot involves translation, allowing organizations serve users in multiple languages without any extra cost.

NLP-powered chatbots are usually built using LLMs (large language models) that can function across languages. For example, ChatGPT can be used in 80+ languages.

2. Scalability

Chatbots using NLP technology allow companies to scale a degree by taking over the bulk of user conversations.

NLP-driven chatbots can also handle a large number of simultaneous queries without any issues, speed up the processes, and complete a wide range of tasks reliably.

3. Cost Reduction

Integrating an NLP chatbot in your business process is cost-effective, which empowers companies to develop NLP-powered chatbots without spending a lot of money.

Upon proper implementation, NLP-backed chatbots can allow organizations to enjoy a positive ROI by automating conversational tasks through NLP and AI.

4. 24/7 Customer Support

24/7 availability is another major benefit of implementing NLP-powered chatbots into your business process.

Also, since these chatbots can handle man interactions from start to finish, you do not need to hire many employees for customer support.

By being active 24/7, NLP-driven chatbots can build a list of leads or customers at any time of the day.

5. Integration Capabilities

You must integrate the existing systems and platforms of your company into your chatbot for high-quality outcomes.

An NLP chatbot can take actions in systems like sending an email, updating a CRM, or notifying an employee, leading to seamless integration into the existing business processes.

6. Employee Support

By using NLP-powered chatbots, your organization can automate tasks that would otherwise take a lot of time and effort from your employees.

Implementing chatbots using the NLP technology can schedule meetings, take customer support calls, conduct analyses, and then deliver the results in a report.

It can further free the time of your employees and allow them to focus more on higher-level processes – or the ones that require higher levels of creativity, strategy, or empathy.

Real-life usages of NLP chatbots – examples across top companies

Thanks to its flexible nature, the NLP based chatbot is used by top companies across diverse industries.

The following are some of the real-life usages of NLP-powered chatbots by top companies worldwide:

1. Bank of America's Erica

Bank of America uses a chatbot named Erica that provides personalized financial guidance to users using the NLP technology. It can further help users to track spending, manage their bank accounts, pay bills, and more.

Results show that Bank of America’s engagement increased by 60%, as Erica drove about 56 million engagements every month by the 1st quarter of 2023.

2. H&M's Kik Chatbot

Popular fashion brand H&M uses an NLP chatbot named Kik to provide fashion recommendations and advice to its users. By using the NLP technology, it understands the users’ requests to provide personalized styling tips.

Result shows that H&M’s click-through rate increased up to 8% while the click-through rate was 6% through email marketing. Kik also drove about 86% of engagement rate with users spending an average 4-minute interaction with the chatbot.

3. Uber’s chatbot

Globally renowned cab booking app Uber has launched a chatbot allowing users to book a ride via WhatsApp without even downloading or opening the Uber application. The NLP-based Uber chatbot can suggest you rides and the best deals to make your commute easy and hassle-free.

As per Uber, the chatbot integration has increased their sales and improved their customer satisfaction.

4. Mastercard’s KAI

Globally renowned Mastercard has also launched its chatbot named KAI to help their customers with their financial management and planning. It can provide the users with personalized financial advice based on their financial goals and spending patterns. Alongside that, KAI can also offer real-time assistance with numerous Mastercard services like balance enquiries and card activations.

Results show that KAI has increased the brand reputation score of Mastercard by 12 points with an average customer engagement rate of 70%.

5. Starbucks’s My Starbucks Barista

Starbucks has launched a chatbot named My Starbucks Barista as a successful marketing tool to provide a more convenient and personalized customer experience, leading to increased loyalty, engagement, and sales.

Results show that Starbucks showed a 20% increase in customer time spend on the chatbot and it is accounted about 10% of all mobile orders in the US in 2019.

6. Wall Street Journal’s Chatbot

Globally known brand Wall Street Journal uses an NLP chatbot for marketing purposes, by collecting personal data and offering personalized content based on that to improve client experience, increase customer engagement and satisfaction rate.

The chatbot of Wall Street Journal has been received multiple recognition like 2018 Webby Award for being the “Best Chatbot in the News and Politics.”

7. Domino's Dom

The leading pizza chain Domino has launched an NLP-powered chatbot named Dom on Facebook Messenger to allow customers order food with just a few clicks. Dom syncs the Google accounts of the customers to allow them to order their favorite pizzas from any device. Dom can further recommend users what type of pizza they would love from toppings to curst types based on their purchase history and past preferences.

Results show that introducing Dom has reduced Domino’s live agent costs by USD 500K by handling over 1.5 million conversations since its launch.

Final thought

Data shows that companies that will survive the next 5 years will be AI-enhanced. Businesses are continually looking for ways to improve customer experience by providing relevant answers based on user queries. Integrating an NLP chatbot into your business process can allow you to scale your business process with a cost-effectiveness that was impossible previously. Developing NLP-powered AI chatbots can help you streamline and automate your customer service with the most agile platform through the best and most compatible high-end NLP-driven chatbot development company like ConvexSol. We hope this blog post can help you understand everything about this.