How to Build an AI Chatbot From Scratch: A Complete Guide (2024)

The business infrastructure is growing at flash speed. In the past decade, numerous trends and technologies have elevated the business competition so much that almost every company faces a run for its money at certain times. Technologies like AI, Big Data, 5G, IoT, and many others are key promoters of business growth.

One of the technologies that have significantly impacted the business landscape is chatbots, aka AI chatbots. Though many of you are aware of it, if not, do look for a chat window on a company website next time. The mini box on the bottom right of the window is a nudge from the chatbot.

An AI chatbot is of use for business in numerous ways, and its demand is increasing. A clear proof of that is the chatbot market. As per the stats, the chatbot market is expected to reach the $1.25 billion mark by 2025.

So, if you plan to harness the ability of an AI chatbot for your business, read this guide carefully!

What is an AI chatbot?

An AI chatbot is a program that leverages the power of AI and numerous other technologies and data to provide appropriate human-like responses to its users. The chatbot aims to interpret the natural language queries from the users and generate appropriate responses in return.

AI chatbots have applications in various application domains, such as information retrieval, customer service, virtual assistants, etc. Some of the best examples of AI-based chatbots are Slush, Cortana, Siri, etc. If we go onto some advanced chatbots, they are ChatGPT, Google Bard, Jasper, etc.

Why you should build an AI chatbot?

Here are some key reasons why you might want to build an AI chatbot with real-life examples.

Provide 24/7 customer support

Chatbots can handle customer queries and provide assistance around the clock, improving customer experience and reducing wait times compared to human agents alone. Spotify's 24/7 AI chatbot instantly assists users with password resets, troubleshooting, FAQs, and account info retrieval, fielding 83% of queries cost-effectively.

Save costs

Chatbots are generally less expensive to Deploy and maintain than hiring human agents for customer support roles. They can handle many routine queries cost-effectively. Aeromexico's chatbot "Aerobot" allows customers to check flight status, retrieve booking information, and get answers to common queries, reducing call volumes to human agents by 30%.

Increase sales and lead generation

Chatbots can qualify leads, provide product information, and guide customers through the sales process to drive more conversions. Pizzahut's chatbot "upsells" things like desserts and drinks after taking a pizza order. Pizza Hut reports around 70% of their total online order traffic now comes through the chatbot ordering channel.

Improve efficiency

Chatbots can handle multiple conversations in parallel and retrieve information quickly from databases, increasing efficiency over humans for certain repetitive tasks. HDFC Bank's chatbot "Eva" can pull up over 8 years' worth of customer policy details and transaction history in a few seconds to resolve queries faster.

Collect customer data for a marketing campaign

Chatbots log all interactions, providing data that can be analyzed to improve products, services, and the chatbot itself over time. Domino's pizza-ordering chatbot collects data on popular topping combinations, busiest hours, etc. which helps them optimize production and increase upsell opportunities.

Provide consistent responses

Unlike humans, chatbots will provide consistent, on-brand responses every time based on their training data. The CDC's chatbot provides consistent, verified information about COVID-19 symptoms, testing, and the latest guidelines directly from the authoritative source.

Integrate with services

Chatbots can integrate with other systems like calendars, knowledge bases, CRMs, etc. to provide a seamless, automated experience. Marriott International's chatbot integrates with multiple services and APIs to provide a seamless experience for everything from booking to managing a guest's entire stay.

Types of AI chatbots - Latest and most relevant

There are several types of AI chatbots, with new variations and capabilities emerging frequently as the technology advances. Here are some of the most relevant and latest types:

Retrieval-based chatbots

These chatbots use predefined responses and rules to provide answers from a knowledge base. They are relatively simple but can be effective for narrow, well-defined domains. Examples: FAQ chatbots, and customer service chatbots.

Generative chatbots

These use natural language processing (NLP) and machine learning to understand queries and generate new, contextually relevant responses. They can handle more open-ended conversations. Examples: Anthropic's Claude, Google's LaMDA.

Task-oriented chatbots

Chatbots are designed to assist with specific tasks like booking tickets, making appointments, or providing recommendations. They use NLP and may integrate with backend systems. Examples: Travel booking chatbots, and scheduling assistants.

Conversational AI assistants

More advanced chatbots that can engage in freeform conversations, understand context and intent, and assist with complex queries across domains. Examples: Apple's Siri, Amazon's Alexa, Google Assistant.

Chatbots with multimodal interfaces

These combine conversational AI with other input modes like touch, voice, vision, and augmented reality for more natural interactions. Examples: Chatbots in AR/VR environments.

Emotionally intelligent chatbots

Incorporating emotional AI to detect and respond appropriately to human emotions and build rapport. Examples: Mental health counseling chatbots.

Chatbots for specific vertical industries or domain-specific

Chatbots optimized for use cases like healthcare, finance, e-commerce, etc. Examples: Medical diagnosis chatbots, and banking chatbots.

As AI capabilities advance, we'll likely see even more specialized and multimodal chatbot types emerge to provide seamless, intelligent digital experiences across industries.

Components for building an AI chatbot

As easy as it may seem to give the command to a chatbot and get the desired result, the actual work is much more complex in the backend. The working of an AI chatbot has numerous technologies and components backing it. Let’s take a look at those components!

There are two primary categories among which the A chatbot components are divided!

The UI components

Everything related to what a user sees and experiences comes in UI components. UI components include

• The user interface has all the visual components like buttons, text boxes, fields, etc.
• The user experience component includes the way things happen on the screen, like the navigation, animations, etc. Anything that improves the feel of the website.
• Conversation design is the third and the most crucial UI component. It focuses on developing the part about how the chatbot is going to interact or communicate with the users. The elements of a conversation design are flow and scripting. The flow part further includes context, entities, and intent that decide what the chatbot will say. The scripting part develops the chatbot's personality (like how the chatbot says something). In short, the conversation design includes uncovering conversation paths, responses, and fonts.

The functional components

The functional components behind an AI chatbot are much trickier than the UI components. Here are the functional components of the AI chatbot!

• Natural language processing is the most critical part of an AI chatbot. Generative AI and NLP help the chatbot understand user inputs, whether it is text or voice.
• Machine learning algorithms ensure that the chatbot is trained on the data. It is done via supervised and unsupervised learning.
• The knowledge base is the information repository that holds the information required by the chatbot to answer user queries. It can be product information, FAQs, etc.
• Dialogue management is another functional component of the AI chatbot. Dialogue management checks the flow of the conversation while taking care of the context, intents, and responses.

How to build an AI chatbot from scratch

Ok, let’s come to the most critical part, how to build a chatbot from scratch.

1. Begin by planning the chatbot

The first step to building an AI chatbot is planning. Planning involves several things, such as defining its purpose, scope, tools to be used, conversation flow, features, etc.

You need to discover the kind of questions it will answer and how it will answer them. Further, you need to know about the conversational flow of your chatbot to ensure better UX. Lastly, finalize the tools and frameworks in the planning stage along with the features of the chatbot. You can go for various programming languages like Python, Javascript, etc., on top of frameworks like Dialogflow, IBM Watson Assistant, Amazon Lex, etc.

2. NLP and ML

Natural Language Processing and Machine Learning are the backbones of Artificial Intelligence technology. The collaboration of both makes the chatbot fit for usage. NLP ensures that the chatbot interprets the user's requests correctly. As users tend to use slang and idioms in their natural language, NLP is trained to understand this via methods like Sentiment Analysis.

In contrast, Machine Learning is a technology that enables a chatbot to learn over time by studying and analyzing the data. With the increase in data and time, the chatbot becomes better as it can reply to users more accurately. Furthermore, the chatbot can handle complex requests. Techniques like neural networks, decision trees, and reinforcement learning can be used to implement machine learning in an AI chatbot.

3. Backend and UI

The backend of the chatbot is the part where all the functionalities reside. The backend of the chatbot is responsible for receiving the request, processing it, and generating the response. As user requests can be of various types, you have to develop programs and algorithms that interpret the user's prompts and generate appropriate responses.

This is where the programming languages like Python, frameworks like Google Dialogflow, and platforms like Chatfuel come into the picture. All of these are used to develop the backend of the chatbot. You may also integrate APIs, databases, or other systems based on the required functionality.

The other part is developing a user interface. A user interface should be visually appealing and interactive so that the user does not get bored. You are free to develop a new UI entirely or integrate with platforms like Facebook, Telegram, or Slack.

4. Integration and testing

Integration of the chatbot deals with integrating it with other systems like CRM, email marketing systems, e-commerce, etc. You can integrate our chatbot with these systems and with technologies like NLP, voice recognition, sentiment analysis, etc., to provide it with the required functionality.

After integration with all the required systems comes the testing part of the chatbot. The testing part ensures that your chatbot responses are appropriate and are not misspelled. You can create various test cases or use real-time user data to check if the chatbot provides the required and accurate responses. Tools for AI chatbot testing include TestMyBot, Botium, Zypnos, etc.

5. Deployment and monitoring

Once you are satisfied with the AI chatbot, deploy it for public use and notice its working and performance. There are various media to deploy your chatbot. You can deploy it on your servers, the cloud, or a chatbot development platform. All of these models have their set of pros and cons.

Further, you can use an analytics tool to track and analyze critical parameters like performance, user interactions, bottlenecks, etc. Based on the analysis, you can make improvements to the AI chatbot.

How much does it cost to build an AI chatbot?

Well, the cost of building an AI chatbot can vary significantly depending on several factors such as types of chatbot, development processes, integrations, model training data, and multimodel interfaces. Explore them in brief:

Type of chatbot

The complexity and capabilities of the chatbot play a big role in determining costs. A simple retrieval-based chatbot with predefined responses will be less expensive than an advanced generative chatbot using large language models.

Development approach

Building a chatbot from scratch using internal resources requires significant investment in AI expertise, data labeling, and computing infrastructure. Using off-the-shelf chatbot platforms/APIs or engaging a chatbot development company reduces upfront costs.

Integrations

If the chatbot needs to integrate with existing systems like customer databases, knowledge bases, calendars, etc., integration costs can add up quickly.

Training data for generative chatbots

The volume, quality, and complexity of training data required to achieve the desired conversational abilities directly impact costs.

Voice/multi-modal Interfaces

Adding voice interfaces, multi-lingual support, or advanced multi-modal capabilities increases development complexity and costs.

Conclusion

Chatbots have become a pivotal element of every business process today. And this has led to the advancement in numerous technologies racing to elevate the level of chatbots. The examples of ChatGPT and Google Bard are clear proof that the chatbot industry has witnessed a paradigm shift. In a scenario like this, for businesses that are still following primitive practices to serve their customers, it is time to invest in an AI chatbot.

Are you a business owner looking for an AI chatbot to streamline operations, boost sales, and enhance customer experience?

Book Your Free 45-Minute Consultation with Our AI Experts Today!

During this personalized consultation, our team will provide:

•  High-impact chatbot use cases for your business

• Guidance on design, build, and deploy a chatbot solution

• Roadmap to integrate chatbots into your existing systems