How to Build an AI Chatbot from Scratch

Olha Kanishcheva ML/NLP Engineer, Researcher

Ivan Kuzlo Engineering Director

By 2027, chatbots are expected to become the leading customer service channel for about a quarter of businesses. The reasons are clear: chatbots can readily understand a user’s intentions, recall their context, and provide them with relevant answers and actionable recommendations. But as Uma Challa, a senior analyst at the US-based research and advisory firm Gartner, notes from the beginning that chatbots can deliver benefits and results only ‘when designed correctly’.

To make interaction with a chatbot natural, accurate, and efficient, a lot of effort is needed: choosing the correct programming language and natural language processing (NLP) framework, building an architecture, then designing an effective conversation flow with external knowledge bases or data sources.

In this article, we will do what not every chatbot development company dares to do: we will explain the technical side of building an AI chatbot in detail and – most importantly – simply. If you plan to implement an AI-based chatbot to help your business communicate, then understanding the underlying technology should be your first step.

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Article Highlights:

• Picking the right AI-driven models like GPT-4 via LangChain can enable flexible and dynamic conversations;
• Using NoSQL databases allows chatbots to store and quickly retrieve unstructured data such as chat logs and user preferences to offer a personalized experience;
• Real-world testing will help you understand how your users formulate questions, allowing you to train your chatbot to recognize intent faster.

Building a Strong Technical Foundation

A successful custom chatbot requires a solid tech foundation, and so we begin our discussion of chatbot development methodology with choosing a programming language, frameworks, and cloud services. 

Programming Languages: The Brain Behind the Bot

The programming language a chatbot is built with determines how it will process user input, generate responses, and interact with databases or APIs. Therefore, before answering the question of how to build an AI chatbot, try to determine the scalability of your future chatbot, its AI capabilities, and integration requirements. With clear requirements, you can choose a suitable programming language with confidence.

Python

Python is the most popular language for AI assistant development due to its extensive libraries and frameworks supporting machine learning, natural language processing, and automation. Besides all this:

• Python works flawlessly with NLP frameworks such as Rasa and LangChain;
• Python supports sentiment analysis based on deep learning and speech understanding.

Python is a good choice for creating an AI chatbot if you need a voice or virtual assistant to analyze sentiment and detect user intent. 

JavaScript (Node.js) 

JavaScript is ideal for chatbots designed for messaging apps (WhatsApp, Messenger), enabling instant responses.

A widely popular platform for server-side JavaScript, Node.js uses non-blocking (asynchronous) architecture that can handle thousands of chatbot users simultaneously, and is perfectly combined with web frameworks like Botpress and Microsoft Bot Framework.

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Java

If you are looking to create a large-scale corporate chatbot that requires integration with existing business systems, we recommend that you pay attention to Java.

Due to its high stability and backward compatibility, as well as the ability to handle complex workflows, Java is usually chosen for banking chatbots or chatbots that run on internal business systems.

Go and C++ 

Developers usually choose Go and C++ as their programming languages when the chatbot requirements include handling large volumes of real-time transactions and processing massive amounts of data quickly. For example:

• Go is used in chatbots that require low latency and high parallelism, including trading bots;
• C++ is ideal for chatbots that need fast data processing in high-performance applications.

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AI and NLP frameworks: Making Chatbots Intelligent

Understanding user input and generating accurate responses are the main chatbot tasks, and AI along with NLP frameworks are responsible for this ability. There are several types of frameworks, and here’s the difference:

Rasa 

Rasa is an open-source NLP framework that gives you complete control over chatbot development. Rasa’s use of machine learning’s intent recognition to understand users makes it a great option for fast learning in a niche area. For example, a chatbot can quickly learn legal terminology and provide nuanced answers.

LangChain 

LangChain is a framework that works with large language models (LLMs) such as GPT-4 to manage the chatbot’s memory, allowing AI to remember past conversations.

The key feature of LangChain is the chunking approach, i.e., breaking large text fragments into smaller parts. The chunking approach stands out because it’s highly flexible for retrieval-augmented generation (RAG): 

• Instead of breaking text into a fixed number of characters, LangChain can break text semantically, so important ideas stay together and search results become more relevant; 
• LangChain is one of the few tools that takes into account the needs of vectorization, meaning that the chunking process is directly optimized for efficient search and extraction; 
• The chunking function is built into the larger LLM processing pipeline. It works seamlessly with other functions, such as memory, search, and operational management, significantly reducing the need for manual configuration. 

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Dialogflow 

If your business needs to create an AI chatbot from scratch, you have a solution for rapid deployment – Dialogflow. A cloud-based NLP service from Google, Dialogflow is pre-trained to understand common business queries and integrate with Google’s chatbot analytics.

OpenAI API 

The OpenAI API allows chatbots to use GPT-4, making it ideal for tasks that need adaptive, creative, or highly detailed responses. The API also integrates with LangChain for better memory management.

If, for example, your business needs a chatbot to help users solve problems and generate personalized support responses, the OpenAI API is the first framework you should consider.

Cloud Hosting 

To make your own AI chatbot work 24/7, you will need to provide it with the server space to process messages, access databases, and run AI models — simply put, your chatbot needs cloud hosting: think of hosting as a chatbot’s virtual office.

Amazon Web Services (AWS)

AWS is one of the most powerful cloud computing platforms available, offering various AI chatbot services, including AWS Lambda and Amazon Lex.

AWS Lambda allows chatbots to execute code without the need to manage servers. The chatbot runs only when it is needed, making it cost-effective and capable of handling high traffic spikes dynamically.

Amazon Lex (NLP-based chatbots)

When setting your goals to create an AI bot, you might realize your business needs a large-scale bot to handle unpredictable traffic, like a customer service chatbot. In that case, Amazon Lex is one of the best options for you.

Lex integrates seamlessly with AWS Lambda, DynamoDB (a NoSQL database), and Amazon Connect (a cloud-based call centre system).

Lex also provides automatic speech recognition (ASR) and natural language understanding (NLU). You can create an Amazon Alexa-like communication experience with voice and text interaction.

Google Cloud Platform (GCP)

Google Cloud is another leading cloud provider that offers advanced AI and NLP tools for example Dialogflow and Vertex AI. This cloud platform is best suited if your chatbot needs advanced generative AI and machine learning features because:

• Google’s NLP service allows chatbots to process natural language input. It can support text and voice chatbots (e.g. Google Assistant);
• Vertex AI allows developers to train their AI models to improve chatbot responses and use Google’s powerful AI models (e.g. PaLM, Gemini) to generate chatbot dialogues.

Microsoft Azure

If your company relies on Microsoft products like Teams, Office 365, and Dynamics CRM, then Azure is a great tool for you. The framework is designed for deployment across Microsoft products and provides pre-built conversation models for common business use cases. You can also integrate with the Azure OpenAI Service to use ChatGPT responses.

Firebase 

How to create an AI chatbot without manual backend configuration? Your development team can do this with Firebase – a serverless cloud service from Google designed for mobile applications (both iOS and Android) and lightweight chatbots requiring real-time data synchronization. Firebase allows developers to build chatbot logic without the need for managing servers or manually configuring the backend. 

Databases: How Chatbots Store and Retrieve Data

How can chatbots remember past conversations and user preferences? It depends on the choice of database. In general, there are two types of databases that can be used in the chatbot development life cycle: SQL and NoSQL databases.

SQL Databases: Structured Data for Organized Storage

SQL databases store data in rows and columns like an Excel spreadsheet. The database then uses queries to retrieve data quickly (e.g., ‘Find the last 5 purchases of user X’).

NoSQL Databases: Flexible Storage for Dynamic Dialogues

NoSQL databases are designed for unstructured data that changes rapidly, which is ideal for chatbot dialogues. NoSQL databases store chat logs, user preferences, and interactions in real-time and can process millions of messages per second. 

Vector Databases: Finding Relevant Information With AI

Vector databases store unstructured data in numerical vectors and allow AI to find and extract information based on meaning, not exact word matches. That is, vector databases compare semantic similarities between data points.

These databases are essential for chatbots, search engines, and recommendation systems because they help find the most relevant answers, even if users phrase their questions differently.

Set up your chatbot for success from the start – partner with CHI Software! Contact our team

Designing Chatbot Architecture

Remember that you can achieve your business goals much faster if you provide users with an engaging and seamless experience. That’s what chatbot architecture is all about. 

Let’s take a look at the three most important components:

You can’t make an AI chatbot without a proper architecture – it serves as a foundation of your solution.

User interface (UI): First Impressions Matter

A chatbot’s user interface is the first point of contact with customers. If the UI is slow, confusing, or cluttered, users may abandon the conversation before even getting a response.

• Web chatbots, usually created for business websites or customer portals, are distinguished by their visibility and accessibility and most typically appear as a floating chat widget in the bottom corner of the website. 

Web chatbots should match the brand’s style guide and stay easily accessible. They also stand out for their ability to support multimedia, links, and interactive buttons.

• Mobile chatbots use a simple and responsive interface suitable for small screens. The key is to make communication quick and easy by using touch-friendly elements like swipes and voice input.
• Chatbots for messaging apps (WhatsApp, Messenger, Slack) are all about natural conversations. To make interactions smooth, they should support quick replies, emojis, GIFs, and accurately understand user requests. The goal is to make chatting with the bot feel as natural as talking to a real person.

Backend and Integrations: The Power Behind the Chatbot

Don’t think of your chatbot as just a messaging tool; a chatbot can be an actual business assistant that can provide you with a wealth of insights on a regular basis. A powerful backend ensures that your chatbot receives data promptly, processes requests, and also scales.

Why is backend integration important for business? In a nutshell:

• APIs allow chatbots to communicate with external systems, receive real-time information, and automatically perform business operations. For example, if a user asks a question about the status of an order, the chatbot can immediately send an API request to the company’s order tracking system, and when the system returns the answer, the chatbot presents it to the user.
• CRM systems personalize interaction with chatbots based on a user’s data. Through CRM integration, a chatbot receives data on past purchases and user requests and can offer relevant products or send notifications about their availability.
• Databases store chatbot memory to remember past interactions and improve responses. With the help of databases (SQL and NoSQL), your chatbot will have access to any information about each visitor, from transaction history to query history.

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Conversation Flow: How Chatbots Communicate

Сommunication is a chatbot’s main task, so in addition to answering the question of how to develop an AI chatbot, you should clearly understand how these developed chatbots can interact with your customers.

1. The process starts with determining user intentions. Natural language processing (NLP) is used to help the chatbot understand what the user wants by recognizing and interpreting the user’s input.
2. Then, to engage in a dynamic dialogue, the chatbot uses decision trees, similar to flowcharts. They help the chatbot choose the right answer based on the user’s responses. For example, if the user selects ‘Technical support,’ the bot follows up with questions about the specific technical issue.
3. With the help of context management, chatbots are able to remember information. Built-in error handling also offers fallbacks or menu options if the conversation doesn’t go as planned.

Remember: ChatGPT developers can customize your AI-powered chatbot to work in the most complex areas, namely banking, medical or legal fields. Thanks to NLP and the ability to learn from previous interactions, a chatbot can handle complex customer queries and still respond in a structured, informative, and natural-feeling way.

How to Make an AI Chatbot: A Step-by-Step Guide

At CHI Software, we work with companies in industries ranging from e-commerce to finance to help them build chatbots that work for their customers. We know that to build your own chatbot can seem like an overwhelming task. Should you implement an AI-driven assistant capable of contextual conversations? Should it live on your website, WhatsApp, or CRM?

That’s why we’re going to walk you through the entire process of building an AI chatbot and break down the fundamental decisions that companies have to make along the way.

Creating an AI chatbot with CHI Software usually involves these five steps.

Step 1: Setting Up Your Development Environment

When companies first come to us with a request to make an AI bot, we first assess their goals. Do they need a chatbot that simply provides information? Or are they looking for something more interactive and AI-driven?

The choice of programming language and technology stack depends on three key factors:

• Where will the chatbot be used (website, app, messaging platform, or multiple channels)?
• How much intelligence does it need (should it manage context-aware conversations or machine learning-based predictions)?
• How scalable it needs to be (handling 100 or 100,000 users daily)?

If, after answering these questions, you are still not sure which technology stack is right for your business, then CHI Software consulting services will help your company assess its needs and choose the right solution.

Step 2: Implementing NLP and Training the Bot 

A chatbot doesn’t understand human speech – it recognizes patterns and matches them with predefined intentions. The problem is that users don’t always speak in a predictable way. That’s where NLP comes in, along with embedding. 

Embedding allows a chatbot to convert text into a numerical representation that reflects meaning and understands messages, even if they are not completely clear. It’s an important point because one of the biggest mistakes companies make is training chatbots using only formal language.

Of course, you can train your bot on the requests like ‘track my order’, but in most cases, customers may ask something closer to ‘Hi, where are my things?’.

Therefore, you need to be sure that your chatbot can adapt to user requests in real time. For example, generative AI development teams can help you build chatbots that don’t just recognize keywords – they understand context. By integrating advanced AI models like GPT-4, these chatbots enable more natural conversations. They can also rely on RAG to retrieve real-time data from external sources and generate contextually appropriate answers.

Ready to build an advanced chatbot? We’ve got you covered! Our team is here to help. Build your tool with trusted experts

Step 3: Generating Responses

Now that the chatbot understands what customers are asking, it needs to answer appropriately. Companies often ask us: should our chatbot have predefined answers or generate them dynamically? The answer depends on how much flexibility you need.

• Predefined answers (scripted responses): Best suited for FAQs, support requests, and frequently asked questions. For example: ‘Our store is open from 9:00 to 18:00’.
• Dynamic responses (fetching accurate data from the API): Best suited for order tracking, CRM interaction, real-time updates, etc.
• Answers generated by artificial intelligence (conversational AI): Best suited for casual conversations, customer engagement, and AI assistants. For example: ‘Tell me about your skincare routine and I’ll recommend the best products.’
• RAG-generated answers: Your chatbot won’t rely on static knowledge – RAG retrieves relevant data from your database and generates qualified answers, especially for specialized topics.

Step 4: Connecting to External Systems 

To develop a chatbot from scratch that aligns with your needs, you first need to understand how different integrations will benefit your business.

• For example, integrating with a CRM not only allows a chatbot to remember past purchases but also allows your business to be proactive. Using a chatbot integrated with CRM, your company will be able to send personalized promotional offers or notifications based on customer preferences.
• Similarly, database integrations can go beyond storing chat history. They can facilitate real-time analytics, and allow a chatbot to adjust responses based on user behavioral trends or feedback patterns.
• In addition, integration with payment gateways leads to full automation of orders, bookings, or subscriptions.

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Testing & Debugging: Making Your AI Chatbot Fail-Proof

After gathering all the information on how to build a chatbot from scratch comes the next challenge: ensuring your chatbot works smoothly in the real world. To avoid interrupted conversations and misinterpreted messages, we recommend you spend adequate time testing your chatbot.

Unit Testing

Unit testing comes in handy when building an AI chatbot if you want to check specific features.

With unit testing, you can check individual chatbot features separately and combine them before launching.

First, you need to decide what parts to test and then, developers create different scenarios to see how the chatbot handles them. After fixing any issues, it’s a smart idea to run the tests again. This makes sure that the fixes didn’t cause any new problems and that everything works as it should.

User Testing

Regardless of how well you’ve designed your chatbot, you need to be prepared for when users ask something you didn’t expect. For example, instead of saying ‘Cancel my subscription,’ users might write ‘I want to log out.’ Testing your chatbot with real users will help you identify possible misunderstandings.

A/B testing

You can build an AI chatbot from scratch and then launch a slightly different tool (changing only one element is enough) to test conversions.

You can try launching two versions of your chatbot to see which one performs better, as even small changes can increase engagement, reduce bounce rates, and improve conversion rates.

For example:

Version A: The chatbot greets users with the question, “How can I help you today?”

Version B: The chatbot greets users with “Looking for something? I can help!”

Results: Version B received 15% more engagement because it was friendlier.

Troubleshooting Chatbot Performance: Common Issues and Fixes

Even well-designed chatbots can face difficulties in real-world situations. Here are the most common problems companies face, and how we usually solve them.

This is how you can solve the most common challenges of chatbot development life cycle.

Chatbot Misunderstands User Requests

Even after testing and training with real customer language, your chatbot may not fully understand the alternative intentions of users. That’s why we recommend creating an AI chatbot that is flexible with its answers. 

You can generate a few clarifying phrases for such situations, so that your chatbot will be able to ask: “Did you mean X, Y, or Z?” instead of “I don’t understand”.

Slow Response Time

Slow response time is usually caused by insufficient backend optimization or inefficient API calls.

To prevent delays in responses, we recommend to:

• Use caching to store frequently requested data instead of fetching it every time. For example, if users frequently ask about store opening hours, cache the answer instead of querying the database;
• Make sure multiple API calls from your chatbot are executed in parallel and not one at a time;
• Host your chatbot on a massively scalable cloud storage, for example AWS Lambda or Google Cloud Functions.

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Chatbot Gives Inconsistent Answers

If you don’t keep track of your chatbot’s training data properly, some parts of your chatbot may rely on outdated or inappropriate information, leading to inconsistent responses and confusion. You can avoid such situations with the help of reliable source synchronization. You can use tools like MLflow, DVC, or Git to track changes to your training data. These tools will help you ensure that new updates don’t accidentally overwrite crucial past work.

It’s also a smart move to allow users to rate chatbot responses. When users flag incorrect answers, you’ll know which areas need retraining, which will help your chatbot become more accurate and reliable over time.

Conclusion 

Building an AI chatbot is a process far more complex than merely connecting scripts. It requires training a virtual assistant that understands user intentions, integrates seamlessly with business systems, and ultimately improves the customer experience. 

To build an AI chatbot from scratch, you have to go from choosing the right programming language to developing a user-friendly interface, and each of these steps is important for the chatbot’s success. But remember: if you go through all the stages carefully, you will be able to build a chatbot that facilitates meaningful interactions that add value to your business.

If you are considering taking the next step and require expert assistance, CHI Software is here to help. We will create a chatbot that will change the way your business communicates.

About the author

Olha Kanishcheva ML/NLP Engineer, Researcher

Olha boasts a decade-long journey in NLP, currently serving as a researcher at Jena University and a Consulting ML/NLP Engineer at CHI Software. Her expertise extends to various realms of NLP, including text summarization, named entity recognition, and keyword extraction. Olha's Ph.D. thesis explored knowledge representations and information retrieval in librarian systems.

Ivan Kuzlo Engineering Director

Ivan keeps a close eye on all engineering projects at CHI Software, making sure everything runs smoothly. The team performs at their best and always meets their deadlines under his watchful leadership. He creates a workplace where excellence and innovation thrive.