A Step-By-Step Guide to Implementing Your First Chatbot

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Olha Kanishcheva ML/NLP Engineer, Researcher

AI technologies offer us a variety of useful and helpful tools to generate content and analyze large datasets to uncover hidden patterns. However, one particular implementation stands out: AI-powered chatbots. 

Chatbot development has been around for quite a while. Their initial implementation was somewhat underwhelming – but user reception changed as soon as more advanced AI algorithms were introduced. But how do you build a chatbot from scratch? Today, we will help you figure it out.

AI Chatbots: Market and Implementation

Chatbots bring a lot of value to any business, which is reflected in the statistics: the chatbot market size was estimated at 5.4 billion USD in 2023, with an expected rise to 15.5 billion USD by 2028.

The growth of the global chatbot market between 2022 and 2028

Chatbots’ popularity has surged with advancements in generative AI models being applied to technologies that can create human-like text, audio, and video. 

This allows organizations to generate new ideas and release creative products. And since chatbots are powered by artificial intelligence, they have been riding the wave of hype around AI. 

AI at Your Service: How Chatbots Are Shaping the Future of Workplace Productivity Read more

But how exactly do businesses put them to use? Depending on the chatbot’s implementation – internal or external – the answer will vary.

External chatbots aim at customer interactions, such as:

• Customer support, where chatbots answer customer’s questions and provide guidance when needed;
• E-commerce chatbots help customers find products, track shipments and process payments;
• Appointment scheduling for users, as well as reservations;
• Inventory management chatbots automate restocking by contacting suppliers on their own.

Such implementations can increase customer satisfaction and engagement while freeing employees to focus on more important tasks. Additionally, using chatbots for external communication is likely cheaper than staffing a full department of customer support specialists.

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Internal chatbots are used to increase employees’ productivity through automation and streamlined processes. Here are a couple of examples:

• Recruitment and onboarding can be easier with the help of chatbots that provide answers to any potential questions of new employees;
• Employee support, such as technical assistance or requests for day-offs and sick leaves, can be fully delegated to internal chatbots;
• Workplace communication can improve, since chatbots can automatically schedule meetings and notify all of the participants. Additionally, they serve as personal assistants for employees, providing them with suggestions and recommendations.

Now that we know what to expect from chatbots, let’s take a look at our comprehensive guide on how to build a chatbot from scratch.

Getting Started With Chatbots: 6 Steps to Implement Your AI Assistant

The chatbot implementation process will vary from business to business, but the rough outline looks like this:

How to implement your first AI-powered chatbot

Step 1: Conduct Market Research

To start the chatbot development process, you need to have a clear understanding of how your assistant is going to be used. Deciding whether your solution will be for external or internal use determines your first logical step – research.

As with everything in business, research is key to success. Firstly, look at your competitors – how they implemented their chatbots, and what features they include. 

In the case of internal chatbots, your next step is to look into your own employee data. Pinpoint the most common obstacles they face, what processes are taking too long, and whether they can be automated with chatbots.

For external chatbots, you will be more interested in user data. Look at the user journey and outline the most common obstacles they face. 

All this data will give you an idea of what problems you need to solve and whether they are solvable with chatbots. After research is done, you are ready for the next step.

Need help with a proper project discovery and planning? We've got you covered! Let's start together!

Step 2: Determine Key Performance Indicators

Before implementing any tools, you need to define how you will measure their usefulness. Key performance indicators (KPIs) do precisely that. The most popular KPIs to monitor how useful your chatbot is are user metrics and conversation metrics.

User metrics are all about the user’s reception of the chatbot. They include:

• Total number of users that initiated a conversation;
• The number of users trying out the chatbot for the first time;
• How many sessions users completed with a chatbot;
• The average number of daily interactions between users and a chatbot.

Conversation metrics, on the other hand, focus on efficiency. For example:

• Total number of conversations between users and the chatbot;
• Average conversation time;
• How often the chatbot fails to understand a request;
• How many messages the user and chatbot exchange during the conversation.

After KPIs are defined, we can start the chatbot development process.

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Step 3: Choose a Development Platform

There are multiple chatbot development platforms out there, like Botpress or IBM’s Watsonx Assistant. But which one to choose? Here are some tips on how to make the right choice for your business needs:

• Look into what features and functions each platform provides. The main focus should be on natural language processing capabilities, APIs and database integration, and sentiment analysis. If your business covers multiple countries, look into multilingual support, too;
• Check the platform’s compatibility with your existing systems and databases. Depending on your business model, a smart call would be to check integration with popular messengers; 
• Determine the platform’s ability to scale with your business needs. Check if it can handle multiple interactions at the same time without performance loss;
• If your business is constantly adapting to changes, your chatbot should reflect this. Take note of how easy it is to customize your chatbot using the platform.

If you’re interested in AI chatbot development, you should consider our services. We at CHI Software gathered a team of experts in AI development to create a perfect chatbot for your business needs.

With our extensive knowledge, we can create highly customizable solutions that will scale with your business. Whether you are interested in fully custom development or GPT-powered chatbots — we can do it all. 

Step 4: Development

With the platform chosen, you are ready for the development process. It involves two major parts: AI development and chatbot development. Let’s cover them separately.

AI development is a complex process involving many moving parts. To start, you must prepare your data for AI training. This data will serve as a knowledge base for your solution in the future, so make sure you’ve provided your chatbot with all needed information. 

To prepare data, engineers need to clean it, which involves structuring, labeling, and deleting corrupted parts and duplicates. Once the data has been cleaned, it is structured into datasets to be used for AI training.

Next comes chatbot development. Using the development platform, specialists start integrating the AI model into your business as a chatbot solution. Once the process is done, it’s time to move on to the testing phase.

Step 5: Testing

As with any software, you need to test your chatbot to make sure it functions correctly and understands the user’s requests. But how do you do that? Here are several of many more testing methods:

• Unit testing checks every chatbot component by running through predefined test cases;
• Functional testing makes sure that the chatbot performs as intended by simulating user interactions;
• Integration testing focuses on checking the data exchange between the chatbot and external services and is required if your chatbot uses external systems and databases;
• Performance testing is used to monitor how well the chatbot will handle varying stress conditions;
• Security testing is done to check whether the chatbot is safe for users to use.

Ideally, you want to combine various testing methods to ensure your chatbot works as intended. 

Step 6: Launch

With development and testing completed, it’s time to launch your chatbot. While you might not notice a major difference from the start, it won’t be long before you start seeing the first results of your investments. 

Depending on the type of chatbot, you may start to see the first signs of the AI improving itself after a few weeks of learning, with more substantial effects coming around the half-a-year mark.

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Step 7: Post-Launch Support

After launch, you need to continually monitor the chatbot’s performance; how fast it responds to queries, and the mean time for a response. Check KPIs to monitor the chatbot’s success. 

Based on the data you’ve gathered, you will have a clear picture of the bot’s reception and usefulness to determine what parts need additional tweaks or fixes.

Again, this is just a rough outline. The chatbot implementation steps can vary depending on your organization. We want to share some of our stories so you can better understand how it looks and what to expect.

Building a Chatbot From Scratch: How CHI Software Does It

Let’s open up our portfolio and take a look at a couple of case studies.

CHI Software’s Website Chatbot: How We Did It

With more companies implementing chatbots on their websites, it seemed only logical for us to do the same, especially since chatbot development is our bread and butter.

To realize our vision, we divided the development process into two phases.

How we implemented a chatbot that knows everything about CHI Software

Phase 1: Creating a text-based AI-powered chatbot

Our goal was to create a Q&A chatbot that can assist our clients by providing answers to their queries. To make it work, we provided a set of documents and links with useful information about our expertise, case studies, articles, etc.

Despite being a work in progress, our chatbot already has some notable features:

• Data security to protect user information and other sensitive data;
• Use of multiple datasets to enhance the chatbot’s ability to provide relevant responses;
• Constant self-learning for never-ending service improvement and adaptation to changing queries;
• Natural language processing (NLP) engine for better communication with users;
• Error handling to recognize misunderstandings and provide feedback.

Phase 2: Designing a realistic avatar

As of July 2024, the second phase is still a work in progress. In the future, you can expect to see:

• A realistic avatar to put a face on our chatbot;
• Animated movement for realistic avatar behavior;
• Lip sync for an immersive answering experience;
• Voice generation to provide users with voice-based answers.

This project has been in the works for the past three months, but we have already seen the fruits of our labor: 

• 20% boost in efficiency of responses since bot integration;
• 35% improvement in manager’s time saved by delegating queries to the chatbot;

To learn more about the chatbot, its features and its tech stack, read our extensive case study.

Do you want to process client queries more effectively? An AI chatbot may help! Contact our engineering team

How CHI Software Implemented a Chatbot for an Educational Institution

Our client, an educational platform, was looking for a solution to improve their business by:

• Enhancing teacher efficiency with a tool that could generate assessment materials;
• Creating engaging learning experiences for students;
• Reducing workload for teachers to save time and streamline administrative tasks;
• Incorporating adaptive scaling to match the platform’s needs;
• Adding automation for straightforward teaching and learning processes.

AI chatbots can be helpful for both students and educators.

We knew that an AI-powered chatbot could satisfy our client’s needs. This idea consisted of four main points:  

• To streamline information and collaboration, we decided to utilize generative AI-powered Slack bots;
• To make lessons more engaging, we incorporated a story writer powered by ChatGPT;
• To evaluate student’s essays based on predefined criteria, we put an AI-powered system in place;
• To plan content for lessons, a generative AI recommendation system was added. 

At that point, we rolled out the following features:

• An AI chatbot that can create open-ended questions, multiple-choice questions, and essays on a variety of topics;
• Scalability and performance to ensure smooth handling of dynamic user base without losing performance;
• Enhanced user experience for streamlined navigation and training;
• Data security to safeguard sensitive information;
• Large Language Model (LLM) integration for enhancing the quality and diversity of generated questions;
• Insight generation based on data analysis of student performance.

Despite the project still being a work in progress, our client has already noticed improvements, such as:

• 40% of teacher’s time saved creating assessment materials;
• A significant qualitative improvement in the learning experience due to diverse questions introduced by the chatbot;
• A 50% workload reduction for teachers since question generation is fully delegated to the chatbot;
• A 70% adaptability increase after generative AI integration;
• A 25% decision quality improvement thanks to insights generated by our chatbot.

You can learn more about the product’s features and development process by reading our case study. In the meantime, let’s talk about the obstacles you may face during development.

Developing a Chatbot: Challenges You Might Encounter

Like every coin has two sides, chatbots have their benefits and challenges. Here, we will discuss the main obstacles you might encounter during the implementation process.

Every implementation challenge can be overcome with the help of professional AI engineers.

Time and Effort to Train AI

Problem: While AI provides a variety of benefits, it takes a lot of time to train it so it can provide informed answers. Issues here can be particularly harmful if the chatbot has no reference outside of its training dataset.

Solution: A good call is to create a dedicated FAQ page so the chatbot can reference it in case of tricky questions. Another thing that will substantially help is using cloud-based resources for AI training, since you won’t have to rely on your own computing power.

Effective System Setup

Problem: The system can provide inaccurate answers to queries due to insufficient FAQ knowledge. Systems can also suffer from scaling attempts due to insufficient infrastructure and poor setup, which lead to performance issues.

Solution: Double-check the FAQ for inaccuracies and duplicated answers and fill in all missing information. In cases when you can’t provide enough infrastructure, a good call would be to rely on cloud-based solutions since they are easy to scale your solution.  

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Integration With Existing Systems

Problem: The integration process is complex and can lead to compatibility issues. If you want your chatbot to interact directly with your databases or CRM systems, security issues can arise.

Solution: Detailed testing and the use of standardized APIs can help mitigate this problem since it will ensure data protection and compatibility by using a “middle-man” in the form of an API. 

Message Customization

Problem: Chatbot is an extension of your organization, which means it should maintain your brand’s image and style of communication. Without proper training, the chatbot will provide neutral or generic answers.

Solution: To match the brand image, create question-answer pairs to provide examples for the AI. Additionally, adaptive learning algorithms should be implemented to fine-tune the chatbot’s responses.

How Much Does It Cost to Build an AI Chatbot From Scratch? 

Building an AI chatbot from scratch isn’t a one-size-fits-all process – it depends on what you need it to do and how sophisticated you want it to be.  

A simple chatbot that handles basic FAQs (for example, ChatGPT integration with no customization) might cost USD 2,000. But if you’re looking for something more advanced, like a chatbot with natural language processing, machine learning, and seamless integration with your business systems, you could be looking at anywhere from USD 20,000. 

Here’s what impacts chatbot development cost: 

• Features provided: Is your chatbot there to answer common questions, or do you want it to handle complex customer interactions? The more it can do, the higher the price tag; 
• Developers’ rates: Whether you hire in-house, work with freelancers, or partner with an expert development company, the team’s location and expertise play a big role in cost; 
• Integrations: Connecting your chatbot to CRMs, e-commerce platforms, or other tools adds value but also development complexity; 
• Ongoing support: Don’t forget about maintenance, updates, and scaling – these are long-term investments. 

While a custom chatbot might require a higher upfront investment, it’s designed specifically for your business needs, making it a smart move for long-term growth. 

Conclusion

AI chatbots are a versatile and efficient tool for any business. Their ability to generate content, create data-driven insights, and provide support is practically unmatched by any other software.

We’ve discussed the outline of chatbot implementation steps, but to get the ideal outcomes, you need a team of experts. Fortunately, we at CHI Software have everyone you may be looking for. 

Our engineers will gladly help you meet your business needs, just like we’ve already helped other clients. So, what are you waiting for? Drop us a message and let’s help you integrate a chatbot you need.

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.