AI-powered chatbots are becoming widely popular due to their ability to improve customer engagement, provide 24/7 support globally, automate routine tasks, and ultimately reduce operational costs. While some businesses opt for ready-to-use solutions, many choose to build their own. But where do you start once you decide to develop your chatbot?
As a software company specializing in ChatGPT development services, we at ElifTech can help you understand the underlying principles of AI chatbots, the features to consider adding, the tools you need to be aware of, and, most importantly, the process your development team will need to follow.
After reading our guide, you will clearly understand what to focus on while overseeing your AI chatbot development.
How do AI chatbots work?
As a good start, let’s clarify what an AI chatbot is and how it works.
An AI chatbot is a software application that uses artificial intelligence (AI) technologies, including machine learning (ML) and natural language processing (NLP), to communicate with users.
Later in our guide, we’ll explain how each technology helps chatbots understand us and deliver relevant replies. For now, we’ll give you a quick overview of how AI chatbots generally function:
- A user initiates an interaction. Everything starts simply with a person sending a message to your chatbot.
- The chatbot processes the input. Depending on whether the user sent a text message or used their voice, the chatbot processes the input directly or uses speech-to-text technology first.
- The chatbot uses NLP to understand the input. Natural language processing helps the chatbot analyze the information and understand the intent behind the user’s message.
- The chatbot generates a response. Based on the recognized intent, the chatbot analyzes the information it accesses and sends a relevant reply to the user.
- The user receives a reply. From here, the conversation might continue or end.
- The chatbot collects data. The chatbot records all user interactions and saves the data they provide.
- The chatbot uses ML to improve its responses. Over time, it analyzes the collected information and improves the quality of its responses based on this data.
Chatbot types: Rule-based vs. AI-based
AI-based chatbots are not the only ones available. There are also rule-based chatbots that, as you might guess, work according to specific predefined rules. To answer your silent question of “Which one is better for my business?” we’ve provided a comparison table so you can make a well-informed decision.
However, if you want a quick answer, AI-based chatbots have a better understanding of human language, learn on their own, and handle more complex queries. So, if your customers require more than predefined FAQ replies, an AI chatbot is your best option.
As you can see, AI chatbots don’t work magic but actually follow a pretty structured workflow. Thanks to this workflow and the technologies chatbots use, they can offer your users specific functionality.
8 Common AI chatbot features
When developing a product, businesses start with a list of features their potential users require. While we may not know the exact chatbot functionality your business needs, here are some features and capabilities we have developed for our clients’ chatbots.
Contextual awareness
By implementing NLP, we make chatbots understand human language and the context of conversations, including emotions, slang, idioms, and jargon. Gathering and analyzing this information allows AI chatbots to provide relevant and valuable responses to users.
Learning capabilities
We use ML to allow AI chatbots to learn from previous interactions with users. This ability enhances their understanding of human language, improves the quality of responses, and allows the chatbot to handle a broader range of queries.
Personalization
Since we develop AI chatbots that understand context, gather information about previous user interactions with your platform, and learn from those interactions, the user experience becomes much more personalized. This allows the chatbot to provide relevant information, solutions, and suggestions that align with individual user preferences and needs.
Integration capabilities
Businesses rarely need a chatbot to function as a separate application; instead, they typically require it to be part of their existing systems. That’s why chatbots are often integrated into internal platforms, websites, CRMs, and mobile or web applications using Application Programming Interfaces (APIs).
Analytics and reporting
In addition to helping businesses interact with customers, AI chatbots provide insights into user behavior, preferences, and engagement patterns. You can see which questions your users ask, which responses they find helpful, and at what stage of the funnel they most frequently have questions. AI chatbots also monitor their own metrics, such as response time, resolution rate, and user satisfaction.
Scalability
Your chatbot has to be scalable, which means it should handle a high volume of interactions simultaneously without compromising performance quality. Cloud-based platforms that support elastic scaling, such as AWS, Google Cloud, or Microsoft Azure, can help you with that.
Human-agent handoff
Even for AI chatbots, some queries can still be too complex to understand, or they might simply not have the information a user asks for. In these cases, we need a seamless handoff—a process where the chatbot recognizes its inability to handle a query and sends it to a human agent.
The most important part of the handoff is to ensure that no information is lost and that the agent receives the entire conversation history and context. This way, users won’t need to repeat themselves, and their experience will be more pleasant.
What’s more, we can make AI chatbots determine the most appropriate agent to direct the user to, ensuring better resolution of their queries.
Customizable user interface
As a brand ourselves, we understand the importance of maintaining a company's brand identity. That’s why we develop our chatbots with customizable interfaces. With this feature, you’ll be able to add your logo, colors, fonts, and other brand elements to your chatbot. This will help provide your customers with a consistent user experience when interacting with your business across different channels.
It’s up to you to decide if you want to implement all of them in your next chatbot, but at least now you know what features to request from your chatbot development team.
What are the key technologies in chatbot development?
Whether you work with an in-house team or an AI chatbot development company, the developers will handle the technical aspects. However, it’s natural to be concerned about fully trusting them.
In this case, knowing what technologies power the desired chatbot functionality will help you feel more confident when reviewing the scope of work and the suggested tech stack. So, here’s what makes your chatbot understand and assist your users.
Natural language processing (NLP)
We’ve already mentioned that NLP helps your chatbot process and understand human language. But how does it work? The process looks like this:
- The technology breaks the user’s message into individual words and parts, sorting them based on how much context they provide.
- It uses previously learned knowledge about human language to understand what the user wants.
- After understanding the intent, NLP extracts other important information like names, dates, or locations.
- If the conversation continues, the technology analyzes the entire dialogue to maintain context.
- Based on predefined answers, the database your chatbot has access to, and the information provided by the user, NLP generates a relevant response.
NLP is truly essential for ensuring that your chatbot perceives user requests correctly, provides the needed information, maintains the context of the conversation, and generates human-like replies.
Machine learning (ML)
With ML capabilities, your chatbot can improve by learning from previous interactions with users. Every question, reply, or feedback is collected into a dataset that serves as training data.
First, the data needs to be preprocessed—prepared for training by removing irrelevant information and extracting important words, phrases, and user intents. This can be done by data scientists or with automated data preprocessing tools. Some ML platforms, like Google AutoML or Microsoft Azure Machine Learning, provide built-in preprocessing features as part of their workflows.
After the data is prepared, we can start training the model. The training process can be done in two ways:
- Supervised learning: Data scientists or ML engineers use labeled data with predefined answers to teach the model.
- Unsupervised learning: Data scientists or ML engineers use unlabeled data with no predefined answers, allowing the model to find patterns on its own.
Both methods are useful, and many businesses use a combination of both: supervised learning for training on data where specific outputs are needed and unsupervised learning for exploring possible outputs and gaining a better understanding of data patterns.
Another way a model can learn is through continuous learning (also called reinforcement learning). This means training the model via interactions with users and their feedback on the replies. For example, if your chatbot responds and a user gives a positive reaction, like “That was helpful!” the model will use similar answers in the future. If the feedback is negative, the model will try different approaches.
ML helps your chatbot stay up-to-date and better assists your users. However, to ensure high-quality replies, it’s important to regularly train the model within your chatbot using fresh data.
Deep learning
Deep learning is a subset of machine learning that uses neural networks—complex algorithms inspired by the structure and function of the human brain. These networks consist of multiple layers, and each of them processes data from a different perspective, allowing the model to gain a deeper understanding of patterns and produce more accurate outcomes.
With the help of deep learning, your chatbot can handle complex queries that traditional machine learning methods might struggle with. For example, it can manage highly emotional requests or ambiguous questions where, depending on the context, a user may mean different things. Additionally, due to its multilayered data analysis, deep learning is also highly effective in processing large amounts of unstructured data.
Generative pre-trained transformer (GPT)
GPT is a model driven by deep learning and trained on a large dataset. This allows it to better understand context, generate human-like responses, and be fine-tuned for specific domains.
In chatbots, GPT improves the relevance of replies and makes them sound more natural. It also has learning capabilities that will help your chatbot improve over time, offering more personalized replies and managing complex queries more efficiently.
As you can see, NLP, ML, Deep Learning, and GPT all play a significant role in enhancing the quality of your chatbot responses. Depending on your specific expectations from a chatbot, you can combine and use these technologies in various ways. With a better understanding of how each technology works, we hope you now have a clearer view of what you need.
Popular chatbot development platforms
There are many platforms that provide the infrastructure for chatbot development. These platforms may offer drag-and-drop interfaces, integration with popular messengers and CRMs, built-in NLP capabilities, and analytics for tracking chatbot performance.
It’s important to understand the tools available in the market, their functionality, the challenges you might face using them, and the type of team and knowledge required for efficient use. So, we’ve prepared a list of the most popular chatbot development platforms and all the information you need about them.
Dialogflow
Dialogflow is a chatbot development platform by Google.
Main Features Include:
- Comprehensive NLP capabilities
- Multi-platform integration
- Visual flow-builder
- Templates and pre-built agents
- Detailed analytics
Platform Challenges:
- Might not be suitable for highly complex use cases
- Limited ability to fine-tune the NLP model
- Can be relatively costly for enterprises
Dialogflow is a great option for businesses that need an easy-to-use platform with advanced natural language processing, and it is especially favored by companies that work within the Google ecosystem.
Microsoft Bot Framework
Microsoft Bot Framework is a tool for developing, testing, and managing bots.
Main Features Include:
- Multiple language support, including C# and JavaScript
- Integration with Azure services for AI and ML
- Cross-platform support
- Natural language understanding through Azure Cognitive Services
- Extensive development tools and libraries
Platform Challenges:
- Complex setup, which may be difficult for new users to understand
- Documentation can be challenging to navigate
- Closely tied to Azure, which limits cloud provider choice
Microsoft Bot Framework is suitable for enterprises that require a flexible and easily scalable tool or development teams looking for support across various programming languages.
IBM Watson
IBM Watson is a comprehensive chatbot application development platform with advanced AI functionality.
Main Features Include:
- Advanced AI and ML capabilities for understanding complex queries
- Highly customizable with extensive configuration tools
- Integration with various channels and enterprise systems
- Advanced analytics and reporting
Platform Challenges:
- Can be expensive, especially for extensive usage
- Complex configuration may require a deeper understanding of IBM’s ecosystem
- May face performance challenges when processing large volumes of data
IBM Watson is a great choice for businesses that need highly customizable chatbots capable of handling complex queries and integrating with existing enterprise systems.
Amazon Lex
Amazon Lex is a service that allows you to build conversational interfaces using voice and text.
Main Features Include:
- Integration with AWS services
- Powerful NLP that leverages Amazon’s machine learning technology
- A scalable infrastructure capable of handling high volumes of interactions
- Pricing based on usage
- Support for multi-channel deployment
Platform Challenges:
- Limited customization compared to other platforms
- Tightly integrated with AWS, which reduces cloud provider flexibility
- Pricing model can be confusing and potentially costly
Amazon Lex is well-suited for businesses that need seamless integration with AWS and scalable infrastructure.
Rasa
Rasa is an open-source framework used for building conversational AI applications.
Main Features Include:
- Comprehensive control and customization with access to the source code
- Advanced machine learning capabilities
- Support for on-premises or cloud deployment
- Active community and extensive documentation
Platform Challenges:
- Requires technical expertise to set up and maintain
- More maintenance and updates are needed compared to managed platforms
- Limited out-of-the-box features and integrations
Rasa is ideal for businesses that want full control over customization and deployment and have the technical expertise to manage and maintain the platform.
Which platform to use depends on your needs, your team's capabilities, and the tools you’re currently using. And if you’re still looking for a company to develop your chatbot, you need to ensure they are familiar with the infrastructure you want to use. That way, you will be certain that all your business systems will work seamlessly together and that your chatbot has the functionality you need.
The chatbot development process
Now that we understand the features chatbots can have and the tools available for their development, it’s time to create a roadmap—a step-by-step plan for developing chatbots. This roadmap may differ from team to team, so we’ll discuss the chatbot development process that we at ElifTech follow and have found to be the most effective based on our experience.
Requirement gathering and planning
In this step, we will gather your requirements and expectations to understand your vision for the chatbot. We will define the main goals for the chatbot, whether it’s for customer support, lead generation, or another purpose. Our team will also analyze your target audience to better grasp the needs of potential users. And finally, based on this information, we will create a detailed list of features that align with your objectives and user needs.
Designing chatbot conversation flow
Chatbot conversation flow is a map that outlines various conversation scenarios and the desired outcomes of the chatbot. This flow helps us ensure that your users interact with the chatbot naturally and intuitively and that your chatbot provides fast, accurate, and consistent responses. To design it, we will use information about your potential users to identify which queries they might have and what replies are a natural response to them.
Choosing the right platform and tools
Depending on the tools you currently use, the required features, and the need for scalability and customization, we will select the most suitable chatbot app development platform. We will also evaluate the available tools and libraries to ensure their relevance to your specific project needs.
Implementing natural language processing (NLP)
The next step is setting up language models that can understand your users. We will configure the chatbot to recognize intents and extract information from user queries, such as names, dates, or locations, to provide contextual replies.
Integrating with backend systems and APIs
At this stage, we can connect your chatbot with various backend systems via APIs. This may include databases or your CRM, enabling the chatbot to access the most up-to-date information.
Training
Training your chatbot is essential to ensure the high quality of its replies. We will use previous interactions, user feedback, and sample dialogues to fine-tune the chatbot.
Testing and debugging
Now it’s time to test how your chatbot works. We’ll evaluate it through real user interactions, identifying any issues with usability, gathering feedback, and improving its user-friendliness and overall performance.
Deployment and monitoring
Finally, we launch the chatbot and connect it to the necessary channels and systems. Now, we need to track its performance metrics and address any issues that arise. It's also important to regularly update the chatbot's database and fine-tune it based on user feedback.
AI chatbot integration with enterprise systems
If you use an enterprise system, such as your company’s CRM, integrating your chatbot with it can automate routine tasks and provide the chatbot with access to crucial information, such as the client database or company handbook. Thus, the main reasons for integrating a chatbot with enterprise systems include:
- Automating repetitive tasks, like answering FAQs, processing orders, and managing appointments.
- Enhancing data flow between different departments of the organization.
- Initiating workflows based on user-triggered actions, for example, creating a support ticket.
- Monitoring the system for possible performance issues and sending regular reports.
- Enhancing the chatbot user experience by accessing data from enterprise systems.
ElifTech can easily integrate your chatbot with any enterprise system via API. Our process will look like this:
- We’ll make a list of the systems you want to connect.
- We’ll determine which API we need to use for your integration.
- We will implement security measures to ensure safe data access.
- After we know everything is secure, we’ll plan how data will move between systems and define the required scenarios for the API.
- We will establish the connection between the chatbot, the system, and the API.
- We’ll verify that the connection works correctly and that the data flows as expected.
- After everything is set, we will continuously monitor the integration and make adjustments as needed to maintain performance.
Custom chatbot development services: Your path to innovative solutions
Developing your own AI chatbot is, as you can see, a complex process. To save you time and minimize your stress, ElifTech is ready to help. With our expertise in AI design and high-tech solutions, we can guide you through all the development stages, ensuring the quality and great performance of your AI chatbot.
Our AI engineers have experience developing in various domains, such as healthcare, finance, manufacturing, retail, logistics, and more. We understand the challenges faced in each industry, enabling us to meet your business's specific needs. Book a meeting with ElifTech and get the best AI chatbot development services today!