How to Make an AI Chatbot?

Chatbots have become a critical technological component of any business’s services during the last 10 years. Gartner experts predict that 38% of organizations will implement chatbot technologies in the next two years. However, currently, 50% of customer service and support (CSS) leaders say they assess the value of chatbots from low to moderate. Chatbots have the potential to improve customer experience (CX) and reduce costs if they fit the environment they are used in, are capable of natural conversation and reliable responses, and are properly fine-tuned.

In this article, tech experts from a custom software development company, Belitsoft, concentrate on types of chatbots, their use cases, and best practices for developing AI chatbots that bring benefits to organizations.

Types of Chatbots

A chatbot is a program which acts like a virtual assistant. It answers users’ requests on certain topics and performs commands. There are different types of chatbots.

Rule-based chatbots function according to set decision trees and predefined rules. They can answer FAQs and assist with basic information.
AI-powered chatbots are based on natural language processing (NLP) and machine learning (ML), so they improve their output over time. They can provide customers with advanced services.
Hybrid chatbots combine AI functionality with a rule-based approach. They help with structured interactions and can adapt their responses to requests.
Intent-based chatbots are capable of understanding users’ intentions, as they possess NLP. Such chatbots know why users address them, for instance, to make a reservation or to buy a product. This feature is possible due to tailored training, during which the chatbot learns to analyze and distinguish between such requests as order tracking, feedback, booking, FAQ, etc.
Context-aware chatbots not only understand intents but also analyze the context, such as previous interactions, location, time, etc. These assistants remember the details of the user history or session state, as well as domain-specific context, and adapt answers accordingly.

Depending on the functionality, chatbots may be multilingual, i.e., interact with customers in different languages, and omnichannel, which operate across various platforms like websites, messengers, mobile apps, etc. Users can switch between platforms seamlessly, as the chatbot remembers the current conversation.

What Are Other Options?

Custom AI chatbots

The difference between a generic chatbot and a custom one is that the second is trained on the business’s proprietary data. This specific data includes internal documents, product information, FAQs, etc. As a result, such bots can understand and use certain terminology and provide answers tailored to users’ requests. When addressing software development partners for customizing a chatbot, they specify the components for optimization to align them with the business model and users’ expectations. For example, for finance and healthcare, bots should run in secure environments and comply with data privacy rules and regulations. For e-commerce, virtual assistants should be trained to offer relevant products and apply promo codes. In manufacturing, chatbots should communicate with IoT devices and databases to be able to check status updates and address equipment issues.

Ready-made solutions

Utilize pre-built chatbot platforms with plug-and-play functionality. For example, such platforms as ChatBot.com, Freshchat, DocBot.ai, etc., offer templates for various use cases. They can be e-commerce tools, lead generation, and others. Such bots cover basic conversation workflows, they do not need coding expertise and are suitable for small businesses and teams.

How to Build an AI Chatbot?

Creating an AI chatbot from scratch allows companies to have complete control over the chatbot’s operation and applied technologies. Developers decide on the integrations, NLP engine, as well as extra features like on-premise deployment for data control and API access.

Define the scope

First of all, you should decide what you need a chatbot for and what use cases you will apply the chatbot in. The complexity of the future chatbot impacts the costs. The final costs depend on the use case and the technology stack.

2. Choose a platform

If you decide to create a chatbot from scratch, you can use some of the open-source chatbot platforms and AI agent frameworks, e.g., Microsoft Bot Framework, Botkit, OpenDialog, RASA, Wit.ai, etc. Open-source resources have multiple benefits for users. There are always enough educational sources and a community of developers who can assist with appearing issues.

3. Tackle the building process

Set the greeting to users, and include the prompts for asking for the user data with variables. It will ensure the chatbot understands user requests properly. If you are building an LLM agent, it should be trained on proprietary data to make it tailored to the domain of the business.

4. Integrate

Depending on the requirements, the chatbot can be integrated into the website, a system like Hubspot, or messengers. It can also be connected with the knowledge base, for example, to provide potential customers with information about the products in stock. Retrieved-augmented generation (RAG) is used to label the data and therefore teach the bot to retrieve the information specific to the domain and request.

5. Test and maintain

Testing the conversation with a chatbot and tweaking it for better results is an important and iterative step in the development process. When the chatbot is deployed, its performance should be monitored. There are multiple metrics to do that, such as the number of interactions with the chat, the average duration of the sessions, the containment rate, which indicates the percentage of cases when the chatbot handles the request without human intervention and other metrics.

Who Can Benefit from AI Chatbots?

Healthcare

Chatbots in healthcare schedule appointments, analyze symptoms, remind patients about medications, renew prescriptions, etc. They can be built as standalone applications or be embedded into popular messengers. AI chatbots are available 24/7 and have access to medical resources, which is why medical organizations receive such benefits as increased customer satisfaction, streamlined resource management, lowered costs and readmission rates, and data safety.

Human resources

AI chatbots help HR experts deal with large amounts of data, analyze CVs, adapt onboarding for various specialists, and arrange training resources for employees. Those processes facilitate the workload of the HR department and increase retention rates.

Education

Custom AI chatbots integrated into learning management systems (LMSs) answer learners’ queries regarding curriculum, relevant courses, and additional materials. Chatbots make traditional LMSs more interactive and dynamic, as they provide real-time support and encourage students to actively participate in the learning process.

E-commerce

Conversational chatbots provide customers with pre-sales offers, discounts, purchasing conditions, and after-sales services, such as tracking deliveries, handling returns, etc. Virtual assistants recommend products to potential clients, recover abandoned carts, answer FAQs, upsell, etc. Integration of chatbots with e-commerce platforms, like Magento, Shopify, and others, and CRMs allows for personalizing offers for customers.

Accounting

Custom accounting chatbots can convert conversations with customers from emails, photos, and handwritten notes into invoices or estimates. They draft personalized invoices, extract details from receipts, and fill in appropriate gaps in accounting software.

Customer relationship management

AI chatbots enhance the functionality of CRMs. They can automatically qualify leads, recommend products and services, generate emails, develop marketing campaigns, summarize calls, and perform all those operations while supporting the security of the data.

FAQ

Do I need to train the chatbot after launching it?

Yes, it is recommended for proper performance. You should update the data and fine-tune the bot to make sure it is aware of current trends, performs new tasks successfully, and provides users with reliable and flawless information.

What are the chatbot development services that tech partners offer?

AI chatbot consulting: custom software development firms like Belitsot gather companies’ expectations and requirements and come up with a project plan and budget estimation. Consultants recommend adequate LLM models, and methods of embedding and vectorizing data, and offer various cloud technologies, such as AWS, Azure, etc.
Custom AI chatbot development: tech partners help startups and large enterprises in chatbot development with relevant algorithms and advanced functionality. As a result, custom AI assistants handle complex scenarios and bring businesses the goals they plan to achieve.
AI chatbot API integration: software partners can assist with setting integrations with AI chatbots. These can be integrations with websites, messengers, voice assistants, and other apps. Apart from third-party services, chatbots can also integrate with backend services, databases, as well as payment gateways, shipping solutions, and other IoT services, devices, and equipment.
AI chatbot design and architecture: software experts apply UI/UX design, conversational AI, and ML technologies to create chatbots that will meet customers’ requirements. The end-to-end design project includes receiving and preparing the database of FAQs and answers from the business domain. Then, developers customize the NLP engine that will interpret user requests and set the Intent Classifier which unites different requests into a single intent. The Entity Extractor determines the keywords to understand what users need.

What services can I integrate with a chatbot?

For example, you can integrate with different communication channels, databases, APIs, and NLP engines. All those integrations will make the performance of the chatbot more accurately optimized to users’ requests.

About the Author:

Dmitry Baraishuk is a partner and Chief Innovation Officer at a software development company Belitsoft (a Noventiq company). He has been leading a department specializing in custom software development for 20 years. The department has hundreds of successful projects in such services as healthcare and finance IT consulting, AI software development, application modernization, cloud migration, data analytics implementation, and more for US-based startups and enterprises.