How to Create a Chatbot in 2025: Ultimate Step-By-Step Guide

A chatbot is a software program developed to simulate human communication with users, helping to solve their problems and answer their questions. Chatbots can interact via text and audio on websites, apps, social media platforms, and smart devices like smart speakers, smartphones, and smartwatches. This tool improves customer engagement, optimizes costs, and tracks to gain insights.

Considering how to create a chatbot, you should understand its type. Such a decision comes from your business type, industry, goals, and needs. So, let’s see what types of chatbots exist and discuss in detail why chatbot creation is beneficial.

There are different types of chatbots that are responsible for different tasks and pursuing different goals. Let’s look at types of chatbots in detail.

Developing a chatbot will bring a lot of benefits to your business, starting with cost-effectiveness and ending with personalization. We’ve outlined the primary advantages chatbots offer and provided statistics to support them.

Cost-effectiveness

Chatbots reduce operational costs by automating a high number of repetitive tasks without the need for constant human support. They can manage everything, from password resets and similar login troubles to other basic service requests, work 24/7, and replace the need for large support teams. According to the Times, Klarna's AI chatbot manages two-thirds of customer service inquiries, performing the work equivalent of 700 full-time agents.

Availability & simplification

After creating an AI chatbot, it's available day and night, helping customers get faster support and even simplifying the trading process. Thanks to this simplification, clients want to use chatbots more and more. Reuters reports that during the 2024 holiday season, shoppers used AI-based chatbot services 42% more than the previous year.

Keen understanding

The people usually find and buy an appropriate product through your company, but rarely talk to you. Without direct interaction, you don't know their concerns or preferences, which means you lose out on valuable feedback. A chatbot implementation can quickly solve this issue. According to Statista, 94% of consumers believe conversational AI will make traditional call centers obsolete, emphasizing the role of chatbots in understanding and addressing user needs effectively.

Personalized services

Chatbot provides an excellent opportunity for personalized user interactions throughout the customer lifecycle. Statista said that 64% of businesses believe that chatbots enable them to deliver more tailored service to their customers. Chatbot service offers all sorts of information about a product, provides support, and interacts with the client, offering guidance.

Answering the question “how to create a chatbot?” is complex because the development of such an application involves different phases. You should consider the chatbot's purpose and platform, consider the right vendor, and more.

So, let’s check the seven steps to build a chatbot:

1. Define chatbot purpose
2. Find a trusted chatbot development partner
3. Decide on chatbot functionality
4. Choose appropriate platform technologies
5. Develop a chatbot solution
6. Introduce a knowledge base
7. Test and improve

From the start, clarify what you need to accomplish when creating a chatbot. It can be the desire to improve customer support, increase sales, collect leads, or automate operational processes. A clear purpose keeps development focused and ensures the chatbot aligns with your business needs. Every decision — from design to feature set — should follow this core objective.

Selection of a trusted chatbot development partner is vital. You should choose the IT vendor with proven experience in software development. Pay attention to their portfolio to check if they have projects related to your case. Look at their case studies to consider how they cope with troubles and provide results. Also, don’t forget about client testimonials that give you a good indicator of your IT partner’s reliability, and check verified reviews on platforms like Clutch.

At Cleveroad, we have extensive experience in providing custom software development services. To prove our expertise, let us tell you about one of our recent cooperations.

We provided dedicated team development services for our client from the USA – Nursing Education Company. They provide online educational and training programs for medical students of different Medical and Healthcare organizations in the US. Their platform has 300,000+ students. NURSING.com was listed on the Inc.5,000 Fastest-Growing Private Companies in America (319 place) and featured on the Fast Company World Changing Ideas list as well.

Cleveroad suggested a custom development approach by first conducting a thorough project analysis and then starting with a clear and transparent plan. Our team redesigned the core modules and implemented major UI/UX improvements on both web and mobile. Developed and implemented SIMCLEX — an NCLEX exam simulator designed to replicate real exam conditions for nursing students. As a result, our customers get a feature-rich healthcare LMS that is fully relevant to users' needs and the client’s revenue model.

Here is what Daniel Jones, CTO at NURSING, said about cooperation with Cleveroad:

To create a chatbot from scratch, choose the specific features your chatbot needs to deliver value. Keep it simple at first. Focus on functionality that directly aligns with your goals. Below are the core functions your chatbot must include:

• FAQ responses. Answer common customer questions instantly.
• Guided flows. Walk users through processes or decisions step by step.
• Product info. Provide details about services, pricing, or availability.
• User input. Collect emails, feedback, or other user data.
• Reminders. Send follow-ups, alerts, or scheduled notifications.

After providing these basic features, you can expand your chatbot with more advanced functions. It can include AI-powered recommendations, voice integration, multilingual support, transactional capabilities, and more. Such a feature pool will increase your customer satisfaction and the quality of your product or services.

Considering how to build a chatbot, you should recognize your tech stack. It depends on the complexity and use case. For simple bots, you can use no-code tools like Chatfuel, Botsify, and Flow XO, which offer drag-and-drop interfaces and prebuilt templates, and are ideal for marketing, FAQs, or basic support. There are also simple tools like QnA Maker and Motion.AI that focus on quick setup and structured answers, while ChatBot adds testing and dynamic response capabilities.

For advanced, custom chatbots, we recommend employing frameworks like Wit.AI, IBM Watson, Microsoft Bot Framework, and Pandorabots, which provide deep AI, multilingual support, and NLP. Complex bots require some programming (Python, Node.js, etc.) and allow integration across platforms like Slack, websites, or apps.

At this stage, your chatbot idea begins to take shape as a fully functioning product. Development is where all the planning, strategy, and design decisions are brought to life. This part consumes your vendor builds a responsive system that meets both user expectations and your business goals.

A key part of the chatbot development process is modeling the conversation, defining how the chatbot should respond, ask questions, and guide users through various scenarios. It also includes the need to manage user input, maintain context, train your chatbot and its large language models (LLMs), etc.

Developers code all these critical elements, combine them into a working solution, and thus realize the chatbot architecture. Now, let's take a look at the example of a typical chatbot architecture.

Also, your development partner must test and improve your chatbot. This part includes making sure that each chatbot aspect works properly, that the bot can handle the expected load, provide fast responses, and more. Such an approach ensures that your bot will perform seamlessly after launch.

A chatbot is only as smart as the data behind it. To build a chatbot with fast and accurate responses, your vendor should connect it to a structured knowledge base to build an LLM. It can be support articles, product info, or internal documentation. The chatbot will get answers from this source and deliver fast, consistent replies. It improves user satisfaction as well as reduces the workload on your support team by handling common queries efficiently. Also, you can implement machine learning technology in the chatbot so that it can learn from customer questions and answers.

Testing is a key part of answering how to set up chatbot systems that perform over time. Review conversation history and flow to make a general interaction pattern, track user behavior, and improve based on what you learn. Also, refine responses, remove friction, and keep the chatbot communication natural. Focus on metrics like completion rate, fallback frequency, and engagement. This is what turns a basic chatbot into a reliable tool that your users trust.

There are two essential types of chatbots usually distinguished: rule-based solutions and AI ones. Let’s look closer at what type of chatbot you should build.

This kind of chatbot is proper for small companies with particular aims (like a bot answering FAQ). Such bots can follow various scenarios and accomplish specific tasks, though they are more straightforward than AI products.

Maybe you’ll ask, “How to make a chatbot function like that?” Thus, you need to know that rule-based bots have a ‘map’ of the conversation using ‘if/then’ logic. It is a list of questions a customer may ask and instructions for the chatbot to respond that should be written when you think only about the chatbot, and how to create it. This way, such bots can solve the problems they are familiar with.

Rule-based bot benefits:

• Security
• Optimal development budget
• Integration with legacy systems
• Possibility to contain and transfer media files

This bot option is appropriate for small businesses. However, according to SSRN, the feature set of such chatbots is limited according to the functionality of the chatbot builder that constructed it. The AI products are more complex, and their feature set can be limited only by the functionality of the messenger they are integrated into.

Let’s move on with AI chatbots. They are famous for their self-learning possibilities, due to which they not only perceive users’ intentions represented in messages but also analyze them to offer better feedback. So, the more you train them, the more appropriate answers they give.

So, if you’ve got a question on how to build AI chatbot, you should first investigate custom AI chatbot benefits benefits:

• Data analysis conducted by AI
• The customers’ behavior analysis
• Multilingual communication
• Decision-making possibility

Keep in mind that no one rule-based chatbot constructor can build a solution satisfying all your needs. That’s why you should collaborate with a development team that will build a custom chatbot according to your business's required characteristics.

To sum it all up, here's a clear comparative table that highlights the key differences between rule-based chatbots and AI chatbots:

If you want to build a chatbot that stands out in today’s market, staying ahead of emerging trends is essential. So, here are the main chatbot trends that will allow you to outperform competitors.

Chatbots no longer give simple and non-personalized answers. Top-performing bots now tailor conversations based on user behavior, preferences, and past interactions. Hyper-personalization boosts engagement and increases conversions by making the experience feel relevant from the first message. If you're looking at how to create AI chatbot systems that connect on a deeper level, this is what you should implement initially.

Modern NLP enables chatbots to understand context, intent, tone, and even slang. This moves bots beyond keyword-matching toward natural conversations that feel human. It also helps reduce misunderstandings, improves response accuracy, and allows bots to handle more complex queries without human handoff. For those exploring how to build a chatbot with better comprehension and smoother interaction, investing in strong NLP tools is non-negotiable.

Users don’t always want to type. They might prefer to speak, click a button, upload a photo, or even watch a short video. Multimodal chatbots allow all of that. This makes the interaction smoother, more intuitive, and accessible across different user preferences and devices. If you're planning how to make a chatbot more engaging, multimodal support helps bridge the gap between static chat and rich customer experience. It can become a crucial competitive advantage.

The best bots don’t wait for the user to ask — they understand needs and act first. Whether it’s sending a reminder, surfacing a useful tip, or flagging a problem before it escalates, proactive chatbots increase value and drive loyalty. These capabilities are possible by using user behavior, patterns, and past interactions to deliver timely, relevant responses. If you're working on how to make a chatbot that feels smart, focus on prediction and timing.

Today’s users jump from your website to social media to mobile apps — and they expect your chatbot to follow. Seamless omnichannel integration means your bot remembers the user, understands the context, and keeps the conversation going no matter where it starts. It improves service continuity and builds trust. If you're exploring how to create a chatbot that supports real customer journeys, connecting it across all platforms is a must.

The cost to create a chatbot depends heavily on what you're building, how complex it is, and what it needs to do. There’s a big difference between a simple support bot that answers FAQs and a multilingual, AI-powered assistant that integrates with multiple platforms.

At the start, you can build a chatbot with basic, rule-based logic for free using no-code tools. But if your goal is to scale or deliver more personalized, AI-driven conversations, expect to start with a budget of $30,000+. A more advanced AI chatbot project that uses machine learning, natural language processing, and deep integration can easily reach $100,000 or more.

So, typically, the chatbot development cost ranges from $30,000 to $100,000+. The development time required varies from 2 to 3+ months.

Let’s take a look at how much it would take to build the chatbot features.

If you need an accuracy estimate, feel free to book a strategy call, and our skilled specialists will review your idea to make a comprehensive cost estimation on developing a chatbot for your business.

Cleveroad is a skilled chatbot development company headquartered in the Central and Eastern Europe region, Estonia. Since 2011, we’ve been providing solutions for startups, small to medium-sized businesses (SMBs), and enterprises, helping them achieve their goals and bring their ideas to life. Additionally, we offer broad AI development services, such as GenAI consulting, NLP, data processing, predictive analytics, custom AI solutions, and more. We are proficient in 9 industries, including Healthcare, Supply Chain, FinTech, Travel, Media, Retail, and more.

During a collaboration with Cleveroad, you can get a range of benefits:

• Deep experience in custom software development, providing robust and flexible web, mobile, cross-platform, and cloud chatbot solutions
• Free Solution Workshop stage to align your AI chatbot needs with technical implementation
• Partnership with an ISO-certified company implementing ISO 9001 quality management systems and ISO 27001 security standards
• All guarantees for your business information security and signing an NDA per your request
• A high level of expertise in providing cloud services, proven by receiving Amazon Web Services (AWS) Select Tier Partner status within the AWS Partner Network (APN)