Businesses across different verticals face the need to provide customer service and remote troubleshooting with rational spending. That’s where chatbots come into play. But how to build a chatbot that can process complex queries and what is the role of AI in chatbot development?
In this article, we’ll discuss technologies, challenges, and best practices for AI chatbot development based on MobiDev’s 15+ years of expertise. Also, we’ll explain what expectations B2B and B2C customers have and why the ability to move customers’ issues forward is crucial for any chatbot.
Benefits of Chatbot Development for Business
Gartner reveals that chatbots will become the primary customer service channel for roughly a quarter of organizations by 2027. Let’s take a look at the key factors contributing to their adoption:
1. 24/7 Customer Support
With chatbots, businesses can be accessible to customers at any time, providing instant responses and resolving queries efficiently. This round-the-clock availability improves customer experience and prevents missed opportunities, making businesses more reliable in the eyes of their clients.
2. Cost Reduction
Automating customer interactions reduces the need for large customer support teams, saving costs on salaries, training, and infrastructure. AI-powered chatbots can handle high volumes of inquiries simultaneously, reducing the workload for support specialists during peak times. Here, it makes sense to talk numbers. By 2026, conversational AI deployment within contact centers will reduce agent labor costs by $80 billion.
3. Scalability
Chatbots can easily adapt to growing business needs, managing increased interactions without additional resources. They integrate with existing systems, such as CRMs, unify communication channels, and streamline customer journeys.
4. Personalization and Multilingual Support
With AI-driven capabilities, chatbots can analyze customer data to provide personalized experiences. They can also recommend products, answer contextually relevant questions, and adjust interactions based on user preferences, driving customer engagement and loyalty.
Moreover, chatbots offer multilingual capabilities, enabling businesses to serve diverse customer bases. This global reach improves accessibility and builds trust with international clients.
5. Data Collection and Insights
Chatbots can collect valuable data from customer interactions and offer insights into preferences, behaviors, and common issues. This information helps businesses refine their strategies, improve services, and develop targeted marketing campaigns.
The benefits of the chatbot vary depending on its type and technologies used.
Exploring Types of Chatbots
Chatbot classification may be based on two criteria: type of user interaction and applied technologies. Simple chatbots offer menu options users can select to initiate different interactions, but more advanced technologies like AI and NLP allow chatbots to be applied for complex scenarios and conversations.
Categorizing Chatbots by Interaction Type
- Text-based chatbots
These bots use natural language processing (NLP) to communicate via written language, answering questions and completing basic tasks. - Voice-based chatbots
By using speech recognition and text-to-speech (TTS) technology, these chatbots can interpret spoken commands and provide audio responses.
Categorizing Chatbots by Technology
Traditional chatbots
- Menu-based: Operate with predefined buttons, guiding users through a fixed process without understanding natural language. For example, an ecommerce chatbot might assist customers by prompting them to “Compare two items” and “Track their order”.
- Rule-based: Use if-then logic to address specific scenarios. For instance, if a user inputs a command like “working hours,” the chatbot retrieves the relevant information.
- Keyword-based: Focus on identifying keywords in user messages to provide appropriate responses. For example, a query like “What are my latest test results?” prompts the bot to recognize “test results” and deliver the requested information.
AI-powered chatbots
- Contextual: Leverage machine learning and NLP to understand user intent and context, allowing for more natural, dynamic conversations. They can ask follow-up questions to clarify requests and provide tailored responses.
- Generative: Often GPT-based, these chatbots create human-like, adaptive responses. They use their knowledge base to maintain open-ended conversations, making interactions feel more genuine and engaging.
AI-powered chatbots can seamlessly transition users to human agents when necessary, avoiding unresolved issues. Agents gain access to the chat history for effective support.
Hybrid Chatbots
A blend of rule-based and AI-powered technologies, hybrid chatbots combine structured workflows with personalized interactions. For instance, they can guide users through predefined menus while offering tips tailored to the user’s history.
Chatbot Development Platforms
Chatbot development platforms serve as the backbone for creating intelligent virtual assistants tailored to business needs. These platforms provide tools and integrations to design, deploy, and manage chatbots. It’s important to understand that the tech stack heavily impacts the chatbot’s capabilities and scalability.
In the following table, we provide a short overview of popular platforms and technologies that can be applied to develop chatbots:
| # | Platform | Description | Strengths | Considerations |
|---|---|---|---|---|
| 1 | Microsoft Azure AI Bot Service | A robust platform that integrates seamlessly with Microsoft Azure services, offering a comprehensive suite of tools for chatbot creation. | Pre-built templates and sample bots to accelerate development. Easily integrate with other Azure services. | Requires technical expertise for development and integration. |
| 2 | Amazon Lex | A serverless platform from AWS for building conversational AI solutions. | Intuitive drag-and-drop interface, seamless integration with AWS services. | May need additional tools for implementing complex functionalities. |
| 3 | Google Dialogflow | A user-friendly platform renowned for its natural language processing (NLP) capabilities. | Easy-to-use interface, pre-built conversational flows, HIPAA-compliant integration with Google Cloud Healthcare API. | Limited customization options for advanced use cases. |
| 4 | GPT-based Solutions | Advanced conversational systems leveraging generative AI for dynamic and context-aware interactions. | Handles unstructured data effectively, supports features like semantic search and personalized recommendations. | Requires specialized development expertise. |
| 5 | IBM Watson Assistant | A feature-rich AI platform providing advanced NLP and analytics capabilities. | Flexible customization options. Supports a wide range of devices and communication channels. | High complexity for beginners, with significant costs for scaling usage. |
| 6 | RASA | An open-source solution offering full control over chatbot customization. | Highly flexible, supports integration with multiple NLP tools, and extensive community support. | Requires significant technical expertise and development effort. |
Let’s summarize. For use cases where natural language processing and contextual analysis are not needed, you can use simple tools to create bots. Sophisticated projects, like smart virtual assistants or personalized chatbots, require powerful conversational AI platforms that support NLP and machine learning.
How to Develop an AI Chatbot Step-by-Step
AI chatbot development can be divided into phases, though these phases can overlap and partially complement each other.
1. Identify the purpose
In the first stage, you need to define why you need a chatbot and what it should achieve. The goal is to determine if it will handle customer support, sales, lead generation, or user engagement.
Also, it’s important to clarify whether the chatbot will provide predefined responses or use AI for complex inputs. Identifying user needs and aligning them with business goals will help to build the right roadmap. If you’re planning to implement the AI chatbot, we recommend diving deeper into our AI app development guide for product owners.
2. Select deployment channels
Choose the platforms where your chatbot will be available, such as your website, mobile app, phone or messaging apps like WhatsApp, Facebook Messenger, or Slack. The chosen platforms must align with your audience’s preferences and support easy integration through APIs or code snippets. Note that maintaining consistency across multiple channels improves the user experience.
3. Choose the technology stack
This step might have a decisive impact on the chatbot’s capabilities. You need to select the tools and frameworks needed to build and operate your chatbot. For simple bots, it’s possible to use no-code platforms like Microsoft Power Virtual Agent. Advanced AI-powered bots require custom development with robust NLP libraries or LLM models.
Before making the final choice, evaluate whether the technology supports voice and text-based interactions, integrates seamlessly with existing systems, and suits your team’s technical skills. Also, remember to plan the infrastructure. Cloud-based solutions like AWS and Google Cloud offer scalability and simplified maintenance, while on-premises infrastructure provides greater data control but involves higher costs and ongoing support requirements.
4. Design the conversation flow
It’s time to map out how users will interact with the chatbot by defining greetings, prompts, user inputs, and responses. The flow should take into account scenarios and include fallback mechanisms for unrecognized inputs. To visualize the conversation structure and implement logical transitions, you can use flowcharts.
5. Train the chatbot
This is an optional step. As a rule, most GPT-based chatbot solutions use RAG architecture that eliminates the need for training. However, your chatbot app may require some specific datasets to be trained on.
With proper training, the chatbot can learn patterns, deliver accurate answers, and improve over time.
6. Test the chatbot
There are two ways to test a chatbot: automated and manual. For automated testing, you can use platforms like Zypnos, TestyourBot, Bot Testing, etc. These platforms generate detailed reports of the results and coding of test scripts.
Manual testing is all about involving users that check the bot’s behavior in different scenarios. Both methods may be time-consuming, but the best results will be achieved only if both methods are combined. Also, it’s crucial to include specific AI app testing tools and practices to achieve the best results.
7. Deploy and monitor
Deploy the chatbot on the selected platforms and monitor its performance using analytics tools. Track metrics such as user satisfaction, query resolution time, and engagement levels.
Regularly update the chatbot based on user feedback and changing needs. Maintain the chatbot’s training and infrastructure to ensure it continues to deliver value and adapts to new requirements.
What to see how an integrated chatbot looks like?
TRY MOBIDEV CHATBOT DEMOIn the following section, we’ll overview challenges that may arise during the AI chatbot development process and the ways to address them.
Challenges and Best Practices for Building Chatbot Apps
Building and adopting a chatbot involves several challenges, including data security, integration, personalization, language support, memory limitations, testing, and response capabilities.
1. Data security
Chatbots often process sensitive user data, such as personal information and payment details, making them attractive targets for cyberattacks. Compliance with privacy regulations like GDPR and CCPA adds another layer of complexity. Many businesses struggle to secure chatbots against unauthorized access and data breaches, risking customer trust, legal penalties, and substantial fines.
To address this challenge, you must implement robust encryption protocols for data in transit and at rest, employ secure authentication mechanisms like multi-factor authentication (MFA), and regularly update security measures to stay ahead of evolving threats.
2. Integration challenges
Integrating chatbots with existing tools like CRM systems, help desks, and databases is often complex. Compatibility issues, varying data formats, and limited API support can create hurdles. These challenges not only delay implementation but also increase costs and raise concerns about data privacy during integration.
You may need to start with system modernization before implementing AI to prepare the foundation for innovations. Watch MobiDev’s webinar recording on why you should modernize before innovating to prepare your product for future growth.
Why You Should Modernize Before Innovating
Access webinar recording3. Personalization and customization
Creating chatbots that feel personal and engaging can be challenging. Many businesses struggle to make chatbots provide tailored responses, leading to generic and uninspired user interactions.
The best way to address this problem is to leverage customer data such as past interactions, purchase history, and demographics, to deliver personalized recommendations and responses. AI algorithms can help improve personalization by analyzing patterns and preferences over time. However, you still need to remember about data security and privacy and make sure to get user consent for their data processing.
4. Supporting multiple languages
Chatbots often face difficulties understanding multiple languages, dialects, and colloquial expressions. LLM, while powerful, has limitations in adapting to the nuances of language diversity, which can impact global user experiences.
By using advanced LLM models and platforms that specialize in multilingual support and training the chatbot on diverse datasets with regional dialects and slang, you’ll be able to provide more accurate multilingual support.
Use Cases for Chatbot Development Across Industries
Let’s dive deeper into industry-specific chatbots and the benefits they bring to businesses.
Healthcare Chatbots
Chatbots in healthcare improve patient engagement, appointment scheduling, and telemedicine services. They assist with symptom checking, medication reminders, and mental health support. However, HIPAA compliance is a critical aspect of their implementation to ensure patient data privacy and security. Developers must integrate features like encrypted communication and restricted access to sensitive information.
Fintech Chatbots
In the fintech sector, chatbots streamline customer support, facilitate account management, and offer financial advice. They can also help users track spending and manage budgets. Compliance with financial regulations, such as KYC (Know Your Customer) and AML (Anti-Money Laundering), is crucial for implementation. These regulations ensure secure transactions and prevent fraud.
Retail Chatbots
Chatbots enhance the retail experience by offering personalized product recommendations, answering FAQs, and assisting with order tracking. They are invaluable in managing customer queries during high-traffic sales periods. AI-powered bots can also analyze purchase history and preferences to drive sales and improve customer retention.
Marketing Chatbots
In marketing, chatbots optimize lead generation, customer engagement, and campaign performance tracking. They enable conversational marketing by interacting with users in real-time, collecting insights, and delivering personalized experiences. Chatbots can also streamline customer surveys and feedback collection, enabling businesses to refine their strategies.
Manufacturing Chatbots
Manufacturing chatbots support tasks like inventory management, equipment monitoring, and employee assistance. They can facilitate predictive maintenance by gathering data from IoT devices and alerting teams to potential issues.
A significant challenge here is integrating chatbots with the legacy systems still used by many manufacturers. Bridging this gap often requires custom APIs or middleware.
Human Resources Chatbots
HR chatbots simplify employee onboarding, answer policy-related questions, and facilitate training programs. They also assist with scheduling interviews and gathering employee feedback. By automating repetitive tasks, chatbots allow HR teams to focus on strategic initiatives, improving overall efficiency.
Chatbots offer significant potential across these industries, but successful implementation requires addressing specific challenges, such as compliance, integration with legacy systems, and customization for user needs. Starting with AI consulting can help mitigate risks of these issues and build an effective AI-powered chatbot that meets your goals and user needs.
Chatbot Development Trends 2025
Key chatbot development trends seem to be formed under the influence of business needs and technology advancements in natural language processing, deep learning, and neural networks.
In 2025, AI voice chatbots will thrive, enabling more companies to provide accurate and intuitive experiences to their customers. Users are able to interact with such chatbots via voice commands, which improves accessibility and overall customer experience.
In addition to voice chatbots, we’re likely to see the rise of different technologies that are focused on personalized interactions. For example, adopting sentiment analysis into AI chatbots will allow companies to better understand customer intentions and the emotional context of the messages. At the same time, contextual understanding is becoming a significant part of new chatbots that offer tailored recommendations and support multiple input modes, including voice, text, or image.
Enhanced LLM models in 2025 will better handle diverse languages, dialects, and cultural nuances. Businesses can use these capabilities to cater to international audiences without requiring separate systems for each language.
Also, chatbots are expected to become deeply integrated with enterprise tools such as CRMs, ERPs, and supply chain management systems. This integration will help businesses to automate workflows, manage data across platforms, and provide instant access to actionable insights. For instance, in customer support, chatbots will pull information directly from CRMs to offer accurate and real-time solutions.
Finally, in 2025, we will see more chatbots designed for niche use cases, addressing the unique challenges of specific industries.
Success Story: Chatbot Development for a US-based Medical Company
Since 2017, MobiDev partnered with a multi-billion-dollar, US-based medical company to develop and maintain a comprehensive web and mobile healthcare management solution. This collaboration extended to integrating an AI-powered chatbot into the client’s existing ecosystem to enhance patient support and streamline operations.
Client’s Objective
The client sought a chatbot solution to reduce call center workload while improving patient-doctor interactions.
Our Approach
- Consulting and Infrastructure Assessment
MobiDev began with a detailed assessment of the client’s infrastructure and business requirements. This consulting phase enabled us to determine the optimal technology stack. The team selected a combination of the Microsoft Azure Bot Framework and Lex for chatbot development. This choice ensured scalability to support a large user base and compliance with HIPAA regulations, a critical requirement for handling sensitive healthcare data. - AI Integration for Enhanced Communication
Using the Microsoft Azure Bot Framework, we integrated advanced AI capabilities through Azure Cognitive Services. Specifically, the Language Understanding Intelligent Service (LUIS) was implemented to enable the chatbot to interpret user intent and extract relevant information from queries. This integration provided a human-like conversational experience and made interactions intuitive and user-friendly. - Scalable Knowledge Base Implementation
To streamline information delivery, we utilized the QnA Maker Service to build a scalable knowledge base for the chatbot. This feature empowered users to receive quick, accurate responses to frequently asked questions, significantly reducing response times.
Importantly, QnA Maker ensures HIPAA compliance by not storing customer data. All data is retained within the Azure region chosen by the client during setup, minimizing risks associated with data retention and bolstering regulatory adherence.
Outcomes and Impact
The implemented chatbot reduced the client’s call center workload by over 15%, leading to savings of approximately $5 million in operational costs within the first year of deployment. This success highlights MobiDev’s ability to deliver impactful, compliant, and scalable AI chatbot solutions tailored to industry-specific needs.
Read the full success story:
HIPAA-compliant Cross-platform healthcare management solutionMobiDev Chatbot Development Services
The MobiDev team has been developing software products tailored to the needs of various industries since 2009. We have a proven track record of navigating complex industry regulations such as HIPAA and GDPR, delivering compliance-ready solutions that support strategic business goals.
If you need to integrate third-party AI services or build a custom AI solution from the ground up, you can count on our AI consulting and engineering services to achieve your goals.
