How To Build A Conversational AI?

Dinesh Thakur

19 Jul 2024

Artificial intelligence has seen significant advancements in recent years, particularly in the domain of conversational agents. When people think of conversational AI, they frequently imagine chatbots and voice assistants used for omnichannel customer service and online chat. The rise of Conversational AI, powered by Large Language Models (LLMs), has opened up new possibilities for creating more natural, engaging, and human-like interactions between humans and computers. By analyzing and generating text contextually, Large Language Models (LLMs) mimic the dynamics of human conversation. They can generate creative responses, tailor their output to the conversational context, and adopt unique AI agent characteristics, such as different dialects and verbosity levels.

According to a report by Future Market Insights, the global conversational AI market is expected to grow from $9.6 billion in 2023 to $47.6 billion by 2033.

What is Conversational AI?

Conversational artificial intelligence (AI) refers to technologies, like chatbots and virtual agents that enable users to engage in talk. These systems leverage vast datasets, machine learning algorithms, and natural language processing capabilities to mimic human interactions. They can recognize speech and text inputs, and translate their meanings across multiple languages.

Conversational AI is transforming the way businesses engage with customers, streamline operations, and make informed data driven decisions.

Whether it's virtual health assistants in the healthcare sector or predictive maintenance in manufacturing, these advanced systems are pushing boundaries, enabling companies to concentrate on delivering lasting business value.

Business Benefits of Conversational AI

Businesses in a wide range of sectors now consider conversational AI an essential tool. It can help businesses meet customer needs and stay competitive. Here are some business benefits of conversational AI:

Boost Productivity and Save Cost: One of the best things about conversational AI solutions is that they can automate tasks. When embedded into any social media or website, conversational AI can do all the boring work so people can focus on the more important things. The organization can save money as a result of increased efficiency and productivity among employees. 

Availability: AI-powered chatbots can handle multiple conversations at once to make sure that every customer gets the attention they deserve without slowing down response times. Because of this feature, businesses can grow and meet the needs of an ever-increasing customer base.

Scalability: Conversational AI is easy to scale up or down based on business needs because it can handle many customers at once. The ability to scale allows the company to maintain high-quality customer service even during busy times, which in turn boosts satisfaction and revenue.

And the list doesn’t end here, Conversational AI Chatbots can help businesses to provide personalization solutions, help monitor performance and more.

How To Build A Conversational AI Chatbot

To build a Conversational AI Chatbot, we at DITS generally follow five steps, which are:

1. Discovery Phase

At this stage, the delivery manager collaborates with the AI software developers and business analysts to explore the potential for the conversational AI for business. The development team’s main focus is to uncover the client’s needs by discussing the company’s goals, pain points, and possible future use cases for the assistant. Once the team aligns on key objectives and priorities, they can outline the features and the platform for the AI assistant. The goal of this discovery phase is to develop a comprehensive vision for the project, including a budget estimate and key performance indicators (KPIs) for monitoring progress.

Maintaining clear communication with stakeholders is essential throughout the conversational AI development process. The more transparent and detailed the communication, the better the stakeholders can align their expectations for the final product. Depending on the project’s scale, the discovery phase may even conclude with a simple product demo, showcasing how the future AI assistant will engage with users.

2. AI Training

Just like any machine learning model, conversational AI is data-hungry. To build a virtual assistant that can effectively take over and handle tasks traditionally managed by humans, it requires comprehensive training. Once we establish our vision and priorities, AI trainers get to work, providing vast amounts of data and diverse examples of possible inquiries. This step is crucial for developing an assistant that can understand intent, gauge sentiment, and respond in a natural, human-like way. Meanwhile, developers focus on integrating the AI into your existing systems, configuring it to respond to specific triggers, such as payment processes and login issues. The end result? A smooth user experience where the conversational AI interacts effortlessly with your company’s infrastructure.

3. Testing

Our team conducts a series of tests assessing how quickly it responds and how well it handles various phrasing. This process goes hand-in-hand with user experience testing, ensuring the assistant is user-friendly and well-integrated with your website and messaging apps. Throughout this phase, we engage with stakeholders on a regular basis, as their feedback helps us shape the AI from the end user's perspective—especially crucial for employee interactions!

4. User Acceptance Testing (UAT)

In the pre-launch phase, stakeholders and end users get to experience the MVP, testing it across various scenarios to see how it performs. They explore its capabilities and evaluate its responses to their queries. If they spot any gaps or have suggestions, our team is committed to refining the product based on their input. Once the MVP impresses and satisfies everyone involved, it’s approved for deployment.  The User Acceptance Testing (UAT) phase is all about ensuring a smooth user experience from the very beginning, so make your feedback count—every detail matters!

5. Post-Production Support

After deploying your conversational AI assistant, our development team doesn’t just step back—we’re here to monitor its performance and provide ongoing technical support! It’s crucial to have AI trainers and software developers involved in the long run, as these systems can’t learn new data by themselves. That’s where our post-production support comes in! Our AI trainers are dedicated to identifying potential data drift and providing the necessary updates to keep your chatbot effective. With our team by your side, stakeholders can feel confident that workflows and client interactions will remain smooth and effective, no matter the changes ahead.

Use Cases of Conversational AI For Various Industries

Here are some Conversational AI use cases for various industries:

Healthcare

Robotics and AI are revolutionizing the healthcare landscape. Conversational AI can help make healthcare services more accessible & affordable, improve operational efficiency and streamline administrative processes like claim processing. These technologies have the potential to assist in diagnosis as well improve and manage patient care, medical data analysis etc. Let's explore these applications in more detail:

Conversational AI Use Cases in Healthcare

Diagnostic Support: Conversational AI assists doctors in diagnosing conditions by analyzing medical images, lab data, and electronic health records to provide data driven diagnostic suggestions. For example, AI helps nephrologists in diagnosing conditions using images from radiology, histopathology, and even smartphone photos.

Symptom Checking and Triage: AI algorithms are increasingly used for symptom checking and triage, helping healthcare providers differentiate between emergency cases and those manageable by primary care physicians. This streamlines the healthcare process and ensures efficient resource allocation.

Clinical Trial Matching: NLP reviews vast amounts of unstructured clinical record data to match patients with suitable clinical cancer studies. For example, NLP can analyze around 10,000 medical charts per hour to identify patients meeting trial inclusion criteria.

Public Health Information Dissemination: During public health emergencies, accurate information dissemination is crucial. AI chatbots, such as those deployed by WHO during the Covid pandemic, distribute reliable information in multiple languages. These bots, trained on real datasets, ensure relevant information is shared across digital platforms.

Appointment Scheduling: Conversational AI simplifies appointment scheduling by enabling patients to book, reschedule, or cancel appointments via chatbots. This reduces the risk of double bookings and allows healthcare staff to confirm appointments with text messages or voice commands.

Patient Support: Conversational AI assists patients who are not tech-savvy by guiding them through processes like account creation and password setting. AI assistants also automate responses to FAQs, enhancing the overall patient experience.

Improved Decision-Making for Healthcare Professionals: Technologies like generative pre-trained Transformers with recurrent neural networks (GPbRNN) are enhancing conversational AI in healthcare. These systems detect patient mood indicators and adjust communication styles accordingly, improving patient experiences.

Administrative Task Automation: Conversational AI chatbots automate administrative tasks, providing healthcare workers with quick access to patient records and appointment data. These chatbots also offer self-service options for patients, responding to queries about health plans and medical services.

Hospital Logistics Optimization: Conversational AI optimizes physician time by facilitating emergency identification, patient prioritization, and expert assignment. These assistants enhance data transparency and communication between professionals and patients.

Patient Insights Collection: Conversational AI meticulously documents patient behavior, offering valuable insights for healthcare providers. Analyzing this data helps in making informed decisions, improving the overall experience for both doctors and patients.

Education

Conversational AI is revolutionizing education by offering numerous applications that enhance both teaching and learning experiences. Here are some key areas where it shines:

Administrative Automation: Conversational AI automates administrative tasks such as enrollment, fee payments, and course registrations. For example, students can interact with a chatbot to discover available courses for the upcoming semester, register for them, and complete the payment process. This streamlines administrative functions and allows educational institutions to allocate human resources to more critical tasks.

Personalized Learning Assistance: Voice-activated virtual assistants offer personalized learning experiences by adapting to each student's learning style and pace. For example, if a student struggles with a particular math concept, the assistant offers additional exercises and explanations to help them understand better. This personalization greatly enhances the learning experience.

Language Learning and Practice: Conversational AI is highly effective in language learning scenarios, offering interactive practice sessions that mimic real conversations. Students can engage in dialogues with the AI Chabot to practice pronunciation, vocabulary, and sentence structure. Additionally, many AI-powered chatbots come with progress-tracking features, enabling students to monitor their improvements over time.

Retail

Over the past few years, customer engagement and buying behaviors have evolved. These days, It's more effective to bring the store to the customers, wherever they are. Brands who are adopting conversational AI are enjoying the benefits of increased sales and customer base. Here we have explained some conversational AI use cases for the retail industry.

Enhancing In-Store Experiences: Top retailers like Amazon and Walmart are investing significantly in improving in-store experiences through conversational AI. AI chatbots provide personalized assistance and real-time product recommendations, making shopping more enjoyable, engaging, and efficient. Customers can ask AI-powered bots for item details, availability, or size assistance, enhancing their overall shopping experience.

Efficient Order Tracking: Timely delivery and accurate order tracking are crucial for customer satisfaction. Conversational AI solutions, including voice assistants and AI chatbots, offer real-time updates using NLP and ML technologies. This streamlines the process, enhancing operational efficiency and reducing the need for manual tracking and customer support.

Data-Driven Decision Making: Conversational AI enables real-time analysis of user behavior and other data, helping retailers make informed decisions. AI chatbots, with natural language understanding and ML algorithms, engage customers, gather feedback, and adapt to changing preferences. This allows retailers to cater to specific customer needs and stay competitive in the market.

Internet of Things (IoT) Devices

Modern homes are increasingly embracing conversational AI devices that utilize speech recognition, IoT to engage with users effectively. Devices like Amazon Echo and Google Home, along with mobile assistants such as Google Assistant, Apple Siri, and Cortana, are transforming the way we interact with technology.

These AI applications offer a variety of features. For instance, you can control your home appliances using just your voice through Google Home or Amazon Alexa. They also keep tabs on devices that can make calls or send messages. Plus, platforms like Amazon Alexa allow you to order food or groceries while adapting to your preferences, suggesting items and services that suit your needs.

Data Collection

Conversational AI is not just about interacting with customers; it also plays a crucial role in helping businesses gather and analyze data that drives smart business decisions. This data helps you understand your customers better and allows your team to pinpoint weaknesses in your current processes.

For example, if a customer expresses anxiety when discussing a particular topic, conversational AI can analyze the sentiment to help diagnose potential issues.

Why Choose DITS To Build Conversational AI?

At DITS, we bring over 7 years of engineering excellence and a proven track record of delivering over 50 innovative tech products across diverse industries, including healthcare, transportation, and retail. Our dedicated AI development teams are composed of specialists in artificial intelligence, data science, software development, UI/UX design, and software operations.

We take pride in having successfully launched over 10 AI-powered software and app solutions that drive real results. Ready to elevate your business? Reach out to us today and let’s discuss how we can meet your unique needs!

AI Chatbot Development Tech Stack We Follow

Drop us an email at info@ditstek.com or call us at +91-623-942-1395 (India), +1(949) 484-0606 (USA).

FAQs

What is the difference between Chatbots, Conversational AI, and Virtual Assistants?

Here’s a comparison of chatbots, conversational AI, and virtual assistants:

What are the best practices for implementing conversational AI in your business?

Integrating conversational AI into your business can transform customer interactions and streamline operations! Start by selecting a use case that truly aligns with your goals for maximum impact. Remember, clean and high-quality data is key for effective AI training, and regular updates will keep it sharp. Don’t skip thorough testing before launch, and ensure your team is on board for a smooth rollout. Choose a scalable and secure platform with strong support, setting the stage for long-term success!

How much does it cost to build Conversational AI?

It varies! The cost to build Conversational AI can range from $15,000 to $75,000, depending on the complexity and performance of the AI chatbots. Your budget will largely depend on the features you need and the specific solutions you’re after.

How can your AI chatbot solutions help my business?

Chatbots do more than just respond quickly—they anticipate customer needs and deliver valuable insights while recommending new products! By analyzing customer interactions, AI can provide tailored recommendations and suggest the best next steps. This proactive approach leads to higher customer satisfaction and boosts your overall customer value.

How long does it take to develop a conversational AI?

The development timeline for conversational AI can vary widely, ranging from a few weeks to several months, depending on project complexity. Usually, the time brackets are determined during the discovery phase, when the team has a solid understanding of the workload and the ultimate goal. 

Our dynamic team of AI software developers and results-driven business analysts is ready to bring your project to life! We’ll create a detailed roadmap that covers every aspect—timelines, methodologies, and deliverables. Together, we’ll ensure your customized AI solution is not only effective but also well-received by both end users and decision-makers.