Customer service chatbots have come a long way from rule-based systems that simply relay predefined answers. Modern chatbots apply natural language processing (NLP) and machine learning (ML) to mimic human conversations, understanding the intent behind customer messages—even when the phrasing is unclear.
With their ability to grasp context, smart chatbots deliver more accurate and human-like responses. They also learn and improve over time, becoming better at engaging with users and meeting their needs.
At ElifTech, we’ve integrated chatbots into many projects and explored various chatbot development tools along the way. In this article, we’ll share key insights from our experiences with these tools.
Why Businesses Use Intelligent Chatbots
Many well-established businesses have already integrated AI-powered chatbots into their customer service workflow. These smart assistants, which have become increasingly human-like in recent years, enhance customer service, make life easier for sales and support teams, reduce costs, and help businesses scale. Here’s how they do it.
Improved customer service
Modern customers expect immediate responses, and AI-powered chatbots can deliver just that. They can reply within seconds, reducing wait times—especially during peak hours when human agents might be overwhelmed. No wonder that 63% of respondents in a Salesforce survey believe generative AI will help them provide quicker support.
Automated manual tasks
Chatbots can automatically handle common questions and requests. While this may seem like a slight advantage, the impact can be substantial. For example, an AWS-based chatbot feature helped one bank reduce call volume by 30%.
Reduced operational costs
AI-powered chatbots benefit employees and customers and can significantly reduce operational costs. Businesses can save money by requiring fewer agents to manage a larger volume of inquiries while still providing excellent service.
Enhanced scalability and 24/7 availability
Chatbots can operate around the clock. This means customers can receive support anytime they need it. Plus, since chatbots can handle multiple interactions simultaneously—unlike human agents—they can help meet staffing needs as your business grows.
Conversely, a chatbot can create friction in your workflow if you implement it incorrectly.
Popular Frameworks for Chatbot Development: ElifTech’s Experience
There are plenty of ways to develop a chatbot, and you might first think about custom development or fine-tuning a large language model (LLM) to meet your specific needs. But why reinvent the wheel when there are fantastic chatbot builders available?
These tools come with built-in features for natural language processing (NLP) and dialog management. Many also offer drag-and-drop functionality and pre-built templates, so you can focus on tailoring the chatbot to your requirements instead of getting bogged down in complex development tasks.
Here are some of the chatbot builders we’ve explored in our experience.
Amazon Lex V2
Amazon Lex V2 is an upgraded version of the AWS conversational AI service. Powered by the same technology that drives Alexa, it’s designed to help you create AI chatbots and voice assistants that produce human-like responses. The platform has built-in intents for handling common requests and generative AI capabilities for streamlined bot creation.
It also offers a zero-code chatbot builder, but it’s less straightforward than the ones in Botpress or Dialogflow.
With Lex V2, you can create chatbots that manage tasks beyond simple responses (like performing calculations or interacting with other services) thanks to AWS Lambda. Notably, you only need a single AWS Lambda function for all intents.
While the service offers integrations with various messaging platforms, there is no widget for web apps. To integrate the chatbot into your app, you must build your own chat interface using AWS SDK and connect Lex in the background. This will allow you to send user text and session data to Lex and receive responses from the bot.
Dialogflow
Dialogflow is Google's solution for building chatbots. It offers two distinct virtual agent creation services: Dialogflow CX and Dialogflow ES. While Dialogflow CX is tailored for complex conversational applications with advanced features and a user-friendly interface, Dialogflow ES is more suited for simpler interactions with a straightforward intent-based structure.
Overall, Dialogflow’s interface for building chatbots is quite intuitive. For each intent, you can easily connect your API to fetch custom data (unlike Amazon Lex, where one Lambda function serves all intents). Dialogflow also offers a variety of valuable integrations, including a widget for your web app.
In Dialogflow, it’s easy to control how and where generative AI will be applied. For example, you can set up your chatbot to process requests through your own backend and reach out to an LLM solely for generating responses. This way, the intent triggers the generative model to create answers while you maintain control over when those responses are provided.
Botpress
Botpress is an open-source conversational AI platform featuring an intuitive drag-and-drop chatbot builder. It offers both cloud-based and on-premise versions, with the latter offering more limited ML capabilities.
The platform supports numerous integrations, including connections with HuggingFace models, and allows users to publish chatbots across various channels: websites, Facebook, and WhatsApp.
Like Dialogflow, Botpress allows you to create an intent-based chatbot that generates responses using a large language model. This combination of clear intent-based logic with text generation capabilities results in more natural and contextually relevant answers.
We can say that Botpress looks promising, especially for clients not restricted by security audits that might prevent them from using this service.
Rasa Open Source
This open-source chatbot builder was actually the first solution we used to create chatbots.
Rasa Open Source is a fantastic option for dev teams that have the time and expertise to dig into code and set up the infrastructure for building bots. However, when it comes to ML algorithms for processing user intents, Rasa Open Source isn’t as robust as some other platforms. While it does offer integrations with popular LLMs and messaging platforms, such as WhatsApp and Amazon Alexa, these integrations require some coding effort.
On the positive side, Rasa has a cloud version and an on-premise option. This means it can run offline, which is especially beneficial for applications in environments with limited internet connectivity, like IoT devices.
Einstein Bots
Einstein Bots is a drag-and-drop solution for creating chatbots that focus on customer service tasks, such as answering questions, managing billing inquiries, and scheduling appointments. In fact, the chatbot feature in ClassWallet, one of our projects, relies on this tool.
Backed by Salesforce, this platform integrates with other products from the provider, making it an ideal choice for e-commerce businesses that rely on the Salesforce ecosystem. However, this strong compatibility with Salesforce has a caveat: you'll need strong expertise in Apex to build features beyond what the no-code interface allows.
For instance, to enable ClassWallet’s chatbot to fetch custom data, we had to write a GET Data class in Apex. On the bright side, Einstein Bots can be deployed across various channels, including SMS, Slack, Facebook Messenger, and WhatsApp.
The primary model used in Einstein Bots is Einstein GPT. This model is trained on Salesforce's proprietary dataset of text and code. Additionally, you can integrate other LLMs using the Bring Your Own LLM (BYOLLM) feature.
Microsoft Bot Framework
The Microsoft Bot Framework is quite similar to Amazon Lex in terms of capabilities. It includes the Bot Builder SDK, which supports multiple programming languages, such as C#, JavaScript, and Python.
This framework caters to users with varying levels of technical expertise. It offers both a development framework and a no-code chatbot-building interface, making it accessible to everyone. Thanks to its native integration with Microsoft Cortana, you can create chatbots and voice assistants.
However, while Lex is primarily focused on the cloud and tightly integrated with AWS services, the Microsoft Bot Framework offers more flexibility in deployment options. Bots built with this framework can be hosted on various cloud platforms that support web APIs, including AWS. Plus, on-premise deployment is also an option.
Our Insights into Choosing the Right Framework
Using a chatbot development framework can make creating a smart assistant much easier. However, it’s crucial to choose the right solution for your needs. Otherwise, you may find the implementation process becoming more complicated as you try to bridge the gap between your requirements and what the framework can deliver.
To avoid unnecessary complications, we recommend carefully looking into your needs. Before choosing the framework, consider the following:
- The type of assistant you need. Are you looking for a voice assistant or a text-based chatbot? Remember that all the platforms on our list allow you to create text-based chatbots, but only a few support voice technologies.
- Budget. While Botpress and Rasa Open Source are available for free, AWS Lex operates on a pay-as-you-go model.
- Infrastructure requirements. Consider whether you plan to deploy the chatbot on-premises or in the cloud. Different chatbot builders have varying levels of infrastructure compatibility. For example, Microsoft Bot Framework-based virtual assistants can be integrated into a wide range of hosting setups, while this flexibility may not apply to Dialogflow.
- Security requirements. If your operations require strict security measures, you might need a bot that can be built and deployed on your premises. However, if you still need internet connectivity, AWS Lex and Microsoft Bot Framework offer options like Virtual Private Cloud (VPC) for enhanced security.
- Available expertise. Each framework has its underlying technologies. For example, you’ll need a solid understanding of Apex to get the most out of Einstein Bots. If minimizing coding is a priority for you, Rasa Open Source may not be the best fit.
- The current stack. Take a look at your existing solutions. If you already use Amazon’s ecosystem, AWS Lex or Microsoft Bot Framework might be ideal choices. If your operations are running on Google Cloud, Dialogflow would be the way to go. And if you're a Salesforce user, Einstein Bots is likely your best option.
Answering these questions will help you make a more informed choice when selecting a chatbot builder. Lastly, here are a couple of important considerations to keep in mind.
Invisible Issues with Chatbot Implementation
Developing a chatbot is a significant step, but it's just part of the equation. Your chatbot implementation strategy can fail unless you consider other important factors, including data privacy, integration challenges, and ensuring that your virtual assistant is fed high-quality data.
Data privacy concerns
As chatbots handle sensitive customer information, data privacy becomes a major concern. It's not just about the potential legal issues that can arise from failing to comply with regulations like GDPR and CCPA; customers also expect their data to be kept private.
A survey by Adobe, which included over 6,800 consumers from 13 countries, revealed that 63% of respondents consider it critically important that brands use their personal data responsibly and securely. In fact, this need for responsible data use outweighs the desire for consistent experiences across channels, personalized recommendations, and even quick support from chatbots.
Therefore, providing this assurance to customers should be a top priority when integrating an intelligent chatbot into your support workflow.
Integration complexity
Effective integration with existing customer service systems, such as CRM platforms and ticketing systems, is essential for chatbots to access customer information, manage inquiries, and escalate issues to human agents when necessary. However, because these platforms often use different tech stacks, many organizations face challenges in achieving seamless communication between the chatbot and other systems.
This is why having a solid integration strategy is key to unlocking your chatbot's full potential.
Data quality considerations
No matter how advanced your chatbot is, it will always depend on the quality of the data you train it on. Poor-quality data can result in inaccurate responses and misunderstandings during customer interactions.
That’s why it’s important to ensure that the data used for training your chatbot is accurate, comprehensive, and up-to-date. This may involve investing in data cleaning and enrichment processes, as well as continuously monitoring and refining your AI models to keep them performing at their best.
Conclusions
If your business handles a large volume of support requests, having an intelligent chatbot can be a game-changer. It can relieve pressure on your agents and help manage your staffing budget, all while enhancing your reputation by providing customers with immediate support. However, the key is building a chatbot that truly delivers these benefits.
This article doesn’t cover every possible framework for creating chatbots, but it does highlight some of the most outstanding options available. We hope our insights serve as a guiding light in your customer service generative AI strategy.
In addition to providing an overview of bot builders, we're here to offer personalized advice for your specific situation—just contact us. You can also explore more helpful insights on implementing chatbots by checking out our blog.