If you are looking to start understanding the foundational concepts of building a chatbot, this article will discuss the various technology stacks that are required for building a chatbot.
While chatbots are no longer a new technology, it is a technology that is being broadly adopted as companies start to understand how to use data to help inform their strategies and decisions in their business.
A simple AI/ML project that businesses can start leveraging is implementing chatbots. Being able to build a very simple chatbot and then continue iterating with new features is one of the easiest projects to start that can also feedback into creating informed decisions with the data on what to build next.
As a former program and product manager (yes, I was both), I’m going to share with you information on the various types of technologies that chatbots use so that you can have an understanding of the technology stacks that are available to deploy your next chatbot project.
Developer Programming Languages for Chatbots
The market for an enterprise level chatbot has not leveled out yet because the technology is still maturing due to the underlying natural language and linguistic processing. There are a multitude of articles that say that the market for chatbot vendors will continue to change and evolve fairly quickly into 2022.
This means that for a developer looking to getting into implementing chatbots as a career may want to continue to develop on a multitude of programming languages. Languages include:
I would also recommend getting the certifications from Amazon Web Services, Microsoft’s Azure, and Google Cloud Platform as a starting point as these certifications (especially the general ones) will introduce you to some of the larger cloud providers and what they have to offer in terms of chatbot services (and ancillary services that can be included). This should not be overlooked. Being able to architect services instead of starting from scratch will allow your project to get off the ground quickly. In addition, often, with these larger platforms, the cost to start isn’t expensive, so it will allow you to experiment and find out what you like and don’t like before moving towards a more boutique solution.
Software as a Service Tech Stacks for Implementing a Chatbot
There are several chatbot messaging platforms that don’t require starting from scratch with code. You don’t even need to know programming or coding in order to start building a chatbot.
There are a multitude of options if you are looking to implement a chatbot without code. Google’s Dialogflow offers an edition of their solution that provides an easy, front-end interface that does not require a developer. For example, this Google Dialogflow tutorial for implementing a chatbot in WordPress allows you to see step by step what it takes to implement a bot onto a common blog website.
Platform as a Service Tech Stacks for Implementing a Chatbot
With PaaS, while you don’t have to worry about the underlying infrastructure, you do need to understand the architecture and what services can be put together to make a bot that serves the needs of your customers and stakeholders. Here are some examples of popular bots that are out there.
Amazon Lex is a service offered by Amazon Web Services as a PaaS that will allow you to implement a chatbot including one that includes Voice Recognition.
Azure Bot Framework and Bot Service
These are two services offered by Microsoft’s cloud service, Azure. Bot Framework and Bot Service are two separate services that can be used together (along with LUIS and QNAMaker) in order to provide a robust framework for your chatbot.
List of Platform as a Service Tech Stacks
- Avaamo Conversational AI Platform
- Amazon Lex
- Convy AI
- Dialog Flow
- Watson Assistant
- Kore.AI Bots Platform
- Communication Studio and Live Agent
- EVA Platform
- Oracle Digital Assistant
- Rasa Open Source and Rasa Enterprise
- Rulai Conversational Computing Platform
- Smart Bot Hub
Example of Technology Tech Stack for Chatbots
Here is an example of a technology tech stack that could be used for implementing a chatbot. I know with a lot of large enterprises, you could be an AWS shop or a Microsoft Shop, so I’m going to share with you an example using Microsoft:
- Microsoft Teams Bot – This is the interface that customers would interact with.
- Azure Bot Service – Its a serverless, intelligent bot service that scales on demand.
- Microsoft Bot Framework – It allows developers to connect bots that interacts with Microsoft Teams Bot (and other services), including developing iFrames to embed into websites.
- Microsoft QNAMaker – This is a knowledge management tool that allows you to create FAQ into the tool and then be able to manage the knowledge and various levels of questions you could potentially ask.
- Microsoft LUIS – This service makes natural language understanding easier.
- Microsoft AppInsights – This provides metrics on API calls and transactions.
- PowerBI – This is a reporting tool that allows you to pull data in from Microsoft Teams and Bot Service.
What are the technologies needed for making a chatbot?
The technologies needed for making a chatbot include choosing technologies that provide an agent interface, middleware stack that includes a natural language processing platform that can manage various models, public and private cloud deployment capabilities, interface that allows non-developers (and business stakeholders) to easily design conversational experiences.
How is natural language processing (NLP) used in a chatbot?
Natural language processing (NLP), also often referred to as natural language understanding (NLU), allows developers and business leaders to understand the input and use various statistical techniques to interpret the input into an output that allows for the identification and/or prediction of future behaviors as well as driving towards continued personalization.
Being able to understand the input that a chatbot user is providing and then interpreting it with varying levels of technology and statistical models allows business leaders to better understand the language that their users are using and also see the delta on how they communicate certain topics vs how their customers communicate certain topics.
How expensive is a chatbot technology stack and what does it cost to invest?
Chatbots can range from free all the way to the $500,000 for an enterprise chatbot (depending on the number of services used). With most cloud providers, the startup cost for testing out a bot is free. As you start consuming services, the price will scale along with the volume and services used. Boutique chatbot companies often charge a large cost upfront, and then adds on per-volume pricing.
For example, you can implement a free chatbot using Google’s Dialogflow. As you start consuming more services and volume, the price like most cloud services will scale with volume and usage. In addition, other costs you may consider include the following roles on-staff:
- Conversational Designer and/or UX Designer
- Program Manager
- Product Manager
- Business Analyst
- Data Scientist
Do I need a data scientist in order to create a chatbot?
If you have a simple use case for a chatbot, you do not need to onboard a data scientist as part of a minimum viable product. As you increase the capabilities of a bot, and the data continues to grow in volume, you may need to find efficiencies in being able scale to interpret large amounts of data. There are various tools that will allow you to review and interpret the data between AWS Sagemaker and Microsoft Azure Machine Learning Studio, all not requiring data scientists.
When you’ve exhausted resources being able to use these solutions along with the need to scale the team and decrease the onboarding process, you can implement a data scientist to close this gap as well as create more sophisticated models for your chatbot.
Summary of Chatbot Technology Stacks
Even though chatbots have been around since the 1960s, the current demand and popularity is due to the rise and trend of artificial intelligence and the ability to now process big data quickly. As consumers start seeing chatbots with larger companies that have been able to be agile and adapt these technologies, it’s becoming a new normal, standard experience that is expected by everyone. There are many benefits in implementing a chatbot, and being able to allow your company to have the agility and the skill set to implement chatbot in which ever flavor will ultimately provide your company with more data to help make better decisions as well as streamline and reduce friction for your customers interacting with your company.
With chatbot development and learnings getting better every day, the chatbot landscape will continue to evolve and change the landscape dramatically over the course of the next few years. Chatbots play an important role in not only answering customer’s frequently asked questions, but also becoming engaged int eh sales process and closing sales. As the landscape continues to change, investing in understanding HOW the landscape is changing with this technology will be crucial especially if you are an early adopter to this technology as it becomes proven over time.
Other Chatbot Blog Posts You Might Find Helpful
- Bot Development Lifecycle
- Azure Bot Service – Explained Simply
- Key Performance Indicators to Measure for Chatbot Implementation
- Azure vs GCP vs AWS Chatbot Services and Solutions
- Bot Framework vs Bot Service – What’s the difference?
- Conversational Marketing and Chatbots
- Chatbots and Digital Marketing Techniques
- Chatbot Architecture