Our team has recently been contacted by a new client regarding the price for AI chatbot development. Although he specified some of the product requirements, it was hard for us to estimate the project’s cost without the proper knowledge of business needs, concept, implementation time, and overall project scope.
Since we are frequently asked questions about our services’ costs, we have decided to provide a review of existing development platforms and financial aspects of chatbot creation.
In this guide, we will discuss the talking points of chatbot types, their functionalities, applications, and outline the chatbot development budget regarding modern platforms and technologies. So, how much does it cost to build a chatbot in 2020?
Well, to answer this question, we need to gather the requirements for chatbot development, estimate them, and prepare a realization plan. Before we get to the chatbot development cost, we will briefly examine the required technologies and chatbots architecture.
In our blog, we gather global insights on upcoming tech trends, mobile technology, design, engineering, and many more! This allows us to shape better intelligent technology solutions for our clients and to help them build and run a successful business. Get the latest views of our experts.
What are Chatbots and Why are They Becoming so Popular These Days?
The chatbot can be defined as a software application that imitates interactive human conversation using in-built voice commands or text chats. In particular, modern chatbots incorporate spoken language using Natural Language Processing (NLP); moreover, they are capable of accepting commands in both oral and written forms thanks to speech recognition. Speech recognition on chatbots includes voice dialing, speech-to-text processing, and home appliance control.
The technology brings many benefits to eCommerce businesses, healthcare, finance, IT, travel and hospitality, and other end-user industries. According to the statistics, the highest demand for chatbot-based solutions is visible mostly in e-commerce, insurance, financial consulting, and telecommunication.
Nowadays, these are the industries with the highest degree of chatbot acceptance in consumers’ opinion: with 34% of consumers in e-commerce, 27% in medical services, 25% in telecommunications, 20% in banking services, and 20% in insurance consulting.
Examples of the popular voice-enabled chatbots: Amazon Echo, Google Home, and AI Timey.
How do Chatbots Work?
Chatbots are built on the same premise as Google Assistant or Apple’s Siri – they adopt NLP that allows the program to process and analyze large amounts of natural language data. Specifically, the process referred to as parsing helps the bot to decipher text messages relying on complex algorithms that infer the meaning of the query and select an appropriate response or information.
Some chatbots are equipped with remarkable human-like characteristics and can provide a great conversational experience, which is almost indistinguishable from genuine interaction with a human.
Although chatbot technology differs somewhat from that of NLP, more progress can be made through the continued developments of the latter. As we know, there is no such thing as a “perfect” technology – every non-trivial program has at least one flaw, and bots are not the exception, either.
In particular, most bots lack the ability to detect subtle nuances in both written and spoken forms; namely, the current ability of machine-learning algorithms to recognize and respond to non-literal language, indirect questions, idioms, and sarcasm is yet to be fine-tuned and integrated.
This is probably the most crucial factor hindering bot’s transition toward an ultimate human-like interactive agent. So, if we do not pursue NLP developments, we run the risk of chatbots ending up stagnating in the corner of the site or app market.
Human-like approach. As we mentioned earlier, the main technology that lies behind chatbots is NLP and Machine Learning. In fact, some intelligent based AI bots are designed to be closer to humans and imitate human conversation and jokes.
More, businesses usually ask tech companies to integrate their brand’s identity (that can be expressed through colours, fonts, and content) into chatbot design, or even equip the bot to mimic a user’s voice. The best example of a human-like chatbot is Meena.
Rich functionality chats. Rich functionality chats is a general feature of every chatbot. They’re meant to have intuitive menus and provide users with clickable elements, media sharing features, and much more. Further, a clear and effective UI design plays a huge role in providing user satisfaction.
For a conversation to look short and straight enough, the bot should be able to structure vast amounts of information and organize it into short texts.
Extended support. Chatbot’s initial objective is to be available 24/7 and improve our daily routine. It should be able to quickly and effectively provide the required information or help, perform tasks, and conduct meaningful conversations with the user. All the tasks must be done within chatbot, and that interaction history and documentation are easily accessible.
You don’t need to browse other sites for additional info – a well-developed chatbot must provide all relevant information within the app, thus reinforcing the role of an absolute virtual assistant.
Predictive in nature. Modern conversational bots incorporate characteristics that people generally expect from a human assistant – they are designed to sustain the conversation and bring the user closer to his/her goals. Thanks to a knowledgeable database, AI-powered chatbots are able to determine a series of appropriate responses and ensure user’s involvement to the greatest extent possible.
Apart from the ability to give detailed, concise, and relevant answers, some of the most recent conversational bots can produce texts that are sprinkled with humor or blunt with metaphors. For example, Facebook has developed a chatbot called BlenderBot, which can handle extended, human-like conversations. A few months ago, Facebook’s reps stated in the blog that it would literally outperform any AI bot existing today.
Accordingly, BlenderBot is endowed with qualities similar to the human mind, e.g., it is able to discuss and debate various topics, assume a person’s feelings, and even express empathy. However, the company does not plan to commercialize it – this is purely research at this point.
Simple purchase process. Chatbots not only collect and provide useful information but are widely used for B2С customer services. Apart from being good companions, they also let customers order goods and services right from the messenger. Ecommerce chatbots have become very popular these days: stats suggest that by the end of 2020, about 80% of businesses will use chatbots for their online businesses.
So, another great benefit of using a chatbot in your business is its role in improving customer service communication. If the customer had a positive experience with a business, he is more likely to return and make a purchase again. That’s where the chatbot comes in: the next time the user returns to the app, the program “fills” that missing role of a (virtual) sales assistant.
Moreover, eCommerce chatbots save the information on purchases you’ve made to offer more accurate goods. Hence, chatbots help companies meet orders efficiently.
Let’s take a look at the main points of Artificial Intelligence popularity and understand how to develop your own Artificial Intelligence Assistant in detail.
Types of Chatbots
Chatbots can be categorized into two major types: Rule-based and ML-based. In order to choose the right technology to help grow your business, you’ll need to understand their differences. The following section describes the standard chatbot types and their functions.
These chatbots are developed based on scripted questions using ‘if/then’ logic. Specifically, rule-based chatbots can handle a list of simple queries a user may ask and answer them in the form of prebuilt responses.
For example, you can integrate them within your weather forecast app or inside a business platform for customer support. In this case, your bot will serve well in providing relevant info on forecast conditions or answering FAQ questions.
Machine Learning-based chatbots. Unlike rule-based chatbots, these are much more effective at handling complex questions. Typically, ML-based chatbots can hold meaningful conversations as they are able to process and learn from complex dialogues and understand users’ intentions. The main advantage of AI chatbots is the ability to analyze the collected data and provide better feedback.
How to Build a Chatbot with a Self-Service Platform? (Development Costs)
Self-service platforms provide access to a large volume of tools needed for building a bot by yourself. Some of them require background knowledge and technical skills, while others allow you to craft your bot without coding. We are going to focus on the latter.
Creating a chatbot might seem like a big deal, but if you have all the necessary instruments, the design process will go smoothly and easily.
For example, with IBM Watson open multicloud platform, both developers and non-coders can build a powerful chatbot application implementing the fundamental concepts like Intent, Entity, Dialog, etc. Watson allows you to create your Chatbot application from scratch or make the development process much faster with pre-built applications and tools.
Moreover, the platform allows you to deploy your app over IBM Cloud command-line interface (CLI). Specifically, IBM Cloud offers a web console where you can deploy your application with a DevOps toolchain while simplifying the management of continuous delivery processes.
Watson has helped many businesses across the globe to integrate AI into organizational intelligence and improve employee and customer engagement. Bots created with Watson are powerful and intelligent assistants that have proved to be particularly useful in global data, insurance, finance, airline, energy, and many other industries. Watch this video to learn more about the IBM Watson platform.
The table below represents the Watson Machine Learning Cloud pricing. If you want to learn about Watson’s pricing plans for IBM Watson® Machine Learning on IBM Cloud Pak™ and Watson Machine Learning Server, click here.
Another platform we’d like to draw your attention to is Google Dialogflow. This is a Google-owned NLP platform that offers an end-to-end development package for a seamless chatbot creation. Dialogflow allows its users to design and integrate Conversational UI into mobile apps, web applications, IoT devices, bots, and so on.
What is more, it integrates with many popular conversation platforms like Google Assistant, Slack, and Facebook Messenger; the main advantages of using Dialogflow are cost and ease of access. So, if you’re planning to build an intuitive chatbot that infers the end-users’ intent and provides a high-quality conversational experience, this is your path.
Below you can see the platform’s monthly packages:
Last but not least is the Azure Bot Service platform that provides a set of tools for bot development, testing, and deployment processes all rolled in one. Developed as the Microsoft cloud computing ecosystem, Azure enables businesses to build intelligent, enterprise-grade chatbots that can provide customer delight and tailored customer service for many industries.
With an open-sourced SDK available for C#, Typescript, and Python, users can easily connect Azure bots on popular channels and devices. If you are a business owner and don’t understand how programming works, you will need to hire a highly-skilled development team to design and build a fully-functional software application. To speed-value your chatbot, it is crucial that you integrate features like Language Understanding, QnA Maker, and Language Generation.
By the way, the platform is currently offering a 12-month free account. You can learn about Azure’s pricing here.
Create a Chatbot from Scratch
In case you are willing to build your project without relying on frameworks or development platforms, you can always hire a software development team to build a chatbot from scratch.
By ‘software development team,’ we mean an agency that provides web and mobile application development services. To add a chatbot to your existing messenger means to develop a server-side app that will integrate chat through an API. In this respect, dev teams need to have a diverse range of skills and specialist knowledge to choose the best technology.
Here at Jelvix, we use either Java or Python programming languages.
You also need to make sure that your future development team consists of A-grade professionals that are able to integrate NLP to your bot. There are many existing NLP engines that help devs empower their bots, such as Opennlp and Nltk.
Now, let’s take a look at the key drivers impacting the development costs and timeline:
1. Integration with one chat – the duration depends on the type of the bot, its technical complexity, and the options you choose, but on average, it takes 40+ hours.
2. Communication interface specification (depends on the number of teams).
- Command-line user interface (CLI) – the development time takes at least 40 hours.
- Natural language user interface – at least 120 hours.
3. Business logic.
- Adaptation of a business logic (providing that the web application or APIs for a mobile app already exist), depends on the number of business rules (120+ hours).
- Creation of business logic from scratch (depends on the amount of logic). The development time takes at least 160 hours.
Now that we have studied all aspects of designing, building, and integrating the chatbot technology, as well as examined the leading platforms that provide frameworks and APIs for its implementation, we can assume the total estimated cost of such a project. One commonly held misconception is that chatbot development incurs significant expenses. While that might be true for complex AI-chatbots powered by NLP and Machine Learning technologies, in case of a scripted bot, the price is significantly lower.
Given the diverse circumstances a software development team has to consider, the minimum price for custom chatbot development starts from $6,000.
On the contrary, the price for a very sophisticated AI-chatbot used to handle customer support ranges a lot. As we have mentioned previously, such bots are able to present customers with a range of topics, recognize natural language, answer complex questions, and provide personalized offers, news, and content distribution. Nevertheless, If you provide your software with payment APIs, such chatbot will become your ultimate competitive advantage.
Ready to Build a Chatbot?
We hope this article will help you make the right choice regarding the development tools for your future project. Whether you decide to hire a professional team of coders or rely on a convenient development platform, make a well-informed and rational decision!
In case you are interested in our services, you can always contact us for a free consultation. Together, we will talk through the main points of bringing your unique chatbot to life.
Need a qualified team?
Unlock new business opportunities with the first-rate dedicated development team.