Did you know you can create a Messenger chatbot without writing a single line of code with Chatfuel in a couple of hours? I, myself, created one, to bring leads on my Facebook Page, in less than an hour.
My point is there is no certain rules regarding how much time goes into developing a chatbot. And as with most pieces of software, apps and programs, it all comes down to feature sets, structure and value.
However, unlike conventional software and mobile applications, chatbots do not need chic UI design, provision for numerous mobile platforms, extended development cycles with the app store reviews, and exclusive acquisition strategies to draw users into apps.
But they still require the conventional software modules like backend to be built around them. The UI or frontend of a chatbot matches the platform it is targeted on: Messenger, Slack, Telegram, etc., which reduces the development time considerably. We live in the world of multi-platform development with each platform having its unique design language and, thus, UI. Did you compare WhatsApp on iOS and Android lately?
Implementation of a chatbot
A chatbot needs a backend to manage an incoming stream of messages from multi-channels and parse them with NLP services such as Watson Conversation, Facebook’s Wit.ai, Api.ai, or LUIS. The backend enforces business logic, enables integrations with existing systems, makes the bot intelligence and, thus, lead the conversation with the user.
Most of chatbot platforms support both .NET and Node.js server-side SDKs, so you can securely choose the programming language of your choice.
The total time to setup server and deploy backend with AWS, Node.js and Microsoft Bot Framework takes around 4 hours.
Once the backend is in place, you must create modules to integrate with each channel. Every channel integration is unique. However, they, in general, follow the exercise of adding an endpoint to send and receive chat messages that depend on access tokens authorization. Further, you must bring some channel-specific UI by integrating quick reply buttons under chat bubbles, or visual cards relevant to the conversation going on.
Natural languages processing
An integral part of a chatbot development is the integration with Natural Languages Processing (NLP) services. Luckily, you don’t have to write your own NLP algorithm. NLP empowers bots to strike natural conversation with a human, and is available as APIs from Google, Microsoft, IBM and Facebook.
Since your bot is going to receive chat messages in everyday language from a person, it needs integration with one of the Natural Language Processing (NLP) services to extract intents and entities out of the message in plain language.
Integrating an NLP service with the help of one of the above APIs is straightforward. However, training the NLP intents and entities takes time and considerable coding. You will be required to map entities to specific objects that are present in an existing system such as Products, names, and Identifiers etc.
To authenticate user input received, you must put some business logic authentication rules to validate the received data. It can range from simple validation such as a phone number must be all numbers and an email address must be in [email protected] format. For better validation, you can call a web service to validate a pin code or city name against a public database. Validation will required many hours of coding in Node.js and .NET
This is where chatbot development goes complicated and can take more 200 hours of development and coding. Creating natural replies to give way to intelligent, meaningful conversation based on the NLP intents and entities is an endless journey. You want to make your chatbots as natural as a human, which is impossible. So, you keep improving it.
Moreover, each conversation must be tailored to a specialized algorithm and features a minimalistic navigation, so that it is easy for a user to start over. While you can use simple if-else trees, some state-of-the-art deep learning algorithms is what customers expect these days.
Integration with existing systems and services
Chatbots aren’t all about engaging in endless gossips with random dudes although they are getting pretty good in that area. Every chatbot serves a purpose and most of the purposes are commercial in nature such as ordering items, buying stuff, booking flights, etc. Your chatbot must have integration with the services you think your customers will ask for.
The integration will include business logic authentication and instructions, perseverance of data and any other processes that might be essential as part of the business operation. A backend with good service layer is the cause of saving some development time here.
Control Panel (optional)
Once your chatbot is out in the market and your users are all over it, you might need a backend to track chatbot conversation history, users, error logs, etc. You can also add a simple analytics metrics to calculate your bot’s performance. This is going to take a lot of clerical work including long code blocks in Node.js + Angular 2 and .NET. Moreover, setting up the database can go easily a week.
Time that goes into developing a chatbot
Let’s calculate how much time and cost an intelligent chatbot that uses Natural Language Understanding (NLU) services, is integrated with an existing set of systems, and will be published on Facebook Messenger take to develop.
As mentioned above, there are three stages in chatbot development.
If you are outsourcing to a firm that works 8 hours a day, 5 days a week, or 40 hours/ week, it will take 14 weeks or around three and a half months.
Calculating the cost of developing a chatbot
A Node.js developer easily charges $50/ hour. On the other hand, an AI developer with proficiency in NLP won’t cost any where less than $100/ hours. Oracle developers also cost around $40/ hours.
Is it too costly for you? Perhaps, you need a better chatbot consultant. Perhaps, you’re unclear of your requirements? Let us help.
Integrate chatbots to improve your customer service experience
- Natural Language Processing (NLP)
- Build across existing services and data
- Train your chatbots just like humans