How Much Does A Chatbot Cost?

The short answer is – it depends. I know some readers might be looking for an exact number but this would be misleading. From our experience we see that the needs of companies vary greatly as do their technical capabilities. As such, to give a simple answer in pricing is akin to answering with a specific price to the question – „how much does a good car cost“. Everyone’s expectation to what a good car is, is different. Same with chatbots.

However, there are specific things that can drive up the cost of your virtual assistant development. Let’s dig in.

The cost of a custom built conversational AI solution largely depends on the features that a company needs. The more advanced features you want in a chatbot, the more it will cost. Knowledgeable planning and clear perception in the early development phase will allow to estimate the total cost of an AI solution accurately.

As a rough guide we have outlined below some of the most common chatbot features and their relative cost. This can vary but gives an understanding of the features and the required investment levels.

Conversational AI solutions come in all shapes and sizes, but the amount of work required to be put in can vary greatly between companies and industries. The very first question that every company in need of an automated virtual assistant should answer is what is the business case and the goal. In other words, every company needs to know their Key Performance Indicators (KPI) to accurately measure business results over time and continue to do so after implementing an AI solution. Intent understanding accuracy, customer satisfaction, goal completion rate, steps per conversion and many more can be used to evaluate conversational AI impact on the business results.

Moving on to the next variable – the amount of topics. Every topic addresses certain problem or a question, that the company’s customers usually have. For example, a topic for a bank can be „When does my credit card arrive?“. Naturally, the most frequently occurring topics should be addressed first. Existing email or chat data could be used to speed up the training of Natural Language Processing algorithms. This way, the early steps of a chatbot will be made in the right direction and the overall costs could be significantly reduced. At an early development stage, 50-100 unique topics should cover most of the high priority enquiries. Later, when the conversational AI gets perfected, the number of topics may rise up to 1000 for bigger companies.

The amount of supported languages also contributes to the pricing of a conversational AI. The better part of today’s AI solutions are optimized only to English language. This is where AlphaBlues Natural Language Processing algorithms start to shine. As of this date, our AI  supports all European languages, Russian and Arabic. AlphaBlues conversational AI can learn as many languages as needed, although the best approach has proved to be implementing one language per bot. Those in need of more languages can easily use the existing bot and copy its structure and logic to a new bot with a new language.

In the ideal world, virtual assistants can handle all customer enquiries, but in most cases symbiosis between virtual and human agents is still the way to go. To some extent, the price of a conversational AI solution will be determined by handover to agent functionality and its scope. If there is a need for live agents, the total number of concurrent users have to be chosen. Our solution, AlphaAI connected to AlphaChat provides optimal customer experience by seamlessly utilising best of the both worlds in a highly customizable interface. However, AlphaAI can also be integrated with most of other popular customer service desk applications. Furthermore, customers may want to have a chatbot window available, for example, on main site, but live agents on subtopics. Context and URL specific configurations are more complex and may increase the total cost.

If a company is considering to take a step towards chat automation and debating about the costs, one should also keep in mind the hosting environment. Companies can either choose on-premise or cloud hosting options. Both public and private cloud hosting options are available and are usually easier to implement than on-premise. In addition to that, intelligent user authentication system, that enables virtual assistants to engage with the customers in a more personalised way, may be chosen. AlphaAI virtual assistants remember the customer and continue their conversation from the word it was paused.

Even though the main structure of a chatbot can be built and launched in about 2-3 months, further development and support is vital in order to keep up with the company’s customer needs and desires. In some ways, conversational AI resembles human intelligent. To stay relevant and gain edge in a fast-changing world, constant learning is necessary. Customers can decide if they want to train their conversational AI independently or use monthly training service and insight-analysis. In the end, the quality of a conversational AI is largely determined by the data and the ways it has been trained. We suggest that every company in need of a conversational AI solution finds an in-house trainer in addition to the bot provider services. In this manner, technical expertise and inside knowledge about the company’s vision can be combined.

Developing a customized conversational AI from scratch requires resourceful planning, time management skills and clear communication. Unanimous understanding between the customer and the AI developers is vital in order to transition smoothly to more automated customer service solutions. More about conversational AI setup process in the next posts!

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