Effective training is often said to be one of the keys to getting a Conversational AI solution working in a truly helpful way. But before we can even move to AI training, we have to get the core structure and topics mapped out in a manner that is both logical and easily manageable even years later when the number of topics might have increased tenfold. The process of bot building and content creation starts from a thought process: What kind of use cases are we trying solve with the use of automation? Who are the persons at the other side
How To Build A Chatbot?
Quantitative evaluation is a vital part of an AI project, but there is little to no standardization within the topic today. In this post, we will look into the ways how Conversational AI can be measured, how the procedure varies between different roles & users and what to do with all of this information. Why measure the performance of a virtual assistant (VA)? Developing a solution based on Conversational AI can introduce a lot of uncertainty to the process when starting out. The early days of a VA can even be characterized as controlled chaos. One of the easiest ways
Not every chatbot and Conversational AI project is a success story. While every company is different and unique in their own way, there are some common pitfalls that should be avoided. ❌ Too High Expectations Our experience has shown that when customers start out with projects, their expectations tend to differ from ours. As a vendor we try to set realistic expectations right away and try to sync with our customers. Going into the project with wrong mindset and expectations will create misunderstandings later on. One common assumption is that chatbots are easy to make. There is some truth to
In this blog post we cover the practical usability of Estonian language speech-to-text and text-to-speech technologies. At AlphaBlues we are focused on building high-end virtual assistants on our own AI platform. Recently we have done work on integrating voice capabilities into our virtual assistants. As we tested Estonian language solutions we thought to share our experience with the aim of giving people who are interested in using such technologies for practical purposes a quick overview. Voice-controlled devices are growing in their usage. Juniper Research estimates that there will be 8 billion digital voice assistants in use by 2023. Today, AI-based
In the previous post, we discussed the first steps in AI training processes. In this post, we will look into the possible training phases when the conversational AI is already up and running. In some ways, AI trainers are the parents of the chatbots. There needs to be skillful parenting in order for the child to grow up and lead a successful independent life. Chatbots behave in the same way. You do not want to underdo it and not particularly overdo it either. We have found out that training and analyzing about 10% of the monthly discussions will be sufficient
In the previous post, we dived in to the chatbot building process. In this post, we examine AI training. It is often argued that conversational AI will replace human workforce and cause unemployment across many sectors. It is true that increasing number of companies are interested in more efficient ways of customer service, but that does not mean humans will be replaced in the near future. Behind every successfully automated customer service lies an active support team. The core intention behind automation is to make business more efficient. Optimized solutions usually do generate more business opportunities, thus pushing companies to
As discussed in the previous post, many aspects of conversational AI implementation are dependent on the details. The same applies to the tempo of the setup process. For smooth development, it is in both customer and developer interests that the customers know what they want. Concrete communication between businesses help to prevent additional tasks in later stages. Although, demo of the conversational AI can be built in minutes on the company’s website, we tend to utilize 1-2 months of development time plus one extra month of silent live for testing purposes. We have found out that with this amount of
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.
If you are looking to buy chatbots then don’t do an RFP. Instead do proof of concepts with 2-3 vendors. This has been the main learning from the recent Forrester report “The Forrester New Wave™: Conversational AI For Customer Service, Q2 2019” and based on our experience I wholly agree with this finding (you can see the Forrester summary video from this link here). Chatbots (or virtual assistants) have become more established over the past year and their value becomes increasingly proven in the realm of contact centers and sales. As more companies are looking into the space they want
Top 2 Questions From Customers Answered – What Topics Should My Bot Know and How Much Training Data Do I Need?
The content and structure will largely determine the value for your virtual customer assistant. It stems from the use case that you give to your bot. Hence, before starting to build the bot it is important to really understand what you need the bot to do and how. Once, that is determined, then you need to figure out the content that goes into the bot. Some important points to keep in mind about the content for the bot that we get asked quite a lot. We advise to select around 50-60 (max 100) topics for your bot. These topics should