How much does it cost to implement an AI bot
AI bots can recognize the meaning of text and voice messages sent by your customers and issue an appropriate response in text or in speech. These bots have been used for several years by customer support services at Alfa Bank, Tinkoff, Sberbank, and Yandex.Taxi.
It's not only large companies that can afford to implement a smart bot today. Here's how to keep your budget at bay when implementing a smart bot.
- What can smart bots do
- How AI bots function
- How much does it cost to implement an AI bot
- Why Flomni is cost-efficient
What can smart bots do
An AI-powered chatbot can recognize a chat message or customer's speech during a hotline call. The customer can ask a free-form question and receive an instant response from the bot. The message will be worded just like one sent by a human operator.
In most cases, human operators don't have to join the conversation as smart bots are mostly capable of solving customer issues on their own. To be confident in the bot performance quality, you can configure the percentage of text recognition, which is usually at 80%. If this value is less than 80%, the bot will automatically hand off the conversation to a human operator.
Here are a few indicators of smart bot efficiency based on our customers' experience after implementation:
- At CDEK, a smart bot increases service automation by 30–40% compared to conventional button bots. The company automatically processes about 86% of all text-based inquiries sent in by customers.
- At Ural Airlines, the smart bot boosts button bot performance by 28% thanks to the random text recognition module. About 63% of all customer requests are processed automatically.
How AI bots function
As a rule, smart bots operate on the basis of conventional button bots as an add-on. AI solutions allow automating request processing and providing better response.
AI bots use machine learning, neural networks, and natural language processing technologies. A bot needs to be trained first to be able to independently process customer requests. For this purpose, so-called marked-up data corpora are used.
Let's say, for example, that we need to teach the bot the meaning of the question "Where is my order?" To do this, humans mark up all important items that contain the meaning: "where" means a question about location, "order" means a paid order that must have a track number; grammar and word connections should be marked up too. Since you can use different ways to phrase this question ("Track the order", "Where is my package", etc.), you need to offer the bot different wordings to train on. Marked-up data is then loaded to train the bot.
If the system encounters a similar wording in the future, it will match the percentage of similarity with wordings it already knows. When the desired percentage of similarity is reached, the system will classify the question as relating to a known query.
Companies usually already have an extensive database of customer correspondence. You can use it to train your smart bot. If you use an off-the-shelf platform with a smart bot, it has a request database as well. For example, when we implement bots for our customers, we use our own database and enhance it with data collected by the customer company. The advantage of this approach is that a company doesn't have to have a large customer correspondence history to implement an AI bot.
Modern AI bots do not look at an issue in isolation, but use additional information called context. The context may be data about the customer, which the bot retrieves from the corporate CRM system to learn more about the customer status and history of their interaction with the company. This makes bots more efficient.
AI bots can:
- categorize texts — determine the topic of a particular message;
- retrieve facts and items from text — like name, number, product name, etc.;
- generate meaningful answers;
- determine the mood of the text — this feature is popular today for automatic review analysis.
Active smart bot development began after 2016, when Google and Microsoft presented their developments in this area, promptly followed by other major companies. Today, AI-based bots can learn and develop on their own by analyzing own experience and errors. Because of this, they are getting smarter and working more accurately.
How much does it cost to implement an AI bot
The cost and timing depend on the implementation option. Let's review the three basic options.
1. Create an AI chatbot on your own
The company creates a smart chatbot from scratch, trains it, provides technical support, resources, and infrastructure. Large mobile network operators or leading IT companies can afford it. Initial investment starts at RUB 10 million. To implement the project, you need a team of specialists, including developers, linguists, analysts, and so on.
2. Connect a conversational AI engine to the company's services
When a company already has button chatbots with an operator chat panel connected and CRM system integration, a new set of functions — an AI chatbot — can be added through integration with a cloud system. It will cost RUB 2 million or more, with most funds being spent on integrations with the company's existing systems, while the cost of the conversational AI will be about 10% of this amount.
3. Choose a platform with all necessary features to automate customer service
Flomni is a good example. Along with regular button chatbots, it has the option to connect voice bots and AI bots. The launch will only take a few weeks, and the costs start from RUB 30K. We are ready to do all technical adjustments and refinements.
Why Flomni is cost-efficient
- We already have the basis for AI chatbots — our own conversational AI system adapted to helpdesk goals. It was borne of five years of dedicated work. All that remains is to train your neural network on queries specific to your company. This means less work, lower implementation costs, and high-quality product.
- The system works in the cloud. You can save on IT infrastructure because all data is stored on our secure servers. It takes less time to deploy the system. Data transfer and data storage conditions comply with Russian laws; however, organizations with the highest information security requirements can install the solution into their corporate IT system.
If you're not sure you need an AI chatbot, Flomni services can be deployed gradually: start automating with simple button bots, assess the effect, and then decide if you should add AI to your system. Many of our customers choose this option. You can use a free consultation to discuss whether this would be an optimal choice for your company.
There is no way of knowing if your message has been read. Communicating in messenger apps is quite the opposite, if organized right.
In this article, we'll explain how you can implement a chatbot to maximize the benefits for your business and make your customers happy.
From online stores to medical clinics to delivery services — let's look at some case studies of how to automate customer service.
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