How to Grow Your Business with an AI-Powered Chatbot
Andrew Mazur
Senior Business Development Manager @ DataArt | Driving Technology Transformation
Deciding whether to invest in an AI chatbot can be tough, especially with worries about effectiveness and security. In this article, our experts reveal key insights on addressing these concerns and leveraging AI chatbots for significant operational and customer service improvements.
With AI technologies booming, chatbots are on the rise. Successful examples, such as Progressive Insurance’s chatbot in Messenger, which has saved the company millions of dollars, are certainly impressive. While many businesses are considering using chatbots to cut costs and increase market share, executives also have concerns about the quality of the information a chatbot gives to clients and data security.
In this article, Konstantin Grusha, our Delivery Manager, and Nikita Kozlov, VP of Cloud Technology, address some of the most common concerns about chatbots and explain how companies can leverage them to grow their business.
Building an AI-Powered Chatbot
A chatbot is an application that generates answers to users' questions in the form of a human-style conversation. To build a successful AI-powered chatbot, you will need:
Different companies offer platforms for AI application development that are suitable for building chatbots. The most famous examples are Amazon Bedrock (provides a choice of foundation models such as AI21 Labs, Anthropic, Cohere, Meta, Mistral AI), Vertex AI by Google (allows access to Google-trained models, like Gemini and Gemma, as well as open and closed-source models like LLaMA from Meta and Claude from Antropic), and Azure AI Studio that hosts AI models from Meta, Hugging Face, and Databricks, in addition to several Azure OpenAI language models. All in all, there are thousands of large language models available. While many of them are free or reasonably priced, keep in mind the computational resources, including processing power and memory, required to run the models are not free.
Several things must be considered when working with LLMs. First, LLMs are not truly intelligent, as they are not thinking yet, and depend entirely on the context we provide. So, you need to give your chat the most relevant and updated information.
Another point worth mentioning is operational memory limitations. The interaction capability of a standard model is limited to roughly 4,000-8,000 tokens, which in data terms would be something like 15-30 kilobytes of an English text (the amount differs for different languages with higher or lower information density). Some of the more expensive and recent models have a larger memory. For example, GPT-4-32K allows you to put in 150 kilobytes of data in one conversation (the equivalent of a large book); and with Claude 3, this number may go up to 200 kilobytes. So, if you are chatting with the model for a long time, at a certain point, it will start to forget what happened at the beginning of the conversation. There are several ways around it, and vector databases are one of them.
Using Vector Databases
Vector databases do not hold text as is—they convert it into abstract concepts and search for relevant information as if they were a local search engine. Any concept in vector databases is a vector in a multidimensional context space. There is an infinite number of contexts, and the more vectors you have, the better you sort out the sector where you need to look for an answer.
These vector databases act as a long-term memory for LLMs, and the process of converting your documentation to be stored in a vector database is called embedding.
Data and Privacy Concerns
When creating a chatbot, figuring out what information you can feed the bot is crucial. Data should be reliable, relevant, and securely managed to avoid exposing sensitive business data. Information at private or public websites, Jira, and various databases – all of it can be used as sources for your vector database.
Most embedding models for creating the vector database usually only focus on text, they cannot use both images and text to create a vector. So for optimal results, prepare your multimedia content (pictures, tables, graphs, and videos) using different models.
One more relevant thing to consider is privacy. While you can keep your search engine locally on your machine and embed your local documents, to generate meaningful answers in the chatbot, you need to send data to an LLM API. So, you should always consider your data’s security level and make sure the model provider you are using aligns with your data privacy policies.
Talking Your Language
The way a chatbot interacts with users is crucial for its success. A Harvard Business Review study found that a more human-like conversational style of the bot significantly enhances consumer satisfaction and strengthens a brand’s reputation.
So, when setting up the language a chatbot speaks, remember to tailor its “personality” to reflect your company’s brand and values. A polite, human-like approach will help you establish a strong connection with clients.
Retrieval-Augmented Generation and Fine-Tuning
Retrieval-augmented generation (RAG) is an important part of an AI application development that ensures the information the chatbot generates is meaningful and specific to the company’s data. The RAG approach optimizes an LLM output by referencing the content with external knowledge bases, allowing you to create solutions that add specific data information and generate useful answers for the business.
Fine-tuning is also beneficial to further training the model on a targeted data set. It helps improve the model's understanding of your specific domain knowledge and enhances the AI solution accuracy.
Whenever we ask the model something, we give it a new iteration of the prompt. With every new question, the system’s message evolves, and we provide new relevant pieces of context. That is the theory, but in reality, users are unpredictable. They can ask unexpected questions or act provocatively. Sometimes, they can try to trick the model into revealing secret information. That is why monitoring how the model responds to user questions allows you to fine-tune the system prompts (information provided to the model when asking questions), adding safeguards against what users might ask and grooming data.?
DataArt Chatbots Success Stories
At DataArt, we have built three in-house chatbots: a marketing chatbot for our website, a helpdesk bot for employees, and a knowledge base bot.
What We Offer
At DataArt, we have a chatbot creation framework that works on any platform with a large language model of your choice that leverages your company’s data and an interface that meets your needs. Whether you require an external chatbot for your website or customer support or an internal application to help employees, DataArt’s dedicated professionals have the expertise you need.
Ensuring a strong privacy level is crucial when creating external chatbots: you don’t want people to have access to your business information. That is why we implement comprehensive security guidelines for creating chatbots. For additional protection, we can build a chatbot based on an open-source model that is deployed in a secure cloud or on-premise environment with controlled data privacy. When building internal bots, we establish user-specific data permissions for added security.
Our team can develop a demo version of a chatbot in just 2 weeks and a fully functioning AI-powered chatbot within 6-8 weeks.
Conclusion
If you know how to use them, AI-powered chatbots can streamline your business operations, increase your customers’ satisfaction, and drive new leads. DataArt can help you create a useful and secure chatbot that strengthens your brand’s reputation within weeks. Let’s harness the potential of AI-driven chatbots for your business!
Originally published here .