What You Need to Know About How AI Knowledge Bases Work

January 29, 2025

AI knowledge bases are quickly revolutionizing the customer service space with their ability to provide fast answers to customer queries and update real-time in response to data modifications. Though their function is well-known by now, how they work is less commonplace. In today's article, we'll be demystifying the mechanics and the logic behind the types of content that go into the creation of AI knowledge bases.

What are AI knowledge bases?

AI knowledge bases are consolidated repositories where client-loaded data is stored and used alongside artificial intelligence (AI) technologies. The usage of AI for processing of this data allows the base understanding, to a degree, of the information within it. This allows an AI powered base to be more dynamic than your traditional knowledge base as its responses to customer queries are faster given that they trim down on the often-tedious search process and frequently drawing from several pools of information all at once. In addition, usage of an AI knowledge base can lower the burden on customer service agents.

The management of data within the AI base is also highly viable, allowing clients to add, delete, and organize all answer-accessible data.

What kind of content do they store?

Various forms of content are typically collected and supported within an AI knowledge base. This data is often classified into one of three categories- structured, unstructured, and automated.

• Structured Content – refers to organized, formatted data that follows a consistent structure. Some examples of this type of content include FAQs, how-to guides, tables, and articles.
• Unstructured Content – refers to disordered content that lacks specific formatting and does not follow a consistent structure. Some examples of this type of content include multimedia (videos, images, audio recordings, etc.) and email or chat transcripts.
• Automated Content – refers to AI generated information suggested in direct response to information and insights collected by users, user activity, or data analysis. This data aims to improve your knowledge base.
  
  The technologies AI knowledge bases use allow them to freely interact with all these different forms of data.

What technologies do AI Knowledge bases use?

The main technologies AI knowledge bases use that makes them far more enhanced than traditional bases include machine learning (ML) algorithms, natural language processing (NLP), and a centralized knowledge repository. These different technologies allow for the storage, processing, and AI understanding of data.

• Machine Learning (ML) Algorithms – processes that enable AI to learn from data, make predictions without the need for explicit human intervention, and identify patterns.
• Natural Language Processing (NLP) – a subfield of AI that focuses on enabling machines to understand, interpret, and generate understandable human language.
• Knowledge Repository – a centralized storage system where all client-loaded data is stored and managed. Working in tandem, these components of an AI knowledge base enable storage, interpretation, and analysis of data, all the tools most necessary towards generation of AI responses to user queries.

Why use an AI Knowledge Base?

Putting to use an AI knowledge base is not only a great way to utilize one of the most exciting and modern advancements in customer service but it also offers various benefits, some of which have already been touched on throughout this article. An abridged version of some of these benefits (see a longer version of this here) will be covered below:

• Customer Self-Service Through use of an AI knowledge base, customers are closer than ever to self-service, and 24/7 answers that don't require human interaction. AI-empowered knowledge bases not only grant customers a fair amount of autonomy in the search process but also speeds it up while making it more efficient.
• Employee Search & Onboarding Customers are not the only ones who benefit from this easy self-service process. Employees seeking answers to customer questions can find their answers while avoiding holding-up clients during the search. In addition, this centralized, fast knowledge search process ensures the on-boarding for new employees is made that much more efficient.
• AI Generated Data The data collected through customer interactions and feedback can be put to great use by your company for the improvement of current or even creation of new data. This ensures your knowledge base content will always stay up to date.

Reduced Strain on Support Teams

Traditional search is incapable of adapting and understanding customer queries beyond a simple keyword match. This often makes it a tool considered frustrating to use as opposed to directly calling the customer support team. An AI-powered searches understanding of user intent, however, makes it capable of deflecting a significant number of routine support queries. This reduces the need for the support team to address routine queries, allowing them to focus on more specialized tasks.

Next Steps

Ready to see how the use of an AI knowledge could benefit your company, or even want to see hypothetical application of one? Check out some starting information on Adrentech's AI knowledge base norm here and schedule a demo here for access to our no-obligation pilot offer!