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What is an AI Knowledge Base?

Learn about AI knowledge base, it's benefits, challenges, use cases and the best AI knowledge base software in 2024.

Published on

September 27, 2024

Updated on

July 25, 2024

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What is a knowledge base?

A knowledge base is a database that contains a collection of information, including concepts, entities, relationships, and rules, which are used to reason, infer, and make decisions. In the context of AI, a knowledge base is designed to support various AI applications, such as natural language processing (NLP), expert systems, and decision support systems.

The concept of a knowledge base dates back to the 1960s, when the first expert systems were developed. These early systems were designed to mimic human expertise in specific domains, such as medicine and engineering. Over time, the concept of a knowledge base evolved to support more advanced AI applications, including machine learning and deep learning.

What is an AI knowledge base?

An AI knowledge base is a centralized repository that stores and organizes vast amounts of information, data, and knowledge to support artificial intelligence (AI) systems, machine learning models, and other intelligent applications. It serves as a single source of truth, providing a unified view of an organization's knowledge, expertise, and experiences.

An AI knowledge base is essential for businesses for various reasons:

Improved decision-making: AI knowledge base provides a single source of truth, enabling AI systems to make informed decisions based on accurate and up-to-date information.
Enhanced AI capabilities: AI knowledge base supports the development of more advanced AI applications, such as chatbots, virtual assistants, and expert systems.
Increased efficiency: AI knowledge base reduces the time and effort required to develop and train AI models, as it provides a centralized repository of knowledge and expertise.

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The components of an AI knowledge base

AI knowledge base also has similar components when compared to traditional knowledge base. It just uses generative AI to search, index and query your knowledge requirements. Here’s a short rundown of the components of AI knowledge base:

Knowledge Acquisition: This involves collecting and processing data from various sources, such as text, images, databases, and human experts. The data is then transformed into a structured format, making it machine-readable.

Knowledge Representation: This component deals with organizing and storing the acquired knowledge in a way that facilitates efficient retrieval and reasoning. Common knowledge representation schemes include semantic networks, frames, and ontologies.

Inference Engine: This is the brain of the knowledge base, responsible for drawing conclusions, making deductions, and solving problems based on the stored knowledge.

Query Interface: This allows users to interact with the knowledge base, posing questions or requests that trigger the inference engine to generate responses.

Image Source

AI knowledge base employs machine learning, natural language processing and graph based networks to perform all the activities.

The benefits of an AI Knowledge Base

Now, an AI knowledge base is more than just a glorified knowledge repository. Its benefits ripple different teams because of easy availability of information. Let’s look at some of these benefits.

Increased Productivity

According to Mckinsey, employees spend over 2 hours daily to search for the right information. With an enterprise AI search, employees can find information they need in seconds.

AI knowledge base provides a centralized repository of information. Employees can find any information they need with just a click of a button. This reduces time and effort to find information. With a knowledge base, employees can spend more time working than searching for information. It also enables teams to collaborate more effectively, sharing knowledge and expertise across different domains and applications.

Enhanced Decision-Making

AI knowledge base platform provides a single source of truth, enabling employees to make informed decisions based on accurate and up-to-date information.

Alltius not only provides information but also cites the sources, thus increasing the reliability of the information. This helps in enhanced decision making without validating information which might take more time.

Cost Savings

A knowledge base tool helps companies organize their documents instantly. A platform reduces the time and effort required to organize and maintain the knowledge. Also, with heavy utilization of existing information, companies can actually reduce time spent in searching information, thus saving costs in terms of employee hours.

Improved Customer Experience

AI knowledge base enables customer support teams to focus more time talking to the customer than searching for information. Such AI enabled knowledge retrieval systems help customer support teams to reduce wait time, increase first touch resolution and improve customer experience.

Alltius helps companies convert their knowledge bases into a searchable database. It can be used by sales, support or any other teams to quickly get relevant answers to their queries. Sales team can use Alltius’ AI sales assistant to handle sales objections related to product without involving product team within seconds. Support teams can use Alltius to resolve customer queries by identifying the correct resolution to customer problems within seconds.

Read how Alltius helped:

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How to Create an AI Knowledge Base?

So, what should you do if you want to create your own AI knowledge base? Let’s look at 5 steps to create an AI knowledge base at your organization.

Step 1: Define the Scope and Objectives

Determine the specific domain or area of expertise for the knowledge base. Establish the goals and objectives of the knowledge base, such as improving decision-making or enhancing customer experience.

Step 2: Collect and Organize Data

AI is as good as the data. In order to improve the quality of your AI knowledge base platform, collect relevant data sources, including documents, articles, and expert opinions. And add all the relevant data to the knowledge base software.

Step 3: Select a knowledge base software

Select a suitable knowledge base software that works with your knowledge base, works with your file types and integrates with your existing techstack.

Step 4: Populate the Knowledge Base

Populate the knowledge base with the collected and organized data.Ensure the data is accurate, complete, and up-to-date.

Step 5: Test

Test and refine the knowledge base to ensure it meets the objectives and requirements.

Here are some of our expert tips to get started:

Use standardized formats: Use standardized formats to ensure interoperability and scalability.
Ensure data quality: Ensure the data is accurate, complete, and up-to-date to maintain the integrity of the knowledge base.
Continuously update and refine: Continuously update and refine the knowledge base to ensure it remains relevant and effective.

Challenges of Using an AI Knowledge Base

While an AI knowledge base offers numerous benefits, it also presents several challenges. Let’s take a look at some of them.

One of the primary challenges in knowledge base development is ensuring data quality. Inaccurate or incomplete data can lead to flawed AI decision making, compromising the reliability of the system. Additionally, data inconsistencies can further introduce errors in AI knowledge base outputs.

Integrating the knowledge base with AI applications can present its own set of challenges. Different applications may be developed using various technologies or frameworks, making seamless integration a complex endeavor. API and interface issues can further complicate the integration process, leading to compatibility problems and potential disruptions.

Ensuring the security and integrity of the knowledge base is paramount, especially if it contains sensitive or confidential information. Implementing robust access control mechanisms is essential to prevent unauthorized access and potential data breaches. Striking the right balance between accessibility and security can be a delicate endeavor.

Developing and maintaining a knowledge base requires a significant investment in expertise and resources. Domain experts with specialized knowledge are crucial for curating and validating the information within the knowledge base. Additionally, the process itself can be resource-intensive, demanding substantial time and effort from dedicated teams.

Addressing these challenges head-on is crucial for organizations seeking to leverage the full potential of your AI knowledge base.

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AI Knowledge Base Tools and Resources

Here are a few examples of top AI knowledge base platforms out there in 2024. These tools are best to get started with and have many features to convert your knowledge into a searchable database.

Alltius

Alltius is AI knowledge base software that helps you search important information from all your company documents with an easy to use chatbot interface. Alltius is based on decades of research at Carnegie Mellon and Wharton. It is developed to reduce time to information for customers and employees and is used extensively by sales and support teams.

Alltius leverages Generative AI to first understand the user input, then search through all the company documents to find the relevant information to answer the user’s query. It can answer from multiple documents to find one answer within 10 seconds.

Alltius can handle documents in multiple formats like websites, text files, videos, PDFs, docx, excel, spreadsheets, Google drive, slides, and more. With integrations, it can extract information from CRMs, slideshare, emails, Zendesk, Intercom, StackOverflow and more! With a complete knowledge repository, your customer support team can use it to generate answers for customer queries in seconds, or your sales team can use it to get answers about what your product can do with utmost reliability.

The best part, Alltius’ AI assistants can be created and deployed within 15 minutes with minimal customization across your website, product, social media channels and more.

Top features:

AI knowledge discovery and recommendations
Intelligent search and natural language processing
Automatic FAQ creation & knowledge updation
0%* hallucinations
Advanced Analytics and reporting
Ability to draw knowledge from complex charts and infographics.

Pricing: Pricing varies from case to case. Offers a 14-day completely free trial to use.

Learn more about Alltius:

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Bloomfire

Bloomfire is a leading AI knowledge management platform that helps organizations centralize, share, and utilize their collective knowledge and expertise. Designed to facilitate seamless collaboration and knowledge sharing, Bloomfire empowers teams to capture and disseminate valuable insights, best practices, and institutional knowledge across the organization.

Top Features:

AI search and content recommendations
Secure knowledge sharing and collaboration
Advanced analytics and reporting
Seamless integration with various applications

Pricing: Bloomfire offers customized pricing based on the number of users and required features. Their plans typically range from $25 to $50 per user per month.

Cons:

The user interface can be overwhelming for some users.
Limited customization options for branding and design.

Zendesk

Zendesk is a popular customer service and engagement platform that includes a robust AI knowledge base component.

Top Features:

Natural language processing (NLP) and machine learning for content suggestions
Omnichannel knowledge base deployment (web, mobile, chatbots)
Advanced analytics and reporting
Seamless integration with Zendesk's customer service suite

Pricing: Zendesk offers a range of plans, with the Knowledge Base feature included in the Guide Professional plan, which starts at $49 per agent per month.

Cons:

The knowledge base functionality may be limited for organizations with complex knowledge management needs.
Integration with non-Zendesk applications can be challenging.

Coveo

Coveo is an AI intelligent search and knowledge management platform designed for enterprise-level organizations.

Top Features:

Advanced AI search and relevance
Unified content indexing and management
Personalized and contextualized content delivery
Machine learning models for content recommendations

Pricing: Coveo's pricing is customized based on the organization's specific requirements and the number of users. Their plans can range from tens of thousands to millions of dollars per year.

Cons:

Coveo's solutions can be expensive, especially for smaller organizations.
The implementation and configuration process can be complex and time-consuming.

Guru

Guru is a knowledge management solution that leverages AI to help teams quickly find and share information.

Top Features:

AI knowledge discovery and recommendations
Seamless integration with various productivity tools (Slack, Microsoft Teams, etc.)
Advanced analytics and reporting
Collaborative knowledge creation and curation

Pricing: Guru offers a range of plans, with the Starter plan starting at $25 per user per month, and the Business plan at $85 per user per month.

Cons:

Limited customization options for knowledge base design and branding.
Some advanced features may require additional integration or customization.

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Are you looking to create a knowledge base?

We'll show you how to build a AI knowledge base assistant (public or private) in minutes with your own documents.

Book a demo

Try it yourself

‍

Enhance Customer Experience with AI knowledge base

Delivering exceptional customer experiences has become a crucial differentiator. An AI knowledge base can be a powerful tool to help organizations achieve this goal by providing customers with accurate, timely, and personalized information.

By leveraging advanced technologies such as natural language processing (NLP), machine learning, and intelligent search, an AI knowledge base can efficiently retrieve relevant information from vast repositories of data, ensuring customers & employees receive the answers they need promptly. This not only improves customer satisfaction but also reduces the workload on support teams, allowing them to focus on more complex issues and further enhance the overall customer experience.

As organizations strive to differentiate themselves in an increasingly competitive market, investing in an AI knowledge base can be a strategic move to enhance customer experience, drive customer satisfaction, and ultimately boost brand loyalty and growth.

Frequently Asked Questions (FAQs)

How do I ensure the accuracy and completeness of my AI knowledge base?

To ensure the accuracy and completeness of your AI knowledge base, it's essential to use high-quality data sources, implement data validation and verification processes, and continuously update and refine the knowledge base.

Can I use an AI knowledge base for multiple AI applications?

Yes, an AI knowledge base can be used for multiple AI applications, including machine learning models, expert systems, decision support systems, and natural language processing applications.

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