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The quick-start guide to building an AI knowledge base
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The quick-start guide to building an AI knowledge base

A headshot of Will Kelly
Will Kelly
24th May, 2024
6 min read
A mechanical hand places a document on top of a pile of books on a stylised background
A headshot of Will Kelly
Will Kelly
24th May, 2024
6 min read
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What is an AI knowledge base?
AI knowledge base: 3 benefits
Build an AI knowledge base

With AI continuing to develop rapidly, more companies are turning to it. Remote and hybrid teams need instant answers that only an AI knowledge base can offer.

As hybrid and remote work become more widespread, your organisation might struggle with knowledge management for employees and customers. The artificial intelligence (AI) knowledge base is one such solution gaining immense popularity. These intelligent systems store vast amounts of multimedia content, automate processes, and provide actionable insights.

Let's walk through the steps to create an AI knowledge base that will revolutionise your team's collaboration and information access.

What is an AI knowledge base?

An AI knowledge base is a digital repository empowered by AI technologies such as natural language processing (NLP) and machine learning (ML). It stores, organises, and analyses vast amounts of information, enabling quick and accurate knowledge retrieval.

This intelligent system can understand user queries, provide personalised recommendations, and continuously improve its functionality over time.

3 benefits of an AI knowledge base

Here are three benefits your teams can realise with an AI knowledge base:

  • Customer-facing employees such as sales development and call centre representatives have a single source of truth for information.

  • Remote and hybrid employees gain anytime/anywhere access to corporate information and documentation they can search via natural language while avoiding a cumbersome interface.

  • All employees can gain renewed confidence in their knowledge base as search and information retrieval improve much to their delight.

How to build an AI knowledge base in 8 steps

1. Define the objectives of your AI knowledge base

Clearly defining your AI knowledge base's objectives is crucial. Work with your stakeholders and internal customers to answer questions like:

  • What's the current state of knowledge management in your organisation?
  • What specific content management problems are we trying to solve?
  • What corporate content and documentation do we want to store and organise?

Understanding your platform goals will guide your AI knowledge base development process and ensure the solution aligns with your business needs.

2. Choose the right knowledge base platform

Selecting the right platform for your knowledge base is critical to its success. Numerous options are available, ranging from open-source solutions to enterprise-grade platforms. For example, Atlassian offers a Confluence knowledge base and AI features.

Consider the following when choosing a platform:

  • Platform scalability to accommodate high-traffic periods such as product launches.
  • Customisation options to modify and reskin the interface to follow your corporate brand guidelines.
  • Easy-to-use content publishing and management tools.
  • Integration with your existing tech stack, such as Google Workspace, Slack, and Microsoft Teams, and backend applications via API.
  • Ongoing technical support available omnichannel, including comprehensive online documentation.

Conducting an internal pilot or proof of concept that enables your users to test features using their current workflows and documents is essential as one of the final steps to deciding on a platform.

3. Collect and organise data

Gather relevant data to populate your AI knowledge base. Start by gathering internal documents, customer support tickets, FAQs, and other information your team regularly accesses. Organise this data into categories and establish a clear hierarchy to facilitate easy navigation and searchability.

4. Train AI and ML

Many SaaS vendors now integrate AI technologies such as Natural Language Processing (NLP) and Machine Learning (ML) algorithms into their knowledge base platforms. These technologies require training with your corporate data to better understand user queries, extract insights from unstructured data, and continuously improve its accuracy and relevance over time.

5. Develop user-friendly interfaces

Design intuitive user interfaces that make it easy for employees to interact with the AI knowledge base. To enhance usability, incorporate features like keyword search, filters, and personalised recommendations. Conduct user testing to gather feedback and make iterative improvements.

6. Ensure data security and compliance

Prioritise data security and compliance with regulations such as GDPR or HIPAA. Implement encryption protocols, access controls, and regular audits to safeguard sensitive information stored in your AI knowledge base. Stay updated with industry best practices and adapt your security measures accordingly.

7. Train and educate users

Provide comprehensive training resources, including tutorials, workshops, and documentation, to help users leverage the full potential of the AI knowledge base.

8. Monitor performance and iterate

Continuously monitor the performance of your AI knowledge base using analytics and user feedback - track metrics such as search accuracy, response times, and user engagement to identify areas for improvement. Iterate on the platform using these insights to ensure ongoing optimisation and relevance for your users.

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Written by
A headshot of Will Kelly
Will Kelly
Content Writer
Will Kelly is a freelance writer. After his earlier career as a technical writer, he’s passionate about easing collaboration pain points for teams, whether technology, process, or culture. He has written about collaboration for IT industry publications.

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