How to build a powerful Confluence AI knowledge base in 2025

Let’s be honest, Confluence is probably where your team’s best-laid plans and official documents go to live. It’s the home for project specs, company policies, and all that institutional knowledge you’re supposed to have on tap. But as your company scales, that well-organized wiki can quickly turn into a digital attic. Finding the one thing you need feels less like a search and more like an archaeological dig.

This is where AI can make a real difference. By adding a layer of intelligence, you can turn your static Confluence space into a resource that talks back. Instead of hunting through pages, your team can just ask a question and get a straight answer.

So, how do you pull this off? This guide will walk you through the ways to create a Confluence AI knowledge base. We’ll compare using Confluence’s own tools versus plugging in an external platform, so you can figure out what makes the most sense for your team.

What is a Confluence AI knowledge base?

Putting it simply, a Confluence AI knowledge base layers artificial intelligence over your existing Confluence content. This lets people, whether they’re employees or customers, ask questions in plain English and get answers instantly, just like they would from a coworker.

You really have two main paths you can take:

1. Use the native AI built into Confluence: This means you’re sticking with Atlassian’s own set of AI features to do the heavy lifting.

2. Integrate a third-party AI platform: This involves connecting an external tool that reads your Confluence data (and other sources) to give you more advanced AI capabilities.

The right choice really comes down to one question: where does your company’s real knowledge actually live? Let’s dig into each option.

The built-in option: Using Atlassian Intelligence (Rovo)

Atlassian Intelligence, which has recently been folded into a broader tool called Rovo, is Confluence’s native AI. It’s built to work smoothly right inside the Atlassian ecosystem, making it a pretty convenient choice for teams who are already all-in on Atlassian products. Its biggest advantage is that it’s already there, wired directly into your Confluence pages, blogs, and comments.

What Atlassian Intelligence can do

Atlassian has worked its AI into Confluence to help with a few common tasks:

• AI-powered Search: Instead of just looking for keywords, you can ask questions in natural language right in the search bar. The AI will then piece together an answer from the content scattered across your Confluence pages.

• Content Generation & Tweaking: It can help you get over that "blank page" feeling by drafting new content from a simple prompt. You can also use it to summarize long pages, change the tone of your writing, or do a quick spelling and grammar check.

• Page Summaries: When you’re looking at a huge project doc or a long comment thread, the AI can whip up a quick summary so you can get the main points without reading every last word.

• AI-powered Automations: You can set up automation rules just by describing what you want. For example, you could tell it to "archive inactive pages after 6 months and notify the page creators," and it will build the rule for you.

This video provides an introduction to how Atlassian Intelligence's OpenAI-powered features can enhance your Confluence AI knowledge base.

A major limitation: It only knows Confluence

But here’s the snag. Atlassian Intelligence is fantastic at understanding what’s inside Confluence, but its knowledge stops right at the Atlassian border. It has no idea what’s happening outside of your Confluence pages and connected Atlassian tools.

Think about where your company’s knowledge truly comes from. It’s not just in the official wiki. It’s in the detailed solutions buried in your Zendesk tickets, the process guides your marketing team keeps in Google Docs, and the quick fixes people share in Slack channels. Atlassian’s AI is blind to all of that.

This creates a huge knowledge silo. Your AI might be able to answer a question based on a policy document from last year, but it won’t know about the updated workaround your support team has been using for the last six months. This leads to answers that are incomplete, out of context, or sometimes just flat-out wrong.

This is why many teams start looking for a tool that can bring all their knowledge together. A platform like eesel AI, for instance, connects not just to Confluence but also to your helpdesk, chat apps, and other wikis to give your AI a complete view of your business.

The integration option: Connecting third-party AI tools

Looking beyond Confluence’s built-in AI isn’t about finding a replacement; it’s about fixing the silo problem. The idea is to create one unified source of truth that an AI can learn from, giving it access to every nook and cranny of your company’s collective brain.

An infographic showing how an external AI can unify knowledge from Confluence, Slack, Zendesk, and Google Docs for a complete Confluence AI knowledge base.:

Why connect an external AI?

There are some solid reasons to go this route:

• A single source of truth: The AI can find answers from Confluence, Zendesk, Slack, and Google Docs all at the same time. When someone asks a question, they get a full answer that reflects everything your company knows, not just one piece of it.

• Help for everyone: An internal AI assistant built this way can help anyone in the company. An HR manager can find a benefits policy from Confluence, an engineer can dig up a technical fix from an old Slack thread, and a support agent can find a resolution from a closed Zendesk ticket, all without switching apps.

• More advanced features: External tools are often designed for tougher jobs. They might offer more sophisticated capabilities like custom actions (e.g., looking up an order status in Shopify), detailed analytics on what people are asking, and fine-grained control over the AI’s personality.

The old challenge: Complexity and engineering headaches

In the past, this approach came with a big headache: it was incredibly technical.

If you look at guides for setting up AI with tools like AWS Bedrock or DIY solutions with vector databases, you’ll notice they’re written for developers. They involve setting up APIs, managing complicated infrastructure, and needing ongoing engineering time to keep it all from breaking.

This put most teams in a tough spot. Do you settle for the limited native AI, or do you dive into a costly and time-sucking engineering project?

Thankfully, this is where modern, self-serve platforms have completely changed the equation. eesel AI gives you all the power of a unified knowledge source without the technical mess. You can connect all your tools, including Confluence, in just a few minutes, no coding needed.

What to look for in a great Confluence AI knowledge base

Whether you go with a native tool or a third-party integration, there are a few things you should absolutely demand. Think of this as your checklist for building an AI assistant that your team will actually want to use.

Connect to more than just Confluence

The most helpful AI assistants are the ones that learn from everywhere your team works. A truly effective solution needs one-click integrations with the apps you already use day-to-day, such as:

• Help desks like Zendesk, Freshdesk, and Intercom.

• Chat tools like Slack and Microsoft Teams.

• Other wikis like Google Docs, Notion, and SharePoint.

Your company’s knowledge is spread out. Your AI’s brain should be, too.

A screenshot showing various one-click integrations available to build a comprehensive Confluence AI knowledge base.:

Set up and test it yourself

The best tools are the ones that can be managed by the people who use them, like support or IT managers, not just engineers. You shouldn’t need to call a developer every time you want to connect a new knowledge source or tweak the AI’s instructions.

A simulation mode is also a must-have. This lets you test the AI on thousands of your past support tickets or questions in a safe environment before it ever talks to a real person. You can see exactly how it would have answered, measure its potential impact, and get comfortable with it before going live.

The simulation mode allows you to test your Confluence AI knowledge base on historical data before going live.:

Unlike platforms that make you book a demo just to see the product, eesel AI is built so you can get started completely on your own. Its simulation feature lets you see how it will perform on your actual historical data before you commit to anything.

Maintain complete control

A one-size-fits-all AI rarely works. Every business has its own quirks, and your AI should reflect that. Your team needs to be able to:

• Customize the AI’s persona and tone of voice to match your brand.

• Define specific actions it can take, like escalating a ticket, looking up an order status in Shopify, or tagging an issue for follow-up.

• Selectively automate by setting rules that decide exactly which questions the AI handles and which ones should always go to a human.

A screenshot showing the customization options for an AI's behavior and actions, which is a key feature of a flexible Confluence AI knowledge base.:

Understanding Confluence AI knowledge base pricing

It’s good to know that Atlassian’s built-in AI features don’t come with every plan. Atlassian Intelligence is only included on Confluence Cloud Premium and Enterprise plans.

If your team is on the Free or Standard plan, you’ll have to upgrade your entire Confluence subscription just to get the native AI tools. Here’s a quick look at the pricing, based on Atlassian’s official page:

Plan	Pricing (per user/month, annual)	Key AI Feature Access
Free	$0 (up to 10 users)	None
Standard	$5.16	None
Premium	$9.73	Atlassian Intelligence features included
Enterprise	Contact Sales	All Premium features plus advanced controls

This per-user pricing can get expensive fast, especially if only one team needs the AI features. It’s worth comparing this with other pricing models. For example, platforms like eesel AI offer plans based on usage (how many AI conversations you have per month) instead of forcing a platform-wide, per-seat upgrade. This can be a much more predictable and budget-friendly way to get started.

Choosing the right approach for your Confluence AI knowledge base

So, what’s the final verdict?

Native Confluence AI is a decent starting point if your team’s entire universe is inside the Atlassian ecosystem. But for most companies, it creates knowledge silos that lead to half-answers. On the flip side, traditional external AI integrations have been powerful but way too technical and time-consuming for anyone but dedicated engineering teams.

This is where a new generation of tools is changing the game. eesel AI was built to fill this gap, giving you the power of a unified knowledge source with the simplicity of a self-serve, no-code platform.

You don’t have to choose between a limited tool and a massive project. You can connect all of your company’s knowledge, from Confluence and beyond, to an AI assistant that’s ready to go in minutes.

Ready to see what your Confluence knowledge base is really capable of? Try eesel AI for free and build an AI assistant that knows your whole business, not just one part of it.