Atlassian Intelligence search across linked knowledge bases

Let's be honest, your company’s knowledge isn’t stored in one neat little box. It’s scattered everywhere, across documents, chat threads, and old support tickets. This makes finding a straight answer a headache for both your team and your customers.

Atlassian is trying to solve this with Atlassian Intelligence, its native AI built to connect Jira Service Management (JSM) and your Confluence knowledge bases. The promise is simple: get the right information to the right person, right when they need it.

In this guide, we'll walk through how it actually works, what you need to get it running smoothly, and some of the key limitations you should be aware of. Because while a built-in AI sounds convenient, it isn’t always the best fit, and sometimes a more flexible approach is what you really need.

What is Atlassian Intelligence search across linked knowledge bases?

Atlassian Intelligence Search Across Linked Knowledge Bases is a set of AI features designed to connect your Atlassian tools. It uses generative AI to understand questions typed in plain English, hunt through your Confluence spaces for answers, and provide a quick summary directly inside JSM and other channels.

So, instead of your agents or customers manually digging through articles, the AI does the heavy lifting. It can take a question like, "how do I set up the VPN on my Mac?" and return a concise answer, complete with links to the original articles it used.

This is the tech that powers features like the JSM virtual agent's "AI Answers," aiming to boost self-service, cut down on repetitive tickets, and help your agents find what they need faster. But remember, its success is completely dependent on having a well-organized and up-to-date knowledge base in Confluence.

Core features of Atlassian Intelligence

Atlassian has woven its AI into a few key areas to make finding and using information a bit easier. Here’s a look at what you can expect.

The virtual agent and AI answers

The main way people will interact with this AI is through the Jira Service Management virtual agent. This is where "AI Answers" shines. Instead of being stuck with rigid, pre-programmed conversation flows, the virtual agent uses AI to handle questions as they come up.

When a user asks something, the agent searches the linked Confluence knowledge base, pulls together the relevant info, and gives a direct, summarized response. This is meant to deflect common questions right in the customer portal or within chat tools like Slack and Microsoft Teams, freeing up your support team to tackle more complex problems.

Natural language search for issues

This next one is a real time-saver for agents. Anyone who’s tried to write a complex Jira Query Language (JQL) query knows how frustrating it can be. Atlassian Intelligence lets agents use natural language instead.

For instance, an agent can just type "find all urgent tickets assigned to me about network outages" instead of wrestling with a complicated JQL string. The AI translates that request into a working query, making it much quicker to find old tickets and related context without needing to be a JQL whiz.

Content summarization and generation

Beyond just searching, the AI can also help agents catch up on existing issues. It can take long ticket descriptions and endless comment threads and boil them down to a few key bullet points. It can also help create new knowledge base articles by drafting content from a simple prompt, making it easier to turn a one-off fix into a helpful document for the future.

How to get the most out of Atlassian Intelligence

You can't just flip a switch and expect an AI to work perfectly. Its performance is directly tied to the quality of the data it can access. Here’s what you need to have in place for Atlassian’s AI to really pull its weight.

A well-structured Confluence knowledge base: The foundation for Atlassian Intelligence

There’s just no getting around it: the AI is only as smart as the information you feed it. If your Confluence articles are outdated, contradictory, or just a mess, you're going to get garbage responses. An AI-ready knowledge base is an absolute must.

Here are a few practices to keep in mind:

• Keep it fresh. Make it a habit to review your articles regularly. Get rid of old information and make sure instructions aren't conflicting.

• Ditch the duplicates. Having multiple articles on the same topic is a recipe for confusion. The AI might pull from an older, incorrect version. Always stick to a single source of truth for each topic.

• Speak your customer's language. When you write articles, use the same words and phrases your customers do. They’re more likely to search for "my laptop won't connect to wifi" than "troubleshoot wireless connectivity issues."

• Use clear headings. The AI relies on the structure of your articles, including headings, to make sense of the content. Good organization leads to better answers.

Configuration and channel limitations

Before you can even start, an administrator has to activate Atlassian Intelligence for your entire organization. It's also worth knowing that features can roll out unevenly across different platforms. For example, the virtual agent was available in Slack long before it was fully functional in Microsoft Teams. If your team lives in a tool that isn't a top priority, you could be left waiting.

Key limitations of Atlassian Intelligence

While Atlassian Intelligence is a solid step for teams living exclusively within the Atlassian ecosystem, its native-only approach comes with some pretty big blind spots.

The knowledge silo problem

The biggest hurdle is that Atlassian Intelligence is built to search Atlassian products. It’s great at navigating Confluence, but what about all the other places your team keeps critical information?

An infographic illustrating how a unified AI platform breaks down knowledge silos by connecting to various apps, a key challenge for Atlassian Intelligence search across linked knowledge bases.

What happens when your most detailed product specs are in Notion? Or when company policies are managed in Google Docs? And what about that goldmine of solutions buried in past tickets from other help desks like Zendesk or Intercom?

An AI that can only see part of the picture can't give a complete answer. This sends your agents right back to square one: manually searching across half a dozen different systems to piece together a solution.

Limited customization and workflow control

Atlassian offers some automation, but it’s largely stuck within its own suite of products. A modern support workflow often needs to do more. You might need your AI to look up an order status in Shopify, check an account detail in a custom CRM, or trigger a webhook to another service.

A workflow diagram showcasing a comprehensive support automation process, which contrasts with the limited workflow control in Atlassian Intelligence search across linked knowledge bases.

This is where a dedicated AI platform like eesel AI really shines. It provides a full workflow engine with a powerful prompt editor and support for custom API actions. This gives you fine-grained control over the AI's tone, personality, and the specific actions it can take, both inside and outside the Atlassian world.

Lack of robust, risk-free testing

With Jira Service Management, you can test your virtual agent in a dedicated channel, which helps you get a feel for its responses. However, it doesn't give you a way to simulate its performance at scale or get data-driven predictions before you launch it for your customers.

A screenshot of eesel AI's simulation mode, highlighting a feature for risk-free testing that is not available in Atlassian Intelligence search across linked knowledge bases.

This is another spot where a specialized tool has a clear edge. For instance, eesel AI includes a powerful simulation mode that lets you test your entire AI setup against thousands of your past tickets. You can see exactly how the AI would have responded, get accurate forecasts on resolution rates, and find knowledge gaps, all before a single customer interacts with it.

Feature	Atlassian Intelligence	eesel AI
Knowledge Sources	Primarily Confluence & Jira	Confluence, Google Docs, Notion, Zendesk, Slack & 100+ more
Customization	Basic automation rules within Atlassian ecosystem	Fully customizable prompt editor & workflow engine with custom API actions
Setup & Onboarding	Requires admin activation and curated KB	Radically self-serve, go live in minutes
Pre-launch Testing	Live testing in a dedicated channel	Powerful simulation mode on historical tickets
Pricing Model	Bundled with high-tier Atlassian plans	Transparent plans, no per-resolution fees

Atlassian Intelligence pricing

You can't buy Atlassian Intelligence as a standalone product. It’s bundled into the Premium and Enterprise plans for Jira Service Management, Jira Software, and Confluence.

So, if your team is on a Free or Standard plan, you'll have to upgrade your entire Atlassian subscription to unlock these AI features. That can be a significant price jump, especially for larger teams. This pricing model essentially locks you into a more expensive tier across the board, even if you only need the AI search.

A better approach: Unifying all your knowledge with eesel AI

This is where a tool like eesel AI offers a smarter path. Instead of being just another tool in your stack, it acts as an intelligent layer that sits on top of all your existing platforms, breaking down the knowledge silos that hold your team back.

A screenshot showing how eesel AI connects to multiple knowledge sources, a superior alternative to the siloed Atlassian Intelligence search across linked knowledge bases.

eesel AI connects to Confluence and Jira just like the native solution, but it doesn't stop there. It also integrates seamlessly with Google Docs, Notion, Zendesk, Slack, and over 100 other sources your team uses every single day. This creates a single, unified brain that actually has the full picture of your business.

Here’s what makes this approach different:

• It’s truly self-serve. You can get up and running in minutes without having to sit through a mandatory sales call or demo.

• No "rip and replace" required. eesel AI plugs directly into your current help desk and workflows, so you don't have to change how you work.

• You're in total control. Use the simulation mode to test and build confidence, then use the workflow engine to customize every single aspect of your AI’s behavior.

Final thoughts

Atlassian Intelligence search is a powerful feature for teams that are all-in on the Atlassian ecosystem and have the time to maintain a perfect Confluence knowledge base. It’s a good start for centralizing information that lives within their "walled garden."

But for most teams, knowledge is spread far and wide across a dozen different apps. In that world, a siloed AI just doesn’t cut it. An effective AI strategy needs a tool that can connect to all your knowledge sources, giving you the flexibility, control, and confidence to automate support intelligently.

Ready to build an AI that knows your whole business, not just part of it?

If you’re tired of knowledge silos and want an AI that works with all the tools you already use, eesel AI is for you.

Connect Confluence, Google Docs, past tickets, and more in just a few clicks. Simulate your AI's performance on real data, and go live with confidence.

Try eesel AI for free or book a 30-minute demo to see how you can unify your knowledge today.