Confluence AI API: The complete guide to unlocking your knowledge base

Your company’s Confluence space is probably overflowing with information. It’s got everything from project plans and dense technical docs to the latest HR policies. But getting that knowledge out and putting it to work automatically can be a real pain. It often feels like you have a library full of valuable books, but they’re all locked away.

Lots of teams want to use their Confluence data to power things like internal help bots or customer-facing assistants. The idea is great, but when they try to build it, they usually hit a wall. It turns out the path isn’t as simple as you’d think.

This guide will walk you through all the real options for creating a "Confluence AI API". We’ll look at what Atlassian offers out of the box, what it takes to build it yourself (spoiler: a lot), and how some third-party platforms can give you a much-needed shortcut.

What is the Confluence AI API?

First, let’s clear something up. A "Confluence AI API" isn’t an official product you can buy from Atlassian. It’s really just a name for the method you use to access all the knowledge in Confluence to power an AI application, like a chatbot that can answer questions using your internal documentation.

There are generally two ways people try to do this:

1. The Direct Way: You use Confluence’s standard REST API to pull out all the raw page data and then pipe it into an AI system you’ve built yourself.

2. The Third-Party Way: You use a platform that already has a connector for Confluence and provides the AI layer for you, saving you a ton of work.

But here’s the single biggest hurdle you need to know about right now: as of late 2024, Atlassian doesn’t have a public API for its own AI features (which you might know as Atlassian Intelligence or Rovo). This is a massive roadblock. It means you can use their AI inside a Confluence page, but you can’t use it to power your own apps, like a bot in Slack or an assistant on your website.

Atlassian’s native AI features

Atlassian Intelligence (recently folded into a rebrand called Rovo) is the company’s own AI, built right into its products. It’s really designed to help you get things done faster within the Atlassian ecosystem.

For everyday tasks, its features are pretty useful. You can use it to:

• Summarize long, winding pages or messy comment threads.

• Whip up a first draft of a document from a quick prompt.

• Clean up your writing by changing the tone or fixing grammar mistakes.

• Find answers to questions based on what’s inside your Confluence instance.

It all sounds pretty good, but once you dig a little deeper, the limitations start to show.

Key limitations of Atlassian Intelligence

While it’s handy for one-off tasks, Atlassian’s AI just doesn’t work when you want to build any kind of automated workflow or custom tool.

The biggest issue is the no public API problem we just talked about. This is the real deal-breaker. You’re completely boxed into their ecosystem. Want to build a bot that helps employees find HR info in Microsoft Teams? Or an AI assistant to help your support agents in Zendesk? Atlassian Intelligence can’t help you there. It’s a closed loop.

Then there’s the problem of siloed knowledge. The AI only knows what’s inside your Atlassian tools. But let’s be honest, where does your team actually keep all its information? It’s probably spread all over the place, in Google Docs, Notion, old support tickets, and who knows where else. Atlassian’s AI can’t see any of that, so the answers it gives will always be incomplete.

The platform is also pretty rigid. You can’t add your own custom actions or workflows. What if you want your AI to do more than just find an answer? Maybe you need it to look up a customer’s order in Shopify or create a ticket in Jira Service Management. With Atlassian’s native tools, you’re stuck. What you see is what you get.

Finally, you have to think about the cost and plan restrictions. You only get access to these AI features on certain subscription plans, and even then, your usage is capped.

Understanding Atlassian Intelligence pricing

Atlassian includes its AI features in its Confluence Cloud plans. To get anything useful, you have to be on a paid plan, and the more you pay, the more "AI credits" you get to spend each month.

Here’s a quick look at how the AI features shake out across the different plans:

Feature	Free	Standard	Premium	Enterprise
Price (per user/mo)	$0 (up to 10 users)	Starts at $5.16	Starts at $9.73	Contact Sales
Atlassian Rovo (AI)	No	Yes	Yes	Yes
AI Credits per user/mo	0	25	70	150
Key AI Features	N/A	Rovo Search, Chat & Agents	Everything in Standard	Everything in Premium

With this pricing model, you’re constantly being pushed toward the more expensive plans to get any real use out of the AI. And even if you spring for the Enterprise tier, your usage is limited by a credit system, which can get unpredictable and expensive as your team starts using it more. You can check out the full details on the official Confluence pricing page.

The DIY approach to building a Confluence AI API

Okay, so if the built-in tools don’t cut it, why not just build it yourself? This is a tempting idea, especially for companies with a talented engineering team. The process usually looks something like this:

1. Get the data: First, you use the Confluence REST API to pull down all the pages and files you want the AI to know about.

2. Clean the data: Here’s where the fun starts. Confluence content is often a messy mix of HTML or XML. You have to parse all of that into clean, simple text.

3. Chunk it up: AI models can’t read a whole 10-page document at once. You have to break the content down into smaller, logical pieces.

4. Turn it into vectors: Next, you use an embedding model (like one from OpenAI) to convert those text chunks into a series of numbers called vectors. It’s basically like turning your text into coordinates on a map.

5. Store it: You then dump all these vectors into a special vector database like Milvus, Pinecone, or Chroma, which is designed to do super-fast searches for similar pieces of text.

6. Build the app: Finally, you have to build the actual application that takes a person’s question, turns it into a vector, finds the most relevant chunks from your database, and sends all of that context to a large language model (LLM) to generate an answer.

Why the DIY method is a huge project

If that sounds like a lot of work, that’s because it is. This is not a weekend project. It’s a serious engineering investment that comes with a whole host of problems you’ll have to solve.

For starters, you need serious technical skills. This isn’t a job for your IT helpdesk. You need a team of engineers who are comfortable with APIs, data processing, machine learning tools like LangChain, and managing databases.

You’ll also hit some snags with the Confluence API. As many developers on the Atlassian community forums will tell you, the API has its quirks. You have to handle pagination (pulling data in small batches), be careful not to hit rate limits and get blocked, and figure out a reliable way to clean up the messy formatting in Confluence pages.

Then you have the hidden costs and ongoing work. The expenses aren’t just developer salaries. You’re paying for API calls to an embedding model every single time a document is added or updated. You’re paying to host the vector database. And you’re paying with time for the constant maintenance needed to deal with API changes, fix bugs, and just keep the lights on. It can easily become a full-time job for an entire team.

This guide breaks down how to dive into the Confluence API, which is a key part of the DIY approach.

Using a third-party platform for a Confluence AI API

After looking at the first two options, you might be thinking there has to be a better way. And there is. The solution is an all-in-one AI platform that does all the complicated backend work for you. These platforms give you a ready-to-go AI layer that connects straight to Confluence and your other tools, offering the power of a custom solution without any of the headaches.

Why a dedicated platform is the practical choice

The difference is like night and day. Instead of a project that will take your engineering team months, you can get up and running in a few minutes. A good platform lets you connect your tools and launch an AI assistant in a single afternoon.

The best platforms also help you bring all your knowledge together. They don’t just connect to Confluence. They can connect to everything else, too, like your Google Docs, Notion, old Zendesk tickets, and Slack history. This creates a single, unified "brain" for your AI, allowing it to give answers that are actually complete.

You also get powerful features right away. Things like a simulation environment to test your AI, analytics to find gaps in your knowledge, or customizable AI actions are incredibly complex to build from scratch. A dedicated platform gives you all of that right out of the box.

Introducing eesel AI: The Confluence AI API without the code

This is where a tool like eesel AI really makes a difference. It’s built to give you the flexibility of a custom "Confluence AI API" without making you write a single line of code.

Connecting Confluence to eesel AI is a one-click integration. You just authorize the app, and you’re done. There’s no messing with API keys, setting up databases, or writing scripts to clean your data.

eesel AI makes connecting your Confluence AI API to other apps a one-click process.

But the really cool part is the customizable AI actions. This is what gives you the flexibility you actually need. With eesel AI, you can set up your AI to do more than just answer questions. It can triage a support ticket in Intercom, look up order details from your database, or send an escalation to a specific Slack channel. It can truly automate a workflow from start to finish.

With a third-party Confluence AI API platform, you can set up custom rules and actions without any coding.

You also get precise control with scoped knowledge. You can easily decide which Confluence spaces or even which pages the AI is allowed to use for different situations. This makes sure your sales bot doesn’t start trying to answer questions using engineering documents, keeping everything relevant.

Best of all, you can test everything with confidence. eesel AI comes with a simulation mode that lets you test your AI on thousands of your past conversations. You can see exactly how it would have performed and what your automation rate would have been before you let it talk to a single real user. This kind of risk-free testing is just not possible with a DIY setup and is something most other tools don’t offer.

Simulation mode allows you to test your Confluence AI API on past conversations to predict performance.

Stop building, start automating with a Confluence AI API

So, we’ve looked at three different ways to get the knowledge out of your Confluence instance:

1. Atlassian’s Native AI: Easy for simple, in-app tasks but far too limited for building custom tools since it has no public API.

2. The DIY Approach: Powerful if you can pull it off, but it’s incredibly complex, expensive, and a huge distraction from your actual business.

3. Third-Party Platforms: The smart choice that gives you the power of a custom solution with the simplicity of a tool that just works.

At the end of the day, your goal is to use your company’s knowledge, not to become an expert in AI infrastructure. For almost every company, building a "Confluence AI API" from the ground up is the wrong move. The best path is to pick a tool that gives you the power you need without all the technical baggage.

Unlock your Confluence knowledge today with a Confluence AI API

Instead of spending the next few months struggling with APIs and vector databases, you could have a powerful AI assistant trained on your Confluence data and ready to go in minutes.

See how eesel AI can connect to all your knowledge sources and turn them into a reliable, automated agent that works everywhere you do.

Start your free trial and see how it works for yourself.