A practical guide to setting up a Confluence GPT in 2025

Let's be real: Confluence is a beast. It’s where your team’s collective brain is stored, project plans, meeting notes, product specs, the works. But as that knowledge base balloons, finding one specific piece of information can feel like digging through a digital junk drawer. We've all been there, scrolling endlessly through spaces and pages, just hoping to land on the right doc.

What if you could just… ask a question and get an answer? That’s the whole idea behind a "Confluence GPT", an AI assistant that can instantly search, understand, and pull together information from all your company’s documentation. This guide will walk you through what a Confluence GPT actually is, the different ways you can build one, the major hurdles like security and accuracy, and how to pick the right path for your team.

What is a Confluence GPT?

A Confluence GPT isn't some off-the-shelf product. It’s more of a concept: using a large language model (like the one that powers ChatGPT) to create a conversational search tool for your Confluence content. Instead of typing keywords and getting a list of pages to sift through, you can ask complex questions in plain English and get a direct, synthesized answer.

The benefit is pretty straightforward but makes a huge difference in day-to-day work. It turns your static library of documents into an interactive expert that’s always on. This means your team can get quick, accurate answers without having to hunt for information or tap a colleague on the shoulder. For new hires trying to get up to speed, support agents looking for a fast solution, or project managers who need a quick update, it's a massive time-saver.

Approaches to building a Confluence GPT

There are a few different paths you can take to bring AI into your Confluence workspace, and each one comes with its own mix of pros, cons, and headaches. Let's break down the most common approaches so you can figure out what makes sense for you.

Atlassian's native AI (Rovo)

Atlassian has its own built-in AI, Rovo, which is designed to work across its entire product family, including Confluence. Because it's built right in, users can use it to draft content, summarize pages, and search for answers without leaving the platform.

The main catch is that it’s designed to live almost entirely inside the Atlassian world. If your company knowledge is also spread across Google Docs, Slack threads, or your help desk, Rovo has no idea it exists. This creates information silos. You're also pretty limited on customization; you’re tied to Atlassian's model, which might not fit the specific tone of voice or complex workflows your team needs.

Pricing for Rovo is bundled into Confluence's paid plans, but it runs on a credit system that can make costs feel a bit unpredictable. Each user gets a certain number of "AI credits" per month, and if you burn through them, the feature just stops working. That can get pretty frustrating if your team starts to rely on it.

Plan	Price (per user/month, annual)	Rovo AI Credits
Free	$0	Not included
Standard	$5.42	25 per user/month
Premium	$10.44	70 per user/month
Enterprise	Custom	150 per user/month

Build a custom solution with OpenAI

This is the full-on DIY approach, similar to what some tech-savvy folks on Reddit have pulled off. Using OpenAI's platform and resources like the OpenAI Cookbook, you can build a custom GPT that connects to Confluence through its API. It involves creating an app in the Atlassian Developer portal, sorting out authentication, defining an OpenAPI schema, and writing detailed instructions for how the GPT should act.

The biggest plus is obvious: you get total flexibility and control. You can tweak the AI’s personality, its functions, and exactly how it uses your data.

But, and it’s a big but, the downsides are pretty hefty. This route demands a lot of technical expertise and developer time, not just to build it, but to keep it running.

One small mistake in the configuration could accidentally expose sensitive company information. On top of that, this kind of setup is often a one-trick pony. Trying to scale it to handle more knowledge sources or more complex tasks quickly becomes a full-time, expensive software project.

Use a dedicated AI integration platform

For most teams, the sweet spot is in the middle: a third-party platform built specifically to connect company knowledge to an AI model. These no-code or low-code tools act as the bridge between the rigid, out-of-the-box native solutions and the sheer complexity of a custom build.

They offer a few key advantages. First, the setup is usually way faster because they handle all the tricky backend integrations for you. Second, they're built to connect to dozens, if not hundreds, of knowledge sources, not just Confluence. Finally, any platform worth its salt is built with security as a core feature, offering things like data encryption, access controls, and strict privacy policies from the get-go.

Key Confluence GPT challenges you can't ignore

The thought of a Confluence GPT is exciting, but to actually make it work well, you have to get a few critical things right. Mess these up, and your shiny new AI assistant could end up being useless, or worse, a liability.

Ensuring security and data privacy

This is the big one. Your internal documentation has everything, proprietary information, strategic plans, sensitive data. The absolute last thing you want is for that information to be used to train a public AI model or get exposed through a security hole.

Platforms built for this, like eesel AI, approach this with a "privacy-first" mindset. Your data is never used to train generalized models outside of your own company. All information is encrypted, and for businesses that need to meet strict compliance standards, data can be hosted exclusively in the EU to align with GDPR.

Maintaining accuracy and avoiding AI hallucinations

An AI "hallucination" is when the model just makes up an answer. It sounds confident and plausible, but it's completely wrong. This can happen if it’s pulling from outdated docs, misinterpreting a question, or just doesn't have the right context. If your team can't trust the answers, they'll stop using the tool pretty fast.

A good tool helps you combat this. With eesel AI, for instance, you get fine-grained control to build a more reliable system. You can "scope" the AI to learn only from specific Confluence spaces or even individual pages, which keeps it from going off-topic. More importantly, you can bring Confluence together with other sources of truth, like your official help center or product docs in Notion, to give it a complete and accurate picture to work from.

The reality of setup and ongoing maintenance

A custom-built solution is never a "set it and forget it" deal. APIs get updated, security issues pop up, and you'll need a way to incorporate feedback from your team. Native tools like Rovo are easier to manage, but you have almost no control. If the search results are bad or a summary is off, there's often nothing you can do but hope Atlassian pushes an update.

This is where the "go live in minutes" idea really pays off. A self-serve platform like eesel lets you connect Confluence and other apps with just a few clicks. You get the power of a custom solution without the perpetual engineering headache.

Go beyond a Confluence bot with a unified knowledge platform

A Confluence GPT is a great starting point, but its true power is unlocked when it can access all of your company’s knowledge, no matter where it is. The reality for most companies is that important information is scattered everywhere. The answer to a question might be in a Confluence doc, but it could just as easily be in a Google Docs project brief, a Slack conversation, or a past support ticket in Zendesk. A bot that only knows Confluence is always going to have blind spots.

This is exactly the problem that a unified platform like eesel AI is built to solve. It doesn’t just create a Confluence bot; it creates a single, unified brain for your entire company. You can seamlessly pull in knowledge from Confluence, Google Docs, Notion, Slack, and your help desks like Zendesk, Freshdesk, and Intercom.

Then, you can use that same unified knowledge base to power different solutions. You could have an internal chatbot in Slack for your team, an AI agent in your helpdesk that automates support tickets, or a copilot that helps agents write faster replies. Before you even launch, you can use a simulation mode to test your setup on past conversations. This lets you see exactly how the AI will perform and what it knows, giving you peace of mind before it ever talks to a real user.

Finding the right Confluence GPT for your team

A Confluence GPT can fundamentally change how your team finds and uses information, turning a passive library of documents into an active, intelligent assistant. While native tools offer an easy way in and custom builds give you all the power, both come with major trade-offs in either flexibility or complexity.

For most teams, the most practical approach is using a dedicated platform that not only solves the immediate problem of making Confluence searchable but also sets you up for a future where all your company knowledge is connected, secure, and available in an instant.

Ready to turn your Confluence spaces, and all your other documents, into an expert AI assistant?

Get started with eesel AI today. Connect your Confluence and other knowledge sources in minutes and build an AI that gives your team the right answers, right away. Try it free.