A practical guide to building a Confluence ChatGPT in 2025

Let’s be real: Confluence is a beast. For a lot of teams, it’s the company’s brain, holding everything from product specs and marketing plans to HR policies and IT guides. But finding the one specific thing you need can feel like digging for a needle in a haystack. You know the answer is in there somewhere, but after a few keyword searches lead you down a rabbit hole, it’s just easier to ping a coworker on Slack.

What if you could just… ask Confluence a question and get a straight answer? That’s the simple but game-changing promise of connecting it with an AI like ChatGPT.

This isn’t about slapping a fancier search bar on top of what you already have. It’s about turning a static library of documents into a smart, conversational resource your whole team can actually use. We’ll walk through what a Confluence ChatGPT is, how it works, the different ways you can set one up, and what to look for so you don’t end up with a tool that nobody uses.

What is a Confluence ChatGPT?

A Confluence ChatGPT is basically an AI chatbot that’s been trained exclusively on your company’s Confluence pages. Instead of typing in keywords and scanning through a list of search results, your team can ask questions in plain English and get a clear, summarized answer.

Think of it like a new hire who has somehow managed to read and memorize every single page in your Confluence.

Under the hood, it uses a Large Language Model (LLM), the same kind of tech behind ChatGPT, but points it directly and securely at your private knowledge base. The whole point is to make all that internal knowledge easy to find. It helps cut down on the same questions being asked over and over, frees up your experts from being human search engines, and lets everyone find the info they need to do their jobs, right when they need it.

How a Confluence ChatGPT actually works

So, how do you take a public AI that knows about everything from Shakespeare to SpongeBob and turn it into an expert on your company’s internal wiki? The process might sound techy, but the idea is actually pretty simple. It’s a technique called Retrieval-Augmented Generation (RAG), and it’s what keeps the AI focused on your documents, not the entire internet.

Here’s a quick look at what’s happening behind the curtain:

1. Indexing: First, all your Confluence pages get broken down into smaller, manageable chunks. Each chunk is then turned into a numerical representation (called a vector) and stored in a special kind of database built for finding meaning, not just keywords. It’s like creating an incredibly detailed index for a library.

2. Retrieving: When someone asks a question, say, "What’s our policy on parental leave?", the system doesn’t just lob the question at the AI. It first scans that special database to pull out the most relevant chunks of text from your Confluence.

3. Generating: Finally, the AI model gets handed those relevant text snippets as context, along with the original question. It uses only that information to put together a clear, coherent answer in normal language.

This three-step dance is what keeps the AI grounded. It can only answer based on the information it’s given from your Confluence, which stops it from making things up or pulling in random, outdated stuff from the web.

While building this from scratch is a heavy lift, modern tools like eesel AI handle all of this for you. You can connect your Confluence space with a few clicks, and the platform takes care of all the indexing, retrieving, and generating in the background.

Common ways to create a Confluence ChatGPT

Once you decide you want to give your team this kind of access, you’ll find there are a few ways to go about it. Each route has its own trade-offs, from duct-taped-together plugins to full-blown company-wide platforms.

The DIY approach with APIs

If you have a team of developers with some time on their hands, building a custom solution with the OpenAI API and Atlassian’s own APIs is certainly an option. You can find technical guides, like the OpenAI Cookbook, that show you the ropes.

The big plus here is control. You can build the exact experience you want. The downside, however, is pretty huge. This path requires dedicated developer hours, not just to build it (which can take months), but for all the ongoing maintenance, bug fixes, and security patches. You’ll be on the hook for handling tricky things like authentication and making sure your data pipeline is secure and efficient.

Native Atlassian Intelligence (Rovo)

Atlassian has its own AI, now called Rovo, which is baked right into Confluence and its other products. It’s designed to help you search, summarize, and create content inside the platform.

The good part is that it’s already there, no installation needed if you’re on the right plan. The major limitation? It’s stuck inside the Atlassian bubble. Most companies’ knowledge doesn’t live in just one place. If you have important info in Google Docs, old support tickets in Zendesk, or key discussions in Slack, Atlassian’s AI is completely blind to it. That means you get incomplete answers. The pricing is also bundled into pricier Confluence plans and is often tied to a credit system, which can be tough to predict.

Third-party apps from the Atlassian Marketplace

A quick look at the Atlassian Marketplace will show you a bunch of third-party apps that claim to add ChatGPT to Confluence. These are usually easy to install and focus on one specific job, like summarizing a page.

These can be handy for small, one-off tasks. The problem is that many of them are just thin wrappers around the public ChatGPT API. They often don’t have the sophisticated RAG setup needed for accurate answers based on your private data. More importantly, they’re designed to be used inside Confluence, mostly for people writing the content. They don’t really work as a standalone chatbot that your whole team can ask questions from wherever they’re working, like Slack or Microsoft Teams.

A dedicated AI knowledge platform

A more modern way to tackle this is with a specialized platform built to connect all of your company’s knowledge and put AI agents right where your team works.

For most companies, this is the way to go. A good platform should be self-serve, connect to way more than just Confluence, and let you control the AI’s behavior without needing a team of engineers. This is exactly what eesel AI was built for. It connects to Confluence but also to places like Google Docs, helpdesks, and chat tools, creating a single source of truth for your AI. Best of all, you can get it up and running in minutes, not months.

Key features (and limitations) to look for

Not all Confluence ChatGPT tools are the same. When you’re looking at your options, you need to go beyond the basic sales pitch of "ask your docs" and check for the features that will make or break whether your team actually uses the thing.

Setup and ease of use

Many AI tools, especially the DIY or big enterprise ones, have these long, complicated setup processes that can take weeks and require getting IT and engineering involved.

You should be looking for a platform that’s genuinely self-serve, where you can connect your accounts with one-click integrations. You shouldn’t have to schedule a demo or talk to a salesperson just to see if it works. This is one area where eesel AI really shines. The goal is radical simplicity. You can sign up, connect Confluence, and launch an AI assistant in your helpdesk or Slack in just a few minutes, all without writing code.

A flowchart showing the simple, self-serve setup process for a Confluence ChatGPT solution like eesel AI.

Knowledge source unification

Here’s a simple truth: your company’s knowledge is never in just one place. If your AI only knows about Confluence, it has massive blind spots. When an employee asks a question, they don’t care if the answer lives in a Confluence page, a Google Slide, or an old Slack thread, they just want the right answer.

A good solution has to be able to connect to all your knowledge sources, including wikis, shared drives, helpdesk articles, past tickets, and chat tools. Platforms like eesel AI are built to bring all that scattered knowledge together. It easily connects to over 100 sources like Confluence, Google Docs, and Zendesk, making sure your AI has the full context before it tries to answer a question.

A screenshot of the eesel AI platform showing various integrations, highlighting how a Confluence ChatGPT can unify knowledge sources.

Customization and control

A generic, out-of-the-box AI won’t get your company’s tone of voice, internal slang, or rules for escalating issues. This leads to bland, unhelpful answers that frustrate people.

You need the ability to shape the AI’s personality, tell it what information it should and shouldn’t use, and create custom actions it can take. For example, maybe you want your IT help bot to be able to create a Jira ticket automatically. eesel AI gives you a full workflow engine, not just a Q&A bot. You can use a powerful prompt editor to control the AI’s tone and give it specific instructions on how to handle things. There’s even a simulation mode that lets you test it on thousands of past conversations before you launch it, so you can be confident it will work as expected.

An image of the eesel AI interface, demonstrating the customization and control features for a Confluence ChatGPT.

Pricing models

AI pricing can be a minefield, and it’s easy to get stuck with a bill that’s much higher than you expected. Here’s a rundown of the common models you’ll see.

• Atlassian Intelligence: This is part of Confluence’s Standard and Premium plans. It doesn’t have a separate cost, but your usage is capped by AI credits. You might find yourself hitting that limit right as your team starts to depend on it, forcing you into an expensive upgrade.

• Marketplace Apps: These usually have a free tier that doesn’t do much, with paid plans that charge per user, per month. It’s easy to budget for initially, but the cost can really climb as your team gets bigger.

• DIY / API Usage: If you build it yourself, you’re paying a provider like OpenAI directly for API calls. This pay-as-you-go model is totally unpredictable and can get very expensive as more people use it, not to mention the hidden cost of developer salaries.

• eesel AI: The pricing for eesel AI is designed to be straightforward. Plans are a flat monthly fee for a certain number of AI interactions. There are no per-user fees and no per-resolution fees, which means your bill won’t suddenly jump just because you had a busy month.

Here’s a quick comparison to make it clearer:

Approach	Pricing Model	Predictability	Key Consideration
Atlassian Intelligence	Bundled in Confluence Plans	Medium	Tied to your Confluence subscription; usage limits apply.
Marketplace Apps	Per User / Per Month or Free	High	Often limited in features; check for hidden costs.
DIY (API Calls)	Pay-as-you-go	Low	Costs can scale unpredictably with usage.
eesel AI	Flat Fee (Interaction-based Tiers)	High	Predictable monthly cost with no surprise fees.

This video explores how a Confluence ChatGPT integration can optimize your team's agile practices and save time.

Your Confluence ChatGPT should go beyond just answers

A simple Confluence ChatGPT is a good start, but a tool that’s actually useful does more than just answer questions. The right platform should break down information silos, not just build a new one around Confluence.

It should be easy enough for anyone to set up and manage, pull knowledge from all the places your team works, and give you the control you need to build trust and get people to use it. Most importantly, it should deliver answers right where your team already is, whether that’s Slack, Microsoft Teams, or your helpdesk.

Instead of sinking months into a complex DIY project or settling for a limited plugin, you can build an AI assistant that connects to Confluence and everything else. Take eesel AI for a spin and you can launch your first AI agent in minutes.