Can ChatGPT access Confluence? A 2025 guide

Your team’s brain lives in Confluence. It’s where every project plan, technical doc, and meeting note is carefully stored. Then there’s ChatGPT, which has completely changed how we find information. So, the question on everyone’s mind is: can you get these two to talk to each other? Can ChatGPT access Confluence and pull out instant, smart answers from your company’s own knowledge?

The short answer is yes, but it’s not quite as simple as flipping a switch. Lots of teams give it a shot only to hit technical walls, wrestle with outdated information, or run into security headaches. It’s a common story you’ll see in threads on

and the Atlassian community.

This guide will walk you through the different ways to connect ChatGPT to Confluence, from the simple copy-paste methods to the more complex custom builds. We’ll look at the good and the bad of each approach and show you the most straightforward way to turn your Confluence wiki into a genuinely useful AI assistant.

Understanding Confluence and ChatGPT

Before we get into the how-to, let’s do a quick refresher on these two tools and why getting them to work together is such a big deal for so many businesses.

What is Confluence?

Confluence is that one central place where your team creates, organizes, and talks about work. Think of it as your official source of truth, using Pages and Spaces to hold everything from project specs and meeting notes to company policies and how-to guides. It’s where your company’s knowledge is supposed to be.

What is ChatGPT?

ChatGPT is a large language model from OpenAI that’s really, really good at understanding and writing like a human. You can ask it to explain a tricky concept, summarize a long document, or answer pretty much any question you throw at it. Its conversational style has made it a go-to tool for getting information fast.

Why connect ChatGPT and Confluence?

When you bridge the gap between these two, you get some pretty great perks that teams are looking to leverage:

• Stop digging for answers. You can get immediate responses to your questions without having to manually search through what feels like a hundred different Confluence pages.

• Give your team their time back. Less time spent searching for information or bugging coworkers for help means more time to focus on the work that actually matters.

• Get summaries on demand. Instantly boil down long project documents, technical guides, or meeting notes into the key points you actually need.

• Put support on autopilot. You can power internal support bots for HR or IT questions, or even help out your customers with a chatbot that uses your trusted documentation as its brain.

How to connect ChatGPT to your Confluence data

It’s obvious that connecting these two is a huge win, but how you do it makes all the difference. Let’s break down the common methods, from the simplest to the most effective.

Method 1: The manual ‘export and pray’ approach

The most basic method, and usually the first thing people try, is to export a Confluence space as a PDF or Word document and then upload that file to a custom GPT. On the surface, it seems like a quick and easy win.

The downsides:

• Instantly stale data. This is the real dealbreaker. The AI’s knowledge is only as good as your last export. The second someone on your team updates a page in Confluence, your GPT is officially working with old news. You’d have to constantly re-export and re-upload files to keep it fresh, which just isn’t realistic.

• Mangled formatting. Confluence pages are filled with tables, macros, and specific formatting that just don’t survive the trip to a PDF or Word doc. When you export, that structure often gets scrambled, leading to confusing or poorly formatted answers from your AI.

• It’s all or nothing. You don’t get much control. You can’t easily pick and choose specific pages to sync or manage who has access to what. This makes it tough to scale and could cause problems if you only want the AI to know about certain things.

Method 2: The technical DIY route with custom GPT Actions

If you have developers on hand, building a custom GPT Action is another option. This involves creating a direct, real-time API connection to Confluence. As you can see in guides like the OpenAI Cookbook, the process means creating an app in the Atlassian developer portal, writing a complex OpenAPI schema, and figuring out OAuth 2.0 authentication.

The downsides:

• It’s incredibly complex. This isn’t something you can set up over a lunch break. It requires serious developer resources and a deep understanding of API integrations and authentication. For most teams, it’s a full-blown engineering project that can take weeks, if not months.

• The maintenance is a headache. APIs change. Security rules get updated. Anything that shifts on either the Confluence or OpenAI side can break your custom-built connection, meaning you need ongoing engineering time to fix and maintain it. It’s a continuous cost, not a one-time setup.

• Security is all on you. As people point out in the Atlassian Community, managing secure access and preventing data leaks is a huge challenge. When you build it yourself, the entire burden of security and governance falls on your team’s shoulders.

Method 3: Using third-party integration platforms

Third-party tools can offer a happy medium, simplifying the connection without forcing you to build everything from scratch. But they’re not all the same.

Automation platforms like Zapier

Tools like Zapier operate on a simple trigger-and-action model. For instance, you could set up a Zap where "When a new page is made in Confluence (trigger), summarize it with ChatGPT (action)."

The catch? These platforms are built for specific, one-way tasks. They aren’t designed to create a smart, conversational AI that can answer any question based on your entire knowledge base in real time. They’re great for simple automations, but they won’t build you an always-on expert.

Basic marketplace apps

You can also find apps on the Atlassian Marketplace that add a little AI magic inside the Confluence interface, like summarizing a page you’re on or helping you write content.

The catch? These tools are usually stuck inside the Confluence website. They don’t let you build an AI assistant that can be used where your team actually works, like in Slack, Microsoft Teams, or a helpdesk such as Zendesk. This really limits how useful they can be.

Dedicated AI integration platforms (the best approach)

This brings us to platforms built specifically to create and deploy AI assistants using your existing company knowledge.

This is exactly what eesel AI does. Unlike the other methods, eesel AI gives you a no-code platform that connects directly to Confluence and your other apps to build an AI assistant that actually works.

Here’s why this approach is different:

• Go live in minutes, not months. It’s a one-click integration. You get to skip the entire developer-heavy DIY mess and can have a working AI assistant ready to go in less time than it takes to brew a pot of coffee.

• Bring all your knowledge together. Your company’s brain probably isn’t just in Confluence. With eesel AI, you can also pull in knowledge from Google Docs, past support tickets, and over 100 other sources to create a single brain for your AI.

• Use your AI anywhere. The assistant isn’t trapped. You can deploy it in Slack for your team’s internal questions, in your helpdesk to help agents, or even as a customer-facing chatbot on your website.

• You’re in complete control. You get to decide exactly which Confluence Spaces and pages the AI learns from. This makes sure it stays on-topic and only gives answers based on information you’ve approved.

A screenshot of the eesel AI platform showing how it can connect to Confluence and many other apps to build a comprehensive knowledge base.

A comparison of the integration options

To make the choice a bit clearer, let’s ditch the table and just talk through how these methods stack up.

If you go the manual export route, your setup time is just a few minutes, which sounds great. But your data is never live, the maintenance is a constant, manual pain of re-uploading files, and you can’t connect any other knowledge sources.

The DIY custom GPT approach gives you real-time data, but the setup time can stretch into weeks or even months of expensive developer time. It also comes with high ongoing maintenance costs, and you’re generally limited to connecting just Confluence.

Automation platforms are quicker to set up, maybe a few hours, but they only work based on specific triggers, not for real-time Q&A. They have moderate maintenance needs and limited ability to unify knowledge from different places.

Finally, a dedicated platform like eesel AI gets you up and running in minutes with a no-code setup. The data is always live, the platform handles the maintenance, and you can unify knowledge from Confluence and over 100 other sources. Plus, you can deploy your AI assistant wherever you need it.

What do Confluence integration solutions cost?

The price tags for these solutions are all over the map, so it’s good to know what you’re getting into.

• Confluence AI (Rovo): Atlassian’s own AI, Rovo, is part of its paid plans. It uses a credit system, which can make your bill a bit of a surprise. According to Atlassian’s pricing page, the Standard plan gives you just 25 AI credits per user each month, and the Premium plan offers 70. As your team uses it more, you can burn through those credits fast and end up with extra fees.

• DIY / Custom GPT: The big cost here is your developers’ time. Building and maintaining a custom integration can easily cost you tens of thousands of dollars, and that’s before you even factor in the separate OpenAI API fees you’ll be paying.

• Automation Platforms: Zapier and similar tools charge you based on how many automated tasks you run. Connecting multiple apps and handling a lot of workflows usually means upgrading to a pricey premium plan.

• eesel AI: This is much more straightforward. You get transparent, predictable pricing with plans that start at $299/month ($239/month if billed annually). The plans are based on overall usage, not confusing per-ticket or per-answer fees. This means you can scale up your AI use without worrying about a surprise bill. Best of all, you can start on a monthly plan and cancel anytime, which is a lot more flexible than competitors who often try to lock you into an annual contract.

The simplest way to let AI access your wiki

While other methods are too clumsy, too complicated, or just too limited, a dedicated platform like eesel AI was built from the ground up to solve this exact problem. It turns your Confluence docs into a reliable, always-on AI expert that you can put to work wherever your team needs it.

• Seriously simple setup. You can forget about wrestling with APIs or sitting through a required sales demo. With eesel AI, you connect your Confluence account with a single click. The platform is truly self-serve, so you can go from signing up to having a working AI assistant in minutes.

• Test it with confidence before you launch. One of the biggest worries with AI is trust. How do you know it’s giving the right answers? eesel AI has a unique simulation mode that lets you test it on your past data. You can see exactly how it would have answered previous questions and fine-tune its behavior in a safe environment before it ever talks to a real user.

A screenshot of the eesel AI simulation mode, where you can test how the AI would answer questions based on your Confluence data.

• It’s more than just a chatbot. eesel AI isn’t just for answering questions. It’s a full workflow engine. You can create custom actions for your AI, allowing it to do things like triage support tickets, look up order details from Shopify, or escalate an issue to the right person. This turns it from a simple information tool into an active part of your team’s processes.

What’s the verdict?

Can ChatGPT access Confluence? Absolutely. But as we’ve covered, the method you pick is the difference between getting a powerful, real-time assistant and getting stuck with a frustrating, high-maintenance project. Manual exports are outdated the minute you create them, custom builds are too expensive for most teams, and other tools are usually built to solve a different, simpler problem.

For teams that need a reliable, scalable, and easy-to-manage way to tap into the knowledge stored in their Confluence wiki, a dedicated AI platform is the clear winner. It handles all the technical headaches for you while giving you total control over what your AI learns and does.

This video demonstrates how a custom bot using ChatGPT capabilities can interact with a knowledge base in Confluence to answer team questions.

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