Let's be real: for most of us, company knowledge is a mess. The one piece of info you desperately need is probably hiding in a dusty Jira ticket, a Confluence page no one’s touched in years, or a Slack thread from last Christmas. Trying to connect the dots can feel impossible, forcing you to make calls based on half the story.
Atlassian saw this and built a new feature for its AI tool, Rovo, called Rovo Deep Research. The idea is to give you an AI researcher that can sift through all that scattered information and pull together a complete picture.
But is it the answer for everyone? In this guide, we’ll walk through what Rovo Deep Research actually is. We'll look at how it works, what it's good for, and, more importantly, where it falls short, especially when you compare it to AI tools made for the fast-paced world of customer support.
What is Rovo Deep Research?
Rovo Deep Research is a specific skill built into Rovo, Atlassian's AI assistant. This isn't your standard Q&A bot that spits out a quick, one-sentence answer. It's more like an AI research analyst for all your internal company stuff. You can throw a big, open-ended question at it, and it will go off on its own to investigate, coming back with a proper, detailed report.
Its main goal is to figure out a research plan, dig through information in your connected Atlassian and other apps, and then package its findings in a way that makes sense.
The end result isn't just a quick message in a chat box. You get a full-blown document, like a Confluence page, that’s organized with sections, summaries, and links pointing back to where it found the information. It’s designed to give you the whole picture, not just a hint.
How Rovo Deep Research works: A look under the hood
So, how does Rovo Deep Research turn a simple question into a full-blown analysis? It's not just running a keyword search. There's a bit more going on behind the scenes involving reasoning, planning, and piecing things together.
Starting with the Teamwork Graph
The core of Rovo is something Atlassian calls the "Teamwork Graph." Think of it as a map of how everything in your Atlassian tools connects, people, projects, documents, you name it. It understands that a specific Jira project is tied to a particular Confluence page and knows who is working on it.
This graph is what gives Rovo its context. It’s how it figures out that when you ask about "Project Chimera," you're talking about the internal marketing launch, not the mythical beast. This tight integration with the Atlassian world is its biggest advantage, as it gives Rovo a solid grasp of your company's inner workings.
Researching in rounds
When you ask Rovo a complicated question, it doesn't just do one big search. First, it breaks your query down into smaller, more manageable sub-questions. Then, it starts looking for answers to these questions at the same time across all your connected data sources.
After each "round" of searching, it takes a step back to review what it has found. It tosses out anything that's not relevant and figures out what information is still missing. Based on those gaps, it plans its next wave of searches. This step-by-step process helps it be more thorough.
How AI models write the report
Rovo leans on well-known AI models like OpenAI's GPT-4 and Anthropic's Claude to do the heavy lifting of planning the research and writing the final report.
Once it has gathered all the information, Rovo organizes it into a clear outline. From there, it writes the full report, which usually includes a quick summary, key findings, and maybe some suggestions. A really useful feature is that it backs up every major point with a citation that links directly to the source, whether that's a Jira ticket or a Confluence page. This makes it easy to double-check the facts and builds a bit of trust.
Where Rovo Deep Research really shines
Since its main skill is piecing together internal knowledge, Rovo Deep Research is most helpful for internal, big-picture tasks where you need to connect a lot of dots.
Bringing project knowledge together
Let's say a project manager needs to write a post-mortem for a big product release. Normally, this means spending days digging through Jira tickets, Slack DMs, and old meeting notes. Instead, they could ask Rovo: "What were the main blockers, key decisions, and results for Project Phoenix last quarter?" Rovo could then pull all that scattered info into a single, organized report.
Speeding up onboarding and research
A new developer joining the team needs to get up to speed on the "billing" system. They could spend a week reading through dense technical docs or bugging senior engineers. Or, they could ask Rovo to "create a report on the architecture, recent problems, and main contacts for the billing service." This gives them a detailed brief in minutes, which can seriously cut down their learning curve.
Finding insights in feedback
Imagine a product team trying to figure out what to build next. They want to know what customers complained about most in the last three months. They can ask Rovo: "Analyze customer feedback from Jira Service Management tickets and Slack support channels to find the top three recurring problems." The report would highlight the common themes, giving the team a data-backed starting point for their roadmap.
Where Rovo Deep Research falls short
Rovo is definitely useful for internal research, but it's important to know what it wasn't built for. This is especially true if you're on a customer support team.
A tool for internal research, not customer-facing automation
Rovo’s job is to create a report for a person to read. It's an analyst, not a support agent. It doesn't talk to customers or solve their problems in a live helpdesk.
For teams that need to automatically answer customer questions in real time, a dedicated AI support tool is a much better option. For instance, an AI agent from eesel AI plugs directly into helpdesks like Zendesk or Freshdesk. It can give instant answers, perform tasks, and resolve tickets without a human needing to step in.
Confined to the Atlassian world
Rovo gets its strength from being so tightly connected to Atlassian's Teamwork Graph. This is great if all your work and knowledge is already in Jira and Confluence. But if your company's information is spread out across lots of different tools, it won't be nearly as effective.
Teams that use a mix of apps need something more flexible. This is why a tool like eesel AI exists; it offers over 100 one-click integrations to connect to knowledge wherever it's stored, whether that’s in Google Docs, Notion, SharePoint, or right inside your helpdesk.
Delivers insights, not actions
Rovo is great at telling you "what" the situation is, but that's where it stops. It's still up to a person to figure out "now what?" The report is the final step in its process, leaving you to handle the next move.
Real support automation closes that loop by turning information into action. An AI agent from eesel AI can do more than just summarize an issue. You can set it up with custom actions to look up an order status in Shopify, change a ticket field in Zendesk, or send a problem to the right team. It connects the dots between knowing about a problem and actually solving it.
Atlassian Rovo Deep Research pricing and availability
Rovo Deep Research isn't a standalone product; it's part of the bigger Rovo AI assistant. Atlassian is gradually making it available to their Cloud customers, beginning with those on Premium and Enterprise plans.
How you pay for it is based on "AI credits," which are bundled into the monthly plans. Atlassian isn't strictly enforcing limits right now, but this kind of model can make your costs hard to predict. If you have a month with a lot of research requests, you might get a bill that's higher than you expected.
For teams who prefer to know what they're paying each month, other pricing structures can make more sense. For example, eesel AI’s pricing is based on a fixed number of AI interactions per month, and they don't charge you for every ticket that gets resolved. This makes budgeting a lot simpler and lets you grow without worrying that you'll be punished for handling more volume.
The takeaway on Rovo Deep Research: Choosing the right AI for the job
Look, Rovo Deep Research is a really strong tool for digging through your company's internal information. If your team lives and breathes the Atlassian suite, its knack for turning scattered data into organized reports can be a huge help for planning, project wrap-ups, and general research.
But if your main goal is to automate customer support, lower your ticket count, and make your agents' lives easier, it's not the right fit. When it comes down to it, Rovo gives you reports, but a real AI support platform gives you resolutions.
This is exactly where a tool built specifically for support, like eesel AI, comes into its own. It was made from day one for support automation. It connects with any helpdesk you use, can actually perform tasks on tickets, and even has a simulation mode so you can test everything out before going live.
Ready to go from Rovo Deep Research reports to resolutions?
Rovo Deep Research is great for getting answers inside your company, but you need a different tool to automatically act on those answers for your customers.
If you're looking to automate your support process and give customers instant resolutions, it might be time to check out what an AI agent designed for the job can do. You can get started with eesel AI in a few minutes and test it out on your team's actual ticket history.
Frequently asked questions
Rovo Deep Research acts as an AI research analyst that sifts through scattered internal company information. Its primary goal is to investigate open-ended questions and compile detailed, organized reports to provide a complete picture of a topic.
It leverages Atlassian's "Teamwork Graph" for context and researches in multiple rounds, breaking down queries into sub-questions. After gathering information, it uses AI models like GPT-4 to organize and write a comprehensive report with sources cited.
Rovo Deep Research excels at tasks requiring consolidation of internal knowledge, such as post-mortems for project releases, speeding up onboarding for new team members, or analyzing internal feedback for product insights. It helps connect dots across various internal documents and communications.
No, Rovo Deep Research is built for internal research and generating reports for human review, not for direct customer-facing automation. It does not interact with customers or resolve live support issues in a helpdesk environment.
Yes, Rovo Deep Research draws its strength from tight integration with Atlassian's Teamwork Graph. While it can connect to other apps, its effectiveness is highest when most of your company's knowledge is within Jira and Confluence.
Rovo Deep Research is included as part of the broader Rovo AI assistant, available to Atlassian Cloud Premium and Enterprise customers. Pricing is based on "AI credits," which are bundled into monthly plans, potentially leading to unpredictable costs if usage varies significantly.
Rovo Deep Research is designed to provide detailed reports and insights, effectively telling you "what" the situation is. However, it does not automate actions or perform tasks based on its findings; those next steps are left to the user.
