Confluence Heatmap

Confluence lacks visibility into comment activity. Our heatmap UI surfaces comment volume and patterns, enabling teams to quickly identify, prioritize, and resolve the most important discussions.

Comment

Inspiration

Confluence is great at capturing discussions, but comments quickly become fragmented and hard to manage at scale. On pages with heavy collaboration, it’s difficult to see where discussions are concentrated, which comments matter most, or which areas need attention.

While working on large, high-impact projects, I noticed that teams often miss important feedback simply because comment visibility is limited to linear lists. There’s no quick way to understand comment activity at a glance. This inspired me to rethink how comment data could be surfaced more visually and intuitively.

What it does

Confluence Comment Heatmap makes comment activity visible at a glance. It overlays a heatmap UI on Confluence pages to show where discussions are concentrated, helping teams quickly identify high-impact sections and prioritize what needs attention.

How we built it

We built the project using Atlassian Forge, integrating directly with Confluence APIs to retrieve comment metadata and activity signals. These signals are aggregated and rendered into a heatmap-style visualization that fits naturally into the Confluence experience.

The UI was designed to be lightweight, intuitive, and non-intrusive, ensuring users gain insight without disrupting their existing workflow.

Challenges we ran into

One of the main challenges was extracting meaningful insights from comment data and translating them into intuitive UI behavior. Beyond aggregating statistics, we needed to accurately map comments back to their corresponding sections on the page.

Implementing interactive behavior—such as clicking a bar in the chart and smoothly scrolling to the exact section where comments were made—required careful coordination between comment metadata, page structure, and UI state. Ensuring this interaction felt fast, precise, and reliable within Forge’s constraints was a key technical challenge.

Accomplishments that we're proud of

  • Built a native Confluence experience using Forge
  • Created an intuitive heatmap visualization for comment activity
  • Improved comment discoverability and prioritization
  • Designed a solution that scales to pages with heavy collaboration

What we learned

We learned how to effectively visualize collaboration data to support better decision-making. We also gained hands-on experience building within the Atlassian ecosystem and designing UI that complements existing tools rather than competing with them.

What's next for Confluence Comment Heatmap

Next, we plan to introduce:

  • Version-history–based filters
  • Enhanced comment sentiment analysis with a more nuanced color range
  • Deeper cross-space and team-level analytics powered by improved algorithms—including AI-driven insights.

Longer term, our goal is to move beyond showing where discussions happen to helping teams understand why they matter, with AI features that summarize conversations and actively assist users in engaging more effectively within comments.

Share this project:

Updates

posted an update —

Dear Atlassian Judges,

My name is Thao, and I’m one of the developers behind this app. Thank you for giving us the opportunity to share our pain points as Atlassian users and to showcase our idea. We have learnt so much throughout this hackathon.

We fully understand that our developing app, in its current state, has limitations given the very limited time span to learn everything about the Atlassian Forge platform from scratch. Our main goal was to deliver a working solution that clearly demonstrates how we can better works with comments. As long‑time Confluence users (personally, I’ve been using Confluence for years), we genuinely believe our Heatmap hackathon idea can benefit many of your customers.

Our original plan was to integrate AI capabilities (Rovo) so that the most important sections could be identified through intelligent analysis rather than a simple comment‑count–based approach. However, at the moment, Rovo APIs, SDKs, and modules are still quite limited within the Confluence full‑page module, which constrained what we could realistically implement. However, we strongly believe rovo implementation is an area that will continue to evolve, and that deeper Rovo integration will become much easier and more powerful in the future for developers' applications.

While the idea is still raw, we believe it is both practical and impactful. We hope the Atlassian team can see the potential here, and how powerful this could be if it becomes a core feature within Confluence.

Thank you again for your time and consideration. I hope everyone is enjoying quality time with their loved ones this festive season.

Best regards, Thao (on behalf of the whole team)

Log in or sign up for Devpost to join the conversation.