From Chaos to Clarity: MDLR for Collaborative Work
24 Nov 2024 • 4 min read
Use case 2: For Collaborative Work
When it comes to collaboration, comments are everywhere. Whether you’re working on 2D/3D design canvases, brainstorming on whiteboards, or managing projects on platforms that offer task management like Jira, comments are the lifeblood of teamwork. But let’s be honest: without structure, they can quickly spiral into a mess of untraceable threads, forgotten ideas, and unanswered questions.
That’s why we built MDLR—a framework designed to turn this chaos into actionable insights. While in Use Case 1 we explored how MDLR helps organize personal notes, here we focus on collaborative work, where the stakes (and the mess) are much higher.
The Problem with Comments
In collaborative projects, comments are everywhere:
- On 2D/3D canvases: Think design reviews, where feedback is pinned to models or drawings.
- In project management tools: Platforms like Monday and Jira are packed with task-specific comments that are difficult to summarize.
- Across brainstorming sessions: Comments on shared whiteboards or documents often lack focus or follow-through.
These comments are valuable, but they’re also overwhelming. Teams spend hours manually combing through feedback, trying to connect the dots and make sense of it all.
How MDLR Makes It Better
MDLR transforms unstructured feedback into ongoing, actionable summaries. Here’s how it works:
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Works with Any Canvas
Whether it’s a 2D drawing, a 3D model, or a brainstorming board, MDLR integrates with your tools to capture comments directly. Imagine reviewing a 3D design and generating a summary of all feedback related to door alignment issues—in seconds. -
Real-Time Summaries on Demand
Want a summary of all comments related to a specific topic? Just set a prompt. For example:- “What are the unresolved issues with the electrical plan?”
- “Summarize feedback on user onboarding improvements.”
MDLR scans the comments, analyzes patterns, and provides insights dynamically. It’s like having an AI assistant that’s always listening.
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Tailored to Your Criteria
Comments are filtered and summarized based on your specific criteria: urgency, topic, assignee, or even sentiment. Whether you’re prioritizing tasks or reviewing a project’s progress, MDLR gives you the clarity you need.
Why This Matters
Traditional tools rely on manual effort to organize and summarize feedback. MDLR flips this dynamic. Instead of teams spending hours extracting insights, the framework does it for you, offering:
- Better Alignment: Everyone stays on the same page with consistent, AI-driven summaries.
- Improved Efficiency: Spend less time sifting through comments and more time acting on them.
- Enhanced Collaboration: Feedback becomes clearer and more actionable, improving team communication.
Collaborative Work, Simplified
MDLR is a tool designed to help teams organize feedback and make collaboration easier. Whether you’re reviewing designs or managing tasks, it fits into your workflow, ensuring comments contribute to actionable outcomes.
With MDLR, you can move beyond scattered feedback and focus on progress. Ongoing summaries give you the clarity to act confidently.
We’re just getting started—stay tuned as we continue improving and expanding MDLR.