Project Galaxy
Your organization's knowledge, constellated. Galaxy connects all project artifacts in a 3D visualization that reveals hidden relationships.
What It Does
Project Galaxy creates a unified knowledge graph from all your project data:
Cross-Project Connections
See how tasks, risks, and documents relate across different projects.
Semantic Search
Find related entities using natural language queries.
AI-Discovered Edges
Machine learning identifies hidden relationships between entities.
Direct Knowledge Ingestion
Upload documents and create notes directly to Galaxy.
Entity Types
| Type | Color | Source |
|---|---|---|
| Projects | Terracotta | Canvas projects |
| Risks | Red | Risk nodes from canvas |
| Milestones | Blue | Milestone nodes from canvas |
| Documents | Gray | Uploaded files |
| Notes | Yellow | Quick notes |
Navigation Controls
Mouse Controls
- Left-click drag: Rotate view
- Right-click drag: Pan view
- Scroll wheel: Zoom in/out
- Click node: Select and view details
Keyboard Shortcuts
- L: Toggle labels
- E: Toggle edges
- Space: Pause/resume physics
- R: Reset camera
- ?: Show all shortcuts
Premium Features
Pro users can view Galaxy. Premium users get additional capabilities:
- Upload files: Add documents directly to the knowledge graph
- Create edges: Draw manual connections between entities
- Verify AI edges: Approve or reject AI-suggested relationships
- AI Insights: Pattern detection, risk cascade analysis, knowledge gaps
- Export: Download graph data as CSV or JSON
How It Works
Galaxy transforms your project artifacts into a searchable, interconnected knowledge base using several key technologies:
Semantic Embedding
Every entity's text (titles, descriptions, content) is converted into a high-dimensional vector that captures semantic meaning—not just keywords. This enables searches like “authentication delays” to find “login slowdown” or “credential latency” without exact word matches.
Relation Ontology
Edges between entities have typed relationships: “blocks,” “mitigates,” “relates_to,” “derives_from,” “triggers.” This structured ontology allows precise queries like “show all risks that block this milestone” or “what mitigates vendor risk?”
AI-Discovered Connections
Machine learning analyzes entity embeddings to suggest hidden relationships humans might miss. When the AI detects similar patterns (e.g., risks with similar root causes, tasks with overlapping scope), it proposes edges for your review. Verified edges strengthen the knowledge graph; rejected ones help train future suggestions.
Force-Directed Layout
The 3D visualization uses physics simulation to position nodes. Strongly connected entities cluster together; isolated nodes drift to the periphery. This creates an intuitive visual map where proximity implies relationship strength.
From Projects to Organizational Intelligence
Most project management tools store isolated artifacts. Galaxy transforms them into compounding organizational knowledge:
Before Galaxy
- • “Didn't we solve this problem before?”
- • Knowledge trapped in individual projects
- • Each new project starts from scratch
- • Cross-project dependencies invisible
With Galaxy
- • “Here are the 3 precedents with outcomes”
- • Every artifact enriches shared knowledge
- • Patterns and lessons surface automatically
- • Cross-project relationships visible instantly
Real-World Example
Three months into a project, you hit unexpected database migration delays. Galaxy search for “database migration delays” surfaces similar issues from two past projects, revealing what mitigation actually worked (splitting the migration into smaller batches) and what didn't (allocating more developers).
Best Practices
Use meaningful names for your canvas nodes—they become searchable entities in Galaxy.
Verify AI-discovered edges to train the system and improve future suggestions.
Create Quick Notes for lessons learned—they're automatically connected to relevant entities.
Tier Availability
Free
No access
Pro
View-only access
Premium
Full access + AI Insights