CONTEXT DAG
Non-linear AI workspace. Every branch preserved. Every insight connected.
Linear chat interfaces force one train of thought and lose context in long sessions. We built a DAG-based AI workspace where every exchange is a node — infinitely branchable, fully persistent, cross-linked. Users explore 10x more ideas per session and never restart a conversation from zero.
AI Tools Are Brilliant. Their Interfaces Are Not.
The fundamental problem with AI chat interfaces is architectural: they are linear. One conversation, one thread, one direction. When a thought is worth branching — "what if we tried this differently?" — users either abandon the original thread or start an entirely new session. Context degrades in long conversations. Sessions cannot be resumed days later. Insights from one conversation cannot be connected to another. The AI is capable of far more than the interface allows.
The Product Team Kept Hitting the Same Wall.
A SaaS product team was using AI heavily for product research, feature ideation, and competitive analysis — but hitting a structural ceiling. Long research sessions degraded. Promising branches got abandoned when they started new directions. Insights from Tuesday's session were disconnected from Thursday's. The team was spending more time managing the AI than using it.
Context Degradation in Long Sessions
After 30-40 exchanges, the AI "forgot" early context. Users had to re-paste documents and restate constraints. Research sessions that should build toward insights were resetting.
No Branching — No Exploration
Exploring "what if we built this feature differently?" meant starting a new chat and losing the original thread. Teams were choosing one direction too early rather than exploring alternatives in parallel.
Sessions Couldn't Be Resumed
Closing the browser ended the session. Coming back meant pasting conversation history manually — tedious enough that most users just started over, losing hours of context.
No Cross-Session Connections
The insight from Monday's competitive research couldn't be referenced in Wednesday's feature design session. Everything was siloed in separate, unconnected chat windows.
A Workspace Where Every Thought Is a Node
We built a graph-based AI workspace where every AI exchange creates a node in a persistent DAG. Nodes can be branched — explore a different angle without losing the original. Branches can be connected — draw relationships between insights from different sessions. Everything persists indefinitely and can be resumed, shared, or built upon.
DAG-Based Conversation Architecture
Built on React Flow, every AI exchange is a visual node. Branch from any node to explore alternatives. Merge branches to synthesize insights. The graph becomes a map of your thinking.
Cross-Node Memory
Concepts from one branch inform others. Reference a specific node from a previous session in a new conversation. The AI understands the full graph context, not just the current thread.
Infinite Session Persistence
Sessions are stored in Redis with PostgreSQL graph metadata. Resume any branch from any device, days or weeks later. Share branches with team members. Collaborate on the same AI research graph.
Multi-Model Support
Switch between OpenAI and Claude within the same session — use GPT-4 for code, Claude for analysis. Each model's responses are distinct nodes with different visual styling.
BUILT WITH THE
RIGHT TOOLS.
The Team Stopped Fighting the Tool. They Started Using It.
LET'S BUILD
YOURS.
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