Case Study

Treyspace

AI-Native canvas. Turns Excalidraw canvases into a queryable knowledge graph with semantic, relational, and spatial retrieval.

Outcomes

Impact

Over the course of 10 weeks, Built a canvas-to-graph pipeline for Excalidraw JSON data which adds custom fields and clusters suitable for retrieval. Implemen ted GraphRAG to ranks graph clusters relevant to user query.
Production deployment supporting concurrent collaborative sessions and real-time data syncing on Azure/Google Cloud.
Solves the problem of switching between LLM tab and whiteboard tab by integrating it in whilst retaining the crucial connections and spatial data that undermines the visual components.
Helps keep the whole canvas in context which creates connections that the user might not see.
Retrieval quality improved by combining semantic similarity with links and spatial grouping

Stack

Decisions

Treating the canvas as the source of truth produced a much better UX than flattening content into plain notes.
Clustering based on proximity plus explicit links consistently beat semantic-only grouping.
Auth and billing had to be integrated into the core product flow to support onboarding and team usage.

Approach

Excalidraw events stream into a canvas-to-graph pipeline that builds semantic, relational, and spatial clusters.
Entities are synced into graph/vector storage so retrieval can use structure and not just raw text.
AI endpoints stream responses with board-aware context and links back to source nodes.
Collaboration and access control are handled through Supabase authentication.
Paid plans and recurring billing are managed through Stripe flows.

Visuals

Links