Design Graph
A Neo4j-backed digital thread for effectivity-aware design traceability across spacecraft and hardware programs.
- Nodes
- 148
- Relationships
- 295
- Stack
- Neo4j · FastAPI · Next
- Access
- Cloudflare Tunnel
Premise
Aerospace programs lose more value to broken traceability than to broken engineering. A requirement gets refined, a part number gets revised, a test result gets entered against the old part — and by the time anyone notices, the program has been quietly designing against two different versions of itself for six months.
Digital-thread tools exist (Teamcenter, Windchill, 3DEXPERIENCE) but they're heavy, expensive, and structured around large-team configuration management rather than around how a small engineering team actually thinks. The premise here is that a graph database with the right schema and a thin web interface can deliver the part of the digital thread that matters — effectivity-aware traceability — at two orders of magnitude less weight.
Architecture
A Neo4j graph holds the program: requirements, parts, assemblies, test campaigns, ICDs, decisions, and the relationships between them, all versioned by effectivity (which configuration each fact applies to) rather than by simple chronology.
- Graph
- Neo4j (Docker)
- API
- FastAPI · 127.0.0.1:8001
- UI
- Next.js · 127.0.0.1:3000
- Remote access
- Cloudflare Tunnel
The current seed is a mock Mammoth spacecraft architecture — 148 nodes and 295 relationships across requirements, subsystems, and verification artifacts. That's enough scale to exercise effectivity queries and verify the graph schema holds up under realistic project shape.
Current state
Working prototype, running locally and accessible through Cloudflare Tunnel. A documented AI data contract lets agents read and write to the graph through a constrained interface — relevant because the long-term play is to have design agents reason about the graph directly rather than scrape READMEs.
The first internal program to commit to using it as its digital thread is Space Kangaroo; integration there exercises the schema against a real, evolving design rather than a frozen seed.
What's next
Two tracks. Public-facing: seed a parallel instance with an Apollo-era
data set (or another program with abundant open material) and host it
at design.dauntless.systems as a portfolio piece. Private-facing:
move the working-graph instance to studio.dauntless.systems behind
Cloudflare Access so working designs stay private without giving up the
showcase.
∴⎯Related work