We’ve been experimenting with a new database architecture for real-time AI, robotics, and agent workloads. The prototype in this 90-second demo shows a persistent graph engine that:
delivers 0.12–0.4 µs hot-path reads
stores 50M durable nodes on an 8GB Jetson Orin Nano
streams a 40–50GB graph from NVMe as if it were RAM
maintains ACID guarantees under power-fail/crash tests
uses a hardware-native concurrency lattice instead of locks or B-trees
The goal wasn’t to optimise an existing database, but to test whether durable sub-microsecond access was even possible without keeping the whole graph in memory.
We’re a very early startup exploring this space and would genuinely appreciate critical feedback, collaboration, and technical pushback from people who’ve worked on database internals, kernels, robotics systems, or high-performance storage.
If you see flaws, edge cases, missing failure modes, or things that “shouldn’t work,” we’d love to hear from you.
delivers 0.12–0.4 µs hot-path reads
stores 50M durable nodes on an 8GB Jetson Orin Nano
streams a 40–50GB graph from NVMe as if it were RAM
maintains ACID guarantees under power-fail/crash tests
uses a hardware-native concurrency lattice instead of locks or B-trees
The goal wasn’t to optimise an existing database, but to test whether durable sub-microsecond access was even possible without keeping the whole graph in memory.
We’re a very early startup exploring this space and would genuinely appreciate critical feedback, collaboration, and technical pushback from people who’ve worked on database internals, kernels, robotics systems, or high-performance storage.
If you see flaws, edge cases, missing failure modes, or things that “shouldn’t work,” we’d love to hear from you.