Engineered for Entropy.
The Engineering Philosophy
We don't believe in "prompt engineering" as a substitute for systems engineering. We build robust, type-safe infrastructure.
Rust & Go Core
Performance is a feature. We write our sidecars and data planes in Rust and Go to ensure millisecond-latency overhead and memory safety at the edge.
Deterministic by Default
We treat non-determinism as a bug. Our replay engine ensures that every agent state can be reconstructed bit-for-bit, regardless of the underlying model.
Kubernetes Native
We do not own workflow execution. RADAR integrates with existing infrastructure and evidence pipelines so operators can review what agents did.
Tools of the Trade.
Evidence Core
Network Proxy
SDK & UI
Model Serving
Deployment
Communication
Telemetry
Messaging
State
IaC
From the Logbook.
Technical deep dives into the challenges of AI compliance evidence, observability, and security.
The Case for Local Inference
Why we moved our core reasoning loop from GPT-4 to fine-tuned Llama 3 on-prem.
Semantic Tracing at 10k TPS
Building a high-throughput telemetry pipeline with ClickHouse and NATS.
Sandboxing Python Interpreters
Using gVisor and Firecracker to safely execute untrusted agent code.
Build with us.
We are looking for systems engineers who want to solve the hardest problems in AI infrastructure.