Engineered for Entropy.
We are building self-hosted compliance infrastructure for a probabilistic world. Join us in defining how regulated AI teams prove what happened.
use akios_pro::evidence::{Collector, EvidencePolicy};
use akios_pro::signals::{LlmCall, ToolCall};
#[tokio::main]
async fn main() -> Result<(), Error> {
let collector = Collector::self_hosted()
.policy(EvidencePolicy::eu_ai_act())
.retain_locally("90d")
.export_to_siem("splunk")
.build()?;
collector.record(LlmCall::new("customer-support"));
collector.record(ToolCall::new("crm.lookup"));
collector.flush().await?;
Ok(())
}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.
Rust
Evidence Core
Go
Network Proxy
TypeScript
SDK & UI
Python
Model Serving
Kubernetes
Deployment
gRPC
Communication
ClickHouse
Telemetry
NATS
Messaging
Postgres
State
Terraform
IaC
From the Logbook.
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.