We build the backbone — event pipelines, data platforms, and agentic orchestration — with the rigor of teams who've run them at scale. Correct, observable, and fast.
Type-safe pipelines in Rust and Scala. The compiler catches what tests miss.
Tracing, metrics, and structured logs wired in from the first commit, not bolted on.
Single-digit-millisecond p99s held under real production load, not benchmark theatre.
Streaming and batch over Kafka and Spark — petabyte pipelines that stay debuggable.
Reliable multi-agent systems with retries, backpressure, and clear failure modes.
Clean, documented code and a real handover. No black boxes, no lock-in.
Rust where correctness and latency matter. Scala for data at scale. Go for services that need to ship and stay simple. We pick deliberately and document why.
async fn dispatch(req: Request) -> Result<Response> {
let route = router.match(&req)?;
route.handle(req).await
}def pipeline(events: Stream[Event]) =
events
.window(5.minutes)
.aggregate(Sum)
.sinkTo(warehouse)func Serve(ctx context.Context) error {
srv := grpc.NewServer()
pb.RegisterRouter(srv, &router{})
return srv.Serve(lis)
}gRPC / event-driven services in Rust, Go, and Java built for failure.
Streaming + batch pipelines, lakehouse, and analytics infrastructure.
Orchestration, tool-use, and evaluation harnesses for production agents.
Observability, SLOs, and the on-call you can actually sleep through.
Tell our AI agent what you're building. We'll reply with how we'd approach it—plainly.