Agent-Native Products. Built on Rust.

It means AI agents aren't an afterthought—they're first-class participants in the system architecture. We design products where agents handle workflows, make tool calls via MCP, and operate autonomously within guardrails. The infrastructure underneath is built in Rust for the performance and reliability that agent workloads require. Think less 'chatbot wrapper' and more 'agents as core workers in a production system.'

Rust and Axum on the backend. MCP servers for agent tool interfaces. PostgreSQL, TigerBeetle, and pgvector for data. HashiCorp stack (Terraform, Nomad, Vault, Consul) for infrastructure. SolidJS or React on the frontend. Claude and OpenAI for agent capabilities. We pick the right tools for each project, but Rust and agent tooling are always at the core.

We build products where agents play a central role—but that doesn't mean every feature has an LLM behind it. Most systems we build are a mix of deterministic Rust services, traditional APIs, and agent-driven workflows. We're pragmatic about where agents add real value versus where conventional engineering is the better tool.

We start by mapping which workflows agents should own and where the Rust infrastructure needs to sit. From there we move into iterative sprints with working software shipping every cycle. You get full visibility through shared boards, sprint demos, and direct access to your engineering lead. We offer both fixed-scope builds and ongoing monthly partnerships.

Agent workloads are bursty and unpredictable—an agent might fire off hundreds of tool calls in seconds. Rust gives us memory safety without garbage collection pauses, predictable latency, and the concurrency model to handle that kind of throughput. Python is fine for prototyping, but when agents are running production workflows, you want infrastructure that doesn't flinch.