Applied AI · Autonomous Systems
We build AI that
does the work.
Most AI still waits to be asked. We build the other kind — systems that watch, decide and act on their own, with the guardrails that make trusting them reasonable rather than reckless.
What we build
Four things, done properly, rather than a menu of everything.
Autonomous agents
Systems that decide and act, not chatbots that answer. Tool-using agents with real permissions, hard spend limits, and a human gate on anything irreversible.
Automation infrastructure
The unglamorous layer that makes autonomy safe: orchestration, scheduling, retries, audit trails, and cost accounting on every model call.
AI-native products
Products designed around machine intelligence from the first line, rather than a language model bolted onto an existing screen.
Production hardening
Migration, observability and security for systems that already carry real traffic. Uptime is a feature; so is knowing what broke and why.
Where this goes
Software has spent forty years getting better at waiting for instructions. The interesting shift now is software that does not wait — that reads its own telemetry, notices what changed, and acts before anyone has filed a ticket.
That shift is less about model quality than most people assume. The models are already good enough for a great deal of routine work. What has been missing is the boring scaffolding around them: permissions, cost ceilings, audit logs, rollback, and a clear line between what a machine may do alone and what it must ask about.
We build that scaffolding, and the systems that run on top of it.
Autonomy needs brakes
An agent allowed to act must also be constrained, logged and reversible. We classify every action by whether a human could undo it in ten seconds — and gate the rest.
Cheap models, used well
Most work does not need a frontier model. Routing the right task to the right tier is the difference between an experiment and something you can afford to run continuously.
Ship into production
We build on live systems under real load, not demos. The constraints that matter — latency, cost, failure modes, migrations — only appear once something is actually running.
Small teams, deep leverage
The interesting question is no longer how many people you can hire. It is how much a very small team can operate when the machines carry the routine.
Building something that should run itself?
We take on a small number of projects at a time — usually where autonomy, cost control, or production reliability is the hard part. Tell us what you're working on.
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