AI workflow automation
that is reliable by design.
Most of what teams want from AI is a workflow: a defined process with one or two model calls in the right places, automated end to end. We build those, with the evals, guardrails, and cost ceilings that make them dependable, then hand them over. Often it is the cheaper, safer answer to a problem someone framed as an agent.
The dependable default
A deterministic workflow with a model call where it adds value is predictable, testable, and cheap. For the majority of business automation, it beats an agent on every axis that matters in production: cost, latency, and the number of ways it can surprise you.
We reach for an agent only when a task genuinely needs to plan and adapt over many steps. For everything else, a well-designed workflow is the honest answer, and we will say so before building anything.
Automation wired into your systems
We build the full workflow: the triggers, the steps, the model calls, and the integrations to your tools, data, and APIs that turn it from a demo into something that does real work inside your stack.
Every workflow ships with an eval set that scores quality on each change, guardrails on inputs and outputs, and a hard cost ceiling so automation cannot quietly run up a bill.
Operated, then yours
Automation needs operating: monitoring, alerting, and a human in the loop where the stakes require it. We run yours during a support window, then hand it over with runbooks and the code in your repositories.
This is the Build & Run offer applied to workflow automation: code in your repos from day one, IP transfer in the contract, and a team that can keep it running after we step back.
Common questions.
Direct answers to the questions we get asked the most. If yours isn't covered, write to the team.
Automate the work with a workflow that holds.
Tell us the process. We will tell you honestly whether it needs an agent or a workflow, then build and run the right one.