Turn AI ideas into real business results.

Work with a reversible outcome and a clear definition of done — drafting, gathering, routing, preparing something for approval. The moment an action moves money, contacts a customer or changes a record of consequence, we put a person in front of it. The question is never what the agent can do, but what it can undo.

By limiting what it can reach. An agent gets its own credentials with the narrowest permissions the task needs, every tool call is logged, and anything irreversible waits for approval. We also treat retrieved content as untrusted input, because a document telling an agent to ignore its instructions is a real attack we have to design against.

It stops and hands over, which sounds obvious and is where most agent systems fail. We cap the steps and the spend per task, detect the loop where an agent retries the same failing action, and escalate with the full trace so a person can see what it was attempting. Silent failure is the outcome we design hardest against.

Reliable on narrow, well-defined work and unreliable on open-ended goals, which is why we scope agents tightly. A pipeline of small deterministic steps with an agent at the judgement points is far more dependable than one agent given a broad instruction, and it is much easier to test and to debug when it goes wrong.

Yes, through the same APIs your staff use, with the same permissions model. Where a system has no API, an agent is usually the wrong answer and an integration is the right one. We map what can actually be reached before designing the agent, because that constraint decides what is possible.

Every run is traced end to end — what it was asked, what it retrieved, which tools it called, what it produced. That trace is the audit record and the debugging tool, and it is why we build it before the agent does anything useful. An agent you cannot inspect is one you cannot improve.