AI agents that get work done,
inside your guardrails.

A chatbot answers a question, grounded in your documentation, one turn at a time, and escalates to a human when it's uncertain. An agent is given a goal, not a question, and works toward it across multiple steps. At each step it decides what to look up, which tool to call, and what to do next based on the result. A chatbot's job ends at a good answer. An agent's job is to plan and execute a sequence of actions, which is why it needs tighter permission scoping and approval gates, not looser ones.

Workflow automation runs one path you've already mapped, reliably, at volume (intake, triage, data entry) with a human checkpoint wherever that fixed path hits a judgement step. An agent has no single fixed path: given a goal and a set of tools, it decides at each step what to do next. The sequence can genuinely vary run to run. That flexibility is the point, but it's also why the guardrails are designed differently. You can't checkpoint "the judgement step" when the steps aren't fixed in advance, so the checkpoint sits on the class of action instead: anything irreversible or above a threshold, wherever it falls in the run.

Any language model can make a wrong call. Anyone promising an agent that never will is selling something. What we control is what a wrong call can actually do. Each tool call carries only the access that step needs, never a blanket key to your systems. Anything irreversible or above a threshold you set waits for a human's approval before it executes; and before the agent is trusted unsupervised, it runs against an evaluation suite scored against your own baseline. Every step and its reasoning is logged, so a wrong call is visible and traceable, not silent and compounding.

Your existing tools: that's the brief, not a constraint we work around. The agent calls the CRM, ERP, helpdesk, internal APIs and spreadsheets you already run through defined tool integrations, rather than requiring a migration first. We're deliberately model-agnostic too: the model underneath is chosen in discovery on fit, cost and data residency. The integration layer is structured so it can be swapped without rebuilding the agent around it.

Supervised, against real scenarios drawn from the actual task: not a synthetic benchmark, and deliberately including cases where the obvious next step is the wrong one. We measure whether it calls the right tool, whether it recognises when it doesn't have enough information to proceed, and whether it holds for approval when it should. Scores are measured against your own baseline, you see them before sign-off, and the suite re-runs whenever the tools or the underlying data change.

It depends on how many tools it needs to call and how tightly the permissions and approval rules need to be scoped, which is why we don't quote from a rate card. Every engagement opens with a scoped pilot: one real task, a working agent in 14 days, priced as a small, defined build. The proposal you receive within 48 hours itemises what ships: the tools wired, the guardrails and evaluation included, and what handover looks like. Extending an agent to further tasks or tools is priced only after the pilot has earned it against your own baseline.