Multi-agent systems that carry a process from start to finish, with approvals built in.

Cost depends on how many steps and systems the process covers, whether agents write data or only read it, and how many approval gates and test cases the work needs. Most projects are a fixed-price build and pilot, followed by a monthly fee for monitoring and phased rollout. Model usage is billed at cost and tracked per run. You'll get a written scope after a free call.

When the process has distinct stages, such as gathering facts, applying rules, taking action and checking the result, that need different tools or permissions. Splitting them makes each agent easier to test and to limit. If one agent or a fixed automation can do the job, we'll recommend that instead, because it's cheaper to run and simpler to maintain.

Most stall because the scope is too wide, success was never defined and nobody trusts what the agent did. We start with one slice of the process, write success criteria first, score every release against real cases and keep people approving sensitive actions. Autonomy grows only where the numbers support it.

Yes, but only on steps you've agreed are low-risk and that have passed review over time. Anything that moves money, contacts a customer or changes an important record can require a person's sign-off, and you can tighten or loosen each gate later. Every action is logged either way.

Every run leaves an audit trail: each step, the agent that took it, the data it read, the tools it called, what it changed and who approved it. Your team can search those records, and we use them to find and fix failures.

Each run has a budget, a step limit and a time limit, and agents stop and escalate when they hit one. We pick the smallest model that passes the tests for each role and alert on cost and latency spikes. You see cost per completed run in the monthly report.

Your data stays out of model training. We use business API terms under which providers don't train on your inputs or outputs. Each agent gets only the data and tools its role needs, and credentials sit in a secrets manager, never in prompts. Where your industry's rules call for it, models can run with in-region hosting or inside your own cloud.

A process owner who knows how the work runs today, access to the systems involved and a set of real past cases to test against. During the pilot, a few reviewers approve or correct the agents' actions each day. Their decisions tell us where autonomy can safely grow.