How we deploy AI agents in six steps

At each step: what we do and what gets defined by the end.

01

Maturity and priorities assessment

We map how AI is already used in the company today, identify candidate processes, and define the result the first use case should produce. We start with the processes that, by the team's own account, take the most time, cause the most errors, or place the greatest constraints on operations.

02

Governance, rules, and responsibilities

We define which sources AI can consult, which actions it can take, who is responsible for each decision, when a person needs to step in, and how exceptions will be handled.

03

Architecture and infrastructure

We design the environments, data, integrations, and controls needed to build and operate the solution.

04

Operating model for teams and agents

We define how tasks are divided between people and agents, along with the review checkpoints, records, and procedures for failures and exceptions.

05

Assisted deployment and training

We put the first use case into operation, monitor how it performs, and prepare the people responsible for operating and reviewing it.

06

Monitoring and operational autonomy

We monitor performance, adjust rules and routines, and progressively hand responsibilities over to the team. The degree of autonomy depends on the project, its risks, and the capabilities built during deployment.