Polar

Start with one bounded workflow that matches Polar's core job: AI analytics platform for ecommerce brands that unifies marketing and commerce data and lets teams query performance conversationally. Connect or upload only the systems and information needed for that task, then define the expected output, completion criteria, and any actions the AI is allowed to take.

Run the workflow on a small representative case first. Compare extracted facts, generated documents, decisions, calculations, or system actions with the underlying source material and the way an experienced human would handle the same task.

Before expanding usage, configure permissions, approval points, spending or action limits, audit logs, and escalation paths where the product supports them. For regulated or consequential workflows, keep qualified human review over final decisions and externally submitted work.

Once the workflow is reliable, monitor failures and corrections rather than only successful runs. Re-test after model, integration, policy, or source-data changes, and keep a record of cases where the system needs additional context or human intervention.