AI workflow quality checks
We build a review cycle around accepted work, human corrections, and failed handoffs. AI helps group recurring issues; a representative test set checks whether a proposed change addresses them without losing useful behavior elsewhere.
How we put it to work
Step 1 of 3
Find the repeated failure.
We collect permitted examples of accepted and corrected results, preserving the source and reviewer feedback. AI groups similar issues for investigation instead of treating every complaint as the same problem.
Your teamConfirms the issue categories and which examples represent real work.
Step 2 of 3
Compare on the same evidence.
We test the current and proposed workflow against the same agreed cases, including edge cases. Reviewers check factual support, completeness, and handoff behavior; cost and speed are assessed separately.
Your teamDefines acceptance criteria and judges whether the change meets them.
Step 3 of 3
Release with a way back.
We record the approved version and rollback steps, then monitor a limited rollout. New failures return to the review queue rather than being hidden by an average score.
Your teamApproves the release and decides when to revert.
What your first engagement includes
- 01Review your setupConfirm existing features, data access, and system compatibility.
- 02Configure one workflowConnect the tools, define approvals, and handle exceptions.
- 03Test and hand overPilot on selected work, train your staff, and provide operating instructions.
