Rubric evaluation
Score outputs against observable quality, policy and domain criteria.
Turn real work into evidence your AI team can act on.
Define the workflow, capture expert standards and test models or agents against representative cases. DataM8 makes failures reviewable and corrections reusable across releases.
EVALUATION PROGRAMS
Evaluate the work, not just the model
Combine the right evaluation modes around an operational outcome, its risks and the evidence required to trust it.
Expert comparison
Use qualified reviewers to compare alternatives and explain preference.
Safety red teaming
Probe risky, adversarial and high-impact failure modes with controlled scope.
Agent trajectory review
Evaluate planning, tool selection, recovery and outcome across multiple steps.
FROM QUESTION TO DECISION
A controlled evaluation workflow
Define the business decision first, establish an expert baseline and only scale after disagreement, failure severity and edge cases are understood.
- 1Map the workflow, outcome, evidence and risk
- 2Build representative cases and an expert baseline
- 3Set versioned rubrics and critical-failure thresholds
- 4Compare agents, models or configurations
- 5Turn meaningful corrections into regression cases
Start bounded. Build an asset that compounds.
Your first workflow produces reusable cases, rubrics, evidence and release criteria—not a one-off score or report.
Create workflow workspace