Methodology
How we evaluate AI systems
A repeatable process that turns open questions about model quality into evidence your team can act on.
Process
Five stages, every engagement
- 01
Define
Agree on the decision you need to make, the use cases in scope, risk areas, success criteria and the data we may access.
- 02
Design
Build test sets, golden sets or task suites and write a scoring rubric with clear definitions and worked examples.
- 03
Calibrate
Evaluators score a shared pilot set. We compare results, resolve disagreements and refine the rubric before full runs.
- 04
Execute
Run human, comparative or adversarial evaluation with ongoing sampled review and agreement monitoring.
- 05
Deliver
Report findings by failure category with evidence, severity and prioritized recommendations — plus the underlying data.
Quality controls
How we keep results reliable
Rubric design
Each score level has a written definition and examples so results mean the same thing across evaluators.
Evaluator calibration
Evaluators are trained and checked on calibration sets before they score live work.
Reviewer agreement
Overlap samples are scored by more than one person; disagreements are adjudicated and tracked.
Quality checkpoints
Sampled second-pass review at defined points, with acceptance criteria agreed upfront.
Traceable findings
Every finding links back to the inputs, outputs and scores that support it.
Confidentiality
NDA-ready scoping; access is limited to the people working on your project.
Have an AI project?
Send us what you are building. We will scope the data, evaluation, benchmarking or testing program and come back with a proposal.