01
Review layer included
Custom benchmarks
Representative tasks, reference answers, and scoring systems matched to your use case.
Measure what matters
Evaluation systems built around real model behavior.
Custom benchmarks, human evaluations, and adversarial testing that reveal capability gaps before they become production issues.
Signal map
Program outcomes
Decision-ready evidence
Actionable error taxonomies
Repeatable release gates
Inside the work
Human expertise, connected to a controlled data workflow.
Every engagement links the people doing the work, the evidence used to review it, and the model behavior the program is meant to improve.
Capabilities
Built to fit the model, domain, and decisions behind your program.
02
Review layer included
Human evaluation
Calibrated reviewers assess accuracy, usefulness, style, safety, and task completion.
03
Review layer included
Red teaming
Structured adversarial testing across misuse, edge cases, and policy boundaries.
04
Review layer included
Production monitoring
Sampling and review programs that track behavior after launch.
What gets delivered
More than a dataset: a usable package of data, controls, and learning.
The exact artifacts change by program, but every delivery is designed to be inspectable, actionable, and ready for the next model decision.
01 / Design
Program blueprint
Workflow architecture, roles, acceptance gates, and delivery plan.02 / Control
Operational scorecard
Quality, rework, issue, and delivery signals organized for clear oversight.03 / Insight
Decision package
Findings, error taxonomy, and recommended actions for the next release or batch.Delivery model
Designed for fast learning and controlled scale.
Programs move in visible stages, with a review point before scope, volume, or complexity increases.
- 01Frame
Translate risks into testable criteria
- 02Calibrate
Build representative evaluation sets
- 03Produce
Run blinded, calibrated reviews
- 04Learn
Turn findings into targeted model work