Model response evaluation
Human review examples, preference comparisons, and error categorization against agreed rubrics.
Evaluation data
Make weaknesses easier to see.
Reliable evaluation begins with relevant tasks, representative scenarios, explicit outcomes, and a way to judge results consistently. We help source and structure the underlying examples.
01 / Real-world relevance
Test what your system is actually being asked to do.
A generic benchmark may overlook the specialized contexts in which your agent or model must operate. We focus on sourcing useful, task-specific evaluation material.
- Domain-specific test cases and difficult edge cases
- Expert-authored or reviewed examples where feasible
- Outcome-linked tasks and scenario metadata
- Rubric-ready samples and clear acceptance conventions
Evaluation use case / illustration
Can an AI agent resolve a real support incident—not just write an answer?
That evaluation may call for the initial issue, authorized environment context, action sequence, resolution evidence, and a defensible outcome definition.
02 / What we can scope◎ ↗ ⚑ ◈ ≡ ✓
Data for meaningful assessment.
Agent trajectories
Context, tool-use actions, checkpoints, and results for multistep task assessment.
Edge-case discovery
Underrepresented situations, exception paths, and stress-test data shaped by target behavior.
Domain review
Specialized expert inputs where buyers require evaluation beyond generic label conventions.
Dataset documentation
Task taxonomy, intended metrics, split conventions, lineage, and known limitations.
Pilot feedback loops
Review acceptance issues, adjust instructions, and target higher-value additional data.
Service availability is engagement-specific
We do not claim a proprietary evaluation platform or a standing team of specialized reviewers. For each project, we define the necessary experts, tools, security measures, and delivery roles before offering a scope.
Start a conversation
Discuss a data needBuild a more realistic test set.
Describe the performance gap and what success looks like; we’ll explore how to source the right examples.