Evaluation datasets
Realistic product tasks with expected behavior, scoring guides, what-if variants, and failure analysis.
Human data for software agents
Better software agents.
Built on human judgment.
We turn real feature requests, customer promises, launches, and roadmap tradeoffs into expert-built training data and evaluations.
Product judgment / evaluation design
A model can write the feature.
Does it know whether
it should?
01
Find missing context
Separate stated facts from assumptions.
02
Choose the right source
Inspect existing behavior before inventing it.
03
Preserve decision rights
Know which commitments need human approval.
04
Adapt to new evidence
Change the route when a key fact changes.
Four dimensions to evaluate in a focused product pilot.
01
Grounded in real work
Built from situations software teams have actually handled
02
Reviewed across functions
Product judgment checked across engineering, solutions, and product operations
03
Structured for research
Task inputs, action boundaries, variants, and scoring stay inspectable
What you receive
Everything needed to test the agent’s next move.
Every part stays separate and traceable, so your team can inspect the source, the expected behavior, and where a model failed.
Training examples
Expert-written examples that show how to find missing information and take a safe next step.
Preference data
Paired approaches and expert feedback that separate strong judgment from confident but unsafe behavior.
Interactive tasks
Controlled tasks for evaluating tool use and multi-step work after the core evaluation is proven.
Inside every task
Designed to expose shallow reasoning.
An answer alone is not enough. The agent must find what is missing, choose the right source, respect authority, and stop before an unsafe commitment.
Starting context
What the agent actually knows at the start
Unknowns
Facts that could change the decision
Sources
Systems, people, and approval owners
Boundaries
Safe actions, escalation points, and stops
Rubric
Strong behavior, weak reasoning, and critical failures
Quality & privacy
Private by design. Traceable by default.
Experts are asked to remove identifying details before submitting product-work examples. Standard exports exclude expert identity and raw narrative. Each task retains its source and review history.
Identifying details removed
Cross-functional review
Time-order checked
Source lineage retained
Purpose-bound exports
Export audit history
Private pilot
Start with the behavior you need to improve.
Tell us where your agent fails today. We’ll define a narrow workflow, review representative tasks, and agree on what useful results should prove.
- 01Share the target workflow
- 02Review sample tasks
- 03Agree on pilot scope
- 04Measure the result
We agree on scope, data permissions, and delivery terms before a pilot begins.