01
Review layer included
SFT demonstrations
High-quality examples built to your task definitions, domains, and target behaviors.
Language and reasoning
Human data for models that reason, respond, and act.
Expert-authored and carefully reviewed data programs for supervised fine-tuning, preference optimization, reinforcement learning, and agent improvement.
Signal map
Program outcomes
Stronger domain reasoning
More useful model behavior
Clearer alignment signals
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
Preference data
Structured comparisons and rationales that capture nuanced quality differences.
03
Review layer included
Reasoning traces
Expert-developed tasks and solutions for complex analytical and technical domains.
04
Review layer included
Agent trajectories
Tool-use paths, failure labels, and recovery patterns for agentic workflows.
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
Task specification
A structured task set, rubric, edge-case policy, and worked examples.02 / Data
Reviewed data batches
Versioned examples with review outcomes, rationales, and adjudication where needed.03 / Insight
Quality readout
Acceptance evidence, error patterns, and recommendations for the next iteration.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
Define the capability and rubric
- 02Calibrate
Calibrate expert contributors
- 03Produce
Produce and review in controlled batches
- 04Learn
Analyze errors and improve the next cycle