01
Review layer included
Knowledge tasks
Representative prompts, reference responses, and rubrics grounded in real roles.
AI in real workflows
Domain-grounded data for AI that has a job to do.
Training and evaluation programs shaped around business processes, proprietary knowledge, and real user expectations.
Signal map
Program outcomes
Domain relevance
Workflow reliability
Clear acceptance criteria
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
Copilot evaluation
Task-completion, groundedness, and usability studies for human-AI workflows.
03
Review layer included
Agent testing
Multi-step task suites covering planning, tool use, recovery, and escalation.
04
Review layer included
Governed operations
Access, handling, review, and delivery controls matched to project risk.
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
Map the workflow and risk
- 02Calibrate
Create representative task suites
- 03Produce
Evaluate with domain reviewers
- 04Learn
Improve against measurable gaps