How it works
You define the capability. We run the data engine.
The six stages
What we run at each stage, and what we need from you.
The same operating system runs every project, whichever product it sits under.
Scope
We define the task, the expertise it demands and the quality bar it has to clear.
Scoping is a working session, not a form. Before any contributor is recruited we agree the task, the expert profile, the quality criteria, volumes and timeline, so everything after it is measured against something written down.
From you
Your task and what a good result looks like. Real examples if you have them.
Source
We recruit and qualify the exact contributor pool the project requires.
Expert fields we recruit forContributors are recruited against the specific expertise a project needs, then assessed with project-relevant tests. We qualify people for capability, not availability. Screening protects the quality bar before any work is produced.
From you
Confirm the expert profile that was agreed at scope.
Calibrate
Contributors train against your rubrics and gold-standard tasks before producing anything.
How calibration worksEvery contributor is trained on your guidelines, examples and edge cases, then has to clear gold-standard tasks measured against your rubric. Calibration shows who meets the bar and where re-training is needed. No one enters production unproven on the actual standard.
From you
Your rubric and guidelines, and answers to edge-case questions as they come up.
Produce
Data is generated and evaluated through structured workflows with full traceability.
How your data is handledWork runs through structured task queues. Every unit is attributable to a contributor, a task version and a review chain, so results are auditable and any piece of output can be traced back through how it was produced and checked.
From you
Review the first batch of real output against the criteria agreed at scope.
Quality control
Multi-level review, adjudication and continuous contributor scoring keep the bar steady.
How review and adjudication workDeliverables move through layered review. Disagreements and edge cases are escalated to senior adjudication rather than left to a single pass, so the hard calls get the second and third look they require.
From you
Agree how disagreements are resolved. The quality dimensions are reported to you as work proceeds.
Scale
Qualified capacity expands on demand — throughput grows without compromising quality.
Contributors are scored continuously on quality and reliability. Underperformers are recalibrated or removed; high performers are expanded. Capacity planning happens around qualified people, so throughput grows while the bar stays fixed.
From you
Decide whether to expand, once the standard has held on your work.
The data engine
Every unit of work is traceable end to end.
- 01ExpertQualified against the expertise the task demands.
- 02TaskScoped with a defined quality bar before any work starts.
- 03ResponseProduced through structured, traceable task queues.
- 04AnnotationLabelled against your rubric by calibrated reviewers.
- 05QualityMulti-level review; disagreements escalate to adjudication.
- 06DatasetAuditable output, attributable back through the chain.
Stage 1 of 6: Expert.
Where it applies
One engine under four products.
Each product is a way of pointing these stages at a different problem. Start from the one closest to yours.
Next step