Business and Data Operations
Put trained human judgement around AI data and model outputs
Dedicated reviewers complete structured annotation, rubric-based evaluation, response comparison, and feedback tasks using your approved guidelines.
AI Data Operations
Why companies use this service
Add consistent human review to data and model-improvement workflows without presenting an unstructured pool of individual contractors.
A good fit when
- AI teams with repeatable annotation or evaluation queues
- Projects that require documented guidelines and reviewer feedback
- Operations that need supervised human review at a defined quality level
Measures we can report
- Items completed and turnaround time
- Agreement and quality-review rate
- Rework and exception rate
- Guideline questions and edge cases
- Output by reviewer and queue
Final measures depend on your systems, available data, and agreed scope.
What this can include
The final scope lists the exact tasks agents may complete, when they must escalate, and what must be recorded.
AI Data Operations in practice
- Data annotation and structured labelling
- Model-response evaluation
- Rubric-based scoring and comparison
- RLHF workflow support
- Domain-specific language-model training support
- Error categorisation and reviewer notes
- Quality sampling and disagreement review
- Continuous feedback queues
Before we quote
What NKCS needs to scope the work
- Current volume, backlog, coverage hours, and expected turnaround time
- The approved workflow, systems, access requirements, and escalation contacts
- Examples, training material, quality rules, and the measures you need reported
What would you like to do next?
Get a scope that your team can evaluate
Share the service, team size, hours, volume, systems, and preferred start date. We will respond with a clear view of fit and requirements.