Data Labeling & Annotation
Our data pipeline supports labeled datasets where a production workload requires explicit examples for classification, extraction, ranking, evaluation, or model adaptation.
When it is used
Labeling is not a default preprocessing step. We use it when the task benefits from a defined target and the expected improvement justifies the cost of creating and maintaining reliable labels.
Label sources
Depending on the workload, annotation can combine:
- Human labeling
- Subject-matter expert review
- Model-assisted labeling
- Programmatic labeling
- Weak supervision
- Consensus review
- Quality-control sampling
The objective is not maximum manual annotation. It is sufficiently reliable ground truth for the decision the dataset must support.
Model-assisted labeling can increase throughput, but generated labels are not automatically ground truth. They can reproduce the same model errors the dataset is supposed to measure or correct. Quality-control sampling and independent review become more important as automation increases.
Label design
Many annotation failures originate in the definition of the label rather than the annotator.
If reviewers cannot consistently distinguish two classes, the taxonomy may be underspecified. If one class dominates the dataset, overall accuracy can conceal poor minority-class behavior. If operating definitions change, historical labels can become stale even when they were correct when created.
We therefore treat label design as part of the system:
- Task definition
- Label schema
- Annotation guidance
- Review / consensus
- Quality sampling
- Dataset version
Relevant failure modes include reviewer disagreement, class imbalance, noisy labels, concept drift, inconsistent annotation, and leakage between training and evaluation data.
Training and evaluation are different purposes
A labeled example used to optimize a model should not automatically be reused as evidence that the optimized model works.
Where a workload requires both adaptation and evaluation, dataset separation matters. Otherwise the system can measure memorization or contamination instead of generalization.
Not every workload requires labeled data. Retrieval, deterministic workflows, exact calculations, and many operating integrations can work without it.
We use annotation where it creates measurable value — not because a modern AI stack is expected to contain a labeling project.