Data Annotation
Create training labels that people can agree on.
More labels do not help if different reviewers mean different things by them. We structure annotation around the model’s task, write usable labeling rules and review agreement before scaling the work. The dataset arrives with its definitions and quality record.
DIRECT ANSWER
Data annotation creates reviewed labels for training and evaluating models. The engagement covers label definitions, a calibration sample, annotation workflow, quality checks and a versioned dataset with documented limitations.
Best for
- Teams preparing text, image, audio or video examples for a defined model task
- Owners whose existing labels are inconsistent or lack review evidence
Consider another approach
- A general warehouse or business reporting pipeline
- Unspecified data collection without a defined use or permission basis
Make the label useful for the downstream task
We define what each label means, when it applies and which cases should remain uncertain. A text classifier may need whole-document categories; a vision model may need boxes, regions or image-level labels. Granularity must match the model and the decision it supports. Machine Learning or Computer Vision can establish that target first.
Resolve disagreement while the sample is small
Reviewers label an agreed calibration sample using the same instructions. We inspect disagreement, improve definitions and record adjudication decisions before increasing volume. Rare and ambiguous cases receive explicit examples. Domain judgments remain with qualified subject owners; an annotation instruction cannot replace expertise that the task actually requires.
Choose the tool and review pattern deliberately
The workflow can use model-assisted suggestions, human labeling or a combination. Suggestions are identified as such and reviewed under the agreed policy. Access, storage, redaction and export formats are settled before work starts. We confirm language and domain coverage for the actual dataset rather than assuming one reviewer profile suits every collection.
Illustrative acceptance: overlapping support categories
An example dataset contains messages about a late delivery and a refund request in the same text. Reviewers initially choose different single labels. The team agrees a multi-label rule, updates the rubric and rechecks affected examples. The delivered dataset identifies the rule version and quality sample, so a future model evaluation uses the intended meaning.
Preserve the history of the dataset
Deliverables include the label schema, instructions, dataset version, review outcomes and a list of excluded or unresolved cases. Training and evaluation partitions are separated according to the agreed sampling design. Corrections are versioned, and personal or licensed source material follows the accepted retention policy. LLM evaluation datasets can continue under AI Evaluation & LLMOps.
Questions before you start
We choose the combination after reviewing the task and samples. Automated suggestions can speed up work, but the acceptance policy specifies what must be reviewed and how quality is measured.
Yes. A sample audit identifies unclear categories, contradictory labels and coverage gaps. We agree which records need correction and preserve the previous version for comparison.
The scope names the required domain reviewers and their availability before work is accepted. Your team may supply that expertise; we do not infer it from general annotation experience.
No. Labels address one part of model quality. Representativeness, leakage, the model approach and evaluation design still need to be assessed for the intended task.
Scope your annotation dataset
Describe the data type, the task it should support and any existing labels. We agree sample access, usage rights and the review process before handling the dataset.