Other Data Services
Data operations beyond annotation: entry, cleansing, auditing, validation and engineering, run by a managed team with review built in.
Who it is for
Teams that need data entry, cleansing or enrichment, auditing, QA or model validation, or data engineering and ETL, carried out carefully by a managed team, either alongside labelling or as a standalone piece of work.
Supported tasks
Each operation is scoped on its own terms, with rules and checks written for that kind of work:
- Data entry: capturing information from documents, forms or other sources into structured records.
- Cleansing and enrichment: correcting, de-duplicating and standardising records, and adding missing attributes.
- Auditing and QA: checking existing datasets or third-party labels against your rules and reporting the errors found.
- Model validation: human review of model outputs to measure where a model is right and where it fails.
- Data engineering and ETL: moving, transforming and preparing data so it is ready for training or analysis.
Patient-data workflows
If the work involves structuring or de-identifying patient-related data, see Healthcare and medical annotation. Those projects follow the handling practices agreed for each engagement, as described on Security and Compliance.
Sample output, FAQ and quote
Before you commit, we can show a permission-cleared or clearly synthetic example of the output, such as a cleansed record set or a report of the label errors found in an audit.
Can you audit labels produced by another vendor? Yes. Auditing and QA of existing datasets is one of these services.Can this run alongside annotation? Yes. Many projects combine cleansing or validation with labelling.