Data Validation & QA
Independent review of datasets you already have, so you know how reliable they are and what needs fixing before you train on them.
What we annotate
Common task types. Every project follows guidelines agreed with you at scoping.
Label audits
Sampling existing labels to measure accuracy and consistency.
Correction & re-labelling
Fixing errors and bringing labels in line with your guidelines.
Guideline review
Finding unclear definitions and edge cases in your taxonomy.
Gold-set creation
Building reference sets to check annotators and models against.
Data cleaning
Removing duplicates, corrupt files, and out-of-scope samples.
Quality reporting
Documented findings, error types, and recommended fixes.
Used across industries
Where this service is most often applied.
AI Needs More Than Data.
Talk to our team about annotation programs, datasets, and enterprise AI infrastructure tailored to your domain.