Specialized training data and evaluation sets, developed through annotation, synthetic generation, and expert review.
Discuss your projectAI-assisted checks flag outputs for review. Human reviewers assess them against task-specific criteria, resolve ambiguity, and document corrections.
Reviewed outputs, decision records, and an evaluation set with explicit quality criteria.
We define annotation schemas and apply them across text, documents, and images. Quality review resolves inconsistent labels and ambiguous cases.
Labeled data in an agreed format, annotation guidelines, and a record of quality checks and edge cases.
We generate examples for gaps in task coverage, including variations in language, format, and input conditions. Validation and review check the data against the task specification.
A synthetic dataset with generation criteria and review records, kept separate from the evaluation set.
Subject-matter review defines correctness within the constraints of your field. Specialists assess terminology, assumptions, and cases where the available context changes the answer.
Domain-specific rubrics, reviewed reference material, and documented decisions for ambiguous cases.
We define inputs, expected outputs, annotation rules, and acceptance criteria before data production.
We annotate existing data, generate missing examples, and review them against the agreed criteria.
We assess results on a separate evaluation set. Errors inform revisions to the data, labels, and review guidelines.
Share the task, available data, and failure cases you need to address.
Services provided by Auxerta, LLC.