Rank model outputs, tag entities, extract entity relations and classify documents & texts with multimodal context through AI engines to fine tune data for NLP models and LLMs.
From RLHF preference ranking to named entity tagging, the data labelling layer routes every document to the right NLP modelities, auto-labelling engines provide results with confidence, while those above threeshold are passed automatically others are directly sent to a manual review so nothing gets shipped unverified.
RLHF preference ranking, named entity recognition, text classification, summarization, and question answering, all routed through the same Label layer.
Every document is scored by the engine best suited to its task. Labels above the configured confidence threshold are accepted automatically everything else is queued for a review.
Yes. Ontologies are configured per project, from flat tag lists to nested, multi-level classification schemas.
Every delivery includes data-level and task-level quality metrics, plus a full audit trail suppourted by lineage report.
Labeled text data flows directly into Version for lineage tracking and Observability for production drift monitoring with no extra export intervention.