Human-verified labels, preference pairs, and RLHF feedback data for enterprise AI models. All annotation work performed on your premises — sensitive data never leaves your environment.
Data annotation is the process of adding structured labels, tags, or human feedback to raw data examples so that AI models can learn the correct patterns, entities, preferences, or classifications during training. It is the critical link between raw data and a model that actually does what you need.
Modern LLM training workflows require multiple types of annotation: instruction-response pairs for supervised fine-tuning (SFT), preference rankings for reward model training and RLHF, named entity labels for information extraction, and classification tags for routing and filtering.
For enterprises with sensitive data — legal, healthcare, financial — annotation work cannot be outsourced to public crowdsourcing platforms where data crosses organizational boundaries. VaultData deploys annotation tooling directly into your environment so that your domain experts annotate your data without it leaving your control.
Human annotators rank or compare model responses to generate preference pairs (chosen / rejected) for reward model training. We support the Bradley-Terry comparison format used by Anthropic, OpenAI, and all major RLHF implementations. Annotator agreement scores and calibration sessions are included.
Domain experts write and validate instruction/response pairs tailored to your specific use case — legal Q&A, clinical documentation, financial analysis, or code review. Every response is scored on accuracy, format compliance, and helpfulness before inclusion in the dataset.
Token-level annotation of named entities: people, organizations, locations, dates, product names, medical terms, financial instruments, and custom entity types specific to your domain. Outputs are compatible with spaCy, Hugging Face Token Classification, and standard IOB2 format.
Multi-class and multi-label classification for document routing, content moderation, intent detection, and sentiment analysis. Hierarchical taxonomy support for complex enterprise classification schemes. Inter-annotator agreement (Cohen's kappa) tracked and reported per category.
We deploy a lightweight, self-hosted annotation interface into your infrastructure. Your team annotates in-browser with no data transmitted externally.
We work with your ML team to define the label taxonomy, annotation guidelines, and quality rubric. For RLHF, we design the comparison criteria. For NER, we define entity types and edge-case rules. A written annotation guide is delivered before any labeling begins.
We deploy a self-hosted annotation platform (Label Studio or a custom lightweight interface) inside your environment. No SaaS accounts required. Annotators access the tool via your internal network — data never leaves your infrastructure.
Before full-scale annotation, annotators complete a calibration batch of pre-labeled gold examples. Inter-annotator agreement is measured and annotators scoring below threshold receive feedback training. This step prevents systematic label noise from entering the dataset.
Annotation proceeds in batches. Each batch undergoes a review pass by a senior annotator. Disagreements are resolved via adjudication. Difficult edge cases are escalated to your domain experts. Progress and inter-annotator agreement metrics are reported daily.
The final annotated dataset undergoes a statistical quality audit: label distribution check, edge-case coverage verification, and cross-validation against gold examples. Exported in the exact format your training pipeline requires — JSONL, CSV, spaCy binary, or Hugging Face Dataset format.
Legal data annotated on external platforms may break privilege. On-premise annotation keeps privileged communications within the legal firewall — no SaaS terms of service override your confidentiality obligations.
Clinical notes and EHR data cannot be sent to crowdsourcing platforms or third-party annotation vendors without BAAs and risk exposure. On-premise tooling means PHI stays inside your covered entity perimeter.
Trade secrets, product roadmaps, and internal research sent to external annotation services expose IP under those vendors' data use policies. Your competitive advantage stays competitive when annotation stays in-house.