Conversation annotation, intent labeling, RLHF preference data,
fine-tuning datasets — multilingual, enterprise-scale.
Text-based data collection, multilingual.
Dialogue tagging, turn-taking analysis, context labeling
Intent classification, named entity recognition, slot filling
Human preference ranking, response comparison, quality scoring
Prompt-response pairs, instruction tuning data
Red teaming, bias detection, model evaluation
Quality isn't just reviewed after the fact — it's built into every step.
Custom AI agents monitor annotations in real-time, flagging inconsistencies and errors before they compound.
Every project gets a custom AI chatbot that answers annotators' questions instantly — reducing errors and ensuring guideline consistency.
Performance ranking across projects. Only top-performing annotators work on your data — we track accuracy, speed, and consistency over time.
Inter-annotator agreement metrics, senior reviewer audits, and dedicated project managers with domain expertise.
Native speakers and linguists across all major languages.
Multilingual projects, code-switching, and regional variants supported.
AIxBlock provides dialogue and RLHF datasets for LLMs, including but not limited to multi-turn conversations, intent and entity labels, human preference rankings, etc. These datasets are used to fine-tune, align, and evaluate LLMs beyond generic web text.
AIxBlock’s RLHF datasets are designed with task-specific rubrics and domain context, not generic thumbs-up signals. Human feedback is structured around real outcomes such as correctness, safety, and task completion, which improves LLM behavior in production use cases.
Yes. AIxBlock creates domain-specific dialogue datasets by using specific SMEs for your requested domains. This helps LLMs learn realistic conversation patterns, terminology, and decision logic that open-domain dialogue datasets do not capture.
AIxBlock supports regulated LLM projects through Self-Hosted Data Platform where dialogue data remains inside the client’s infrastructure. This setup supports data sovereignty, auditability, and compliance requirements common in banking, healthcare, and government AI systems.
A team should choose AIxBlock when internal efforts fail to meet the scale and diversity required for production-ready models. Specifically:
No. Period.
