LLM data training
Broad and domain-specific language expertise paired with human-grade judgment to train and refine LLMs for generation, classification, reasoning, and multi-step analysis tasks
Supervised Fine-Tuning
Prompt–response generation and verification, prompt categorization, domain-specific and general knowledge tasks, multilingual instruction data, open-QA, summarization, rewriting, chain-of-thought reasoning, and a wide range of supervised fine-tuning use cases
Preference Optimization
Dataset and guideline development, output scoring and comparison, and structured preference ranking for Direct Preference Optimization (DPO), Proximal Policy Optimization (PPO), and other preference-based alignment methods
Human-in-the-Loop Evaluation
Diverse human evaluation and benchmarking of model performance, output moderation, and rigorous verification workflows designed to detect errors and mitigate LLM hallucinations.