Autonomous LLM Training Data Curation Platform

Training data costs range from $5 to $25 per labeled example, with fine-tuning typically requiring 10,000 to over 100,000 examples, making data acquisition expensive and resource-intensive. Manual curation scales poorly, producing only 50 to 100 examples per person per day, which cannot keep up with model demands. Synthetic data quality is often inconsistent, as naive generation methods can degrade model performance when used without validation. Reinforcement learning with human feedback (RLHF) further adds to the burden, requiring teams of 20 to 50 annotators and costing over $500,000 per fine-tuning cycle. Compounding these challenges, there is no automated quality assurance for training data, allowing poor-quality examples to silently impact and degrade overall model performance.