Training large language models (LLMs) requires massive datasets, but real-world data often suffers from quality issues like bias, underrepresentation of key topics, and lack of diversity, hindering model performance and generalization. Manual data collection and curation are time-consuming and expensive, creating a bottleneck in LLM development.

Lemon AI provides a platform for generating high-quality synthetic data to enhance LLM training and fine-tuning. The platform leverages advanced encoder-only and decoder-only models to provide dataset explainability and predictive data attribution. It identifies data integrity challenges, predicts optimal datasets, and selectively removes, rewrites, or generates specific records as needed. Lemon AI enables users to address semantic or lexical underrepresentation and introduce targeted biases, improving model accuracy, reducing latency, and lowering costs.

Automated data curation to identify and address data quality shortcomings

Synthetic data generation to expand datasets and address underrepresented topics

Data cleaning tools to remove duplicates and rewrite text while maintaining dataset integrity

Dataset explainability features to analyze over- and underrepresented topics, duplicates, and syntax diversity

Support for Parquet, CSV, and JSON data formats

Lemon AI targets organizations building custom LLMs, including AI agents, who need to improve data quality, build data moats, and customize user experiences.