LibGem provides a secure platform that aggregates anonymized data from multiple companies in the same industry to generate high‑fidelity synthetic datasets. These datasets can be accessed instantly via a credit‑based model and integrated into existing ML pipelines, helping AI and data science teams improve forecasting, fraud detection, churn prediction, and LLM fine‑tuning without exposing proprietary data.
Funding
Funding not disclosed
Founders
Product
Problem
AI and data teams often lack sufficient, high‑quality training data for tasks like demand forecasting, fraud detection, churn prediction, and LLM fine‑tuning. Individual companies’ datasets are limited in size and scope, leading to models that miss rare patterns and underperform.
Solution
LibGem offers a secure aggregation platform that pools anonymized signals from multiple companies within the same industry. Contributors upload their data through an encrypted ingestion layer where it is immediately anonymized and never stored in an identifiable form. The platform then synthesizes a large, high‑fidelity dataset that preserves the statistical relationships of the combined pool while protecting each participant’s privacy. Users can access these synthetic datasets instantly via a credit‑based system, exporting them directly into their existing training pipelines to improve model accuracy from day one.
Target Audience
Primary customers are AI and data science teams in enterprises that need large, diverse training datasets for predictive modeling, forecasting, fraud detection, and large‑language‑model fine‑tuning within a specific industry vertical.
Features
- Secure ingestion pipeline that anonymizes data on arrival and prevents any identifiable storage
- Cross‑company aggregation that multiplies available data volume (average 78× multiplier) while maintaining 97.3% synthetic fidelity
- Automated synthetic data generation preserving statistical patterns for churn, demand forecasting, fraud detection, LTV, and LLM fine‑tuning use cases
- Credit‑based access model with instant export in common ML framework formats
- Real‑time pool metrics dashboard showing total records, contribution impact, and growth rates
- Role‑based controls allowing contributors to retain full governance over their data contributions