Co-founder & CEO
Profile summary: Poliark • Chief Revenue Officer (CRO) · MS at Rotterdam School of Management, Erasmus University
Alpha
InactiveAlpha offers a platform that trains AI models directly on raw tabular data—CSV files, spreadsheets, and live databases—without requiring preprocessing or manual feature engineering. Its encrypted environment uses a proprietary schema language to automatically generate features, select model architectures, and provide one‑click deployment of continuously updated model endpoints for enterprise data science teams.
- Artificial Intelligence
- Enterprise Software
Updated yesterday
Funding
Funding rounds are not available yet.
Founders
Product
Problem
Building AI models on tabular data typically requires extensive preprocessing, manual feature engineering, and complex pipeline setup, which slows development and demands specialized expertise. Organizations also face challenges keeping raw data secure while moving it through multiple transformation stages, leading to compliance and privacy concerns.
Solution
Alpha provides a platform that trains AI models directly on raw tabular sources such as CSV files, spreadsheets, and live databases without any preprocessing. Its proprietary Schema data language model interprets table structure and semantics, automatically generating features and selecting appropriate model architectures. Users connect to data sources or generate synthetic data, preview and validate inputs within an encrypted environment that never transfers data outside the user’s control. The platform delivers real‑time training metrics, an interactive testing playground, and one‑click deployment of model endpoints, supporting continuous updates as underlying data evolves.
Target Audience
Primary customers are data science teams, product engineers, and analytics professionals in enterprises that need to accelerate AI development on internal tabular datasets while maintaining strict data security.
Features
- Direct ingestion of CSVs, spreadsheets, and database connections with end‑to‑end encryption
- Schema data language model that natively understands table structure and semantics
- Automated feature engineering and model architecture selection without manual coding
- Multi‑source dataset handling with auto‑sync for evolving data pipelines
- Interactive testing playground for performance evaluation and rapid iteration
- One‑click deployment of model endpoints with built‑in versioning for continuous updates