Dataerai offers a unified platform that automatically captures research data and metadata from instruments, simulations, and analysis pipelines, then converts it into standardized, AI‑ready assets. The system provides secure, fine‑grained cross‑institution sharing and a graph‑based search engine to discover and provenance‑track datasets, enabling labs and R&D teams to build and run AI workflows on curated multimodal data banks hosted on‑premises or in the cloud.
Funding
Funding not disclosed
Founders
Product
Problem
Scientific research generates large volumes of data from instruments, simulations, and analysis pipelines, but this data is often fragmented, inaccessible, and not formatted for AI use, limiting its impact on discovery and innovation.
Solution
Dataerai provides a unified platform that automatically captures research data and metadata, structures it into standardized AI‑ready assets, and securely connects institutions for collaborative access. The platform uses graph‑based discovery to locate relevant datasets across organizations and tracks provenance to ensure reproducibility. Researchers can then build and run AI workflows on curated multimodal data banks, whether hosted locally or in the cloud, accelerating model development and scientific insight.
Target Audience
Primary customers are research laboratories, academic institutions, and industrial R&D facilities that need to manage large scientific datasets and develop AI models, as well as government agencies overseeing federally funded research data.
Features
- Automated ingestion of data and metadata from instruments, simulations, and pipelines without manual effort
- Secure cross‑institution sharing with fine‑grained access controls and trusted authentication
- Standardization of raw outputs into AI‑ready formats across multiple data modalities
- Graph‑based search engine for discovering relevant datasets across distributed repositories
- Integrated provenance tracking linking data, code, and results for reproducibility
- Scalable data banks deployable on‑premises or in the cloud, linked through a common platform
- Built‑in tools for training domain‑specific AI models and creating digital‑twin pipelines