TileDB provides a unified platform for managing and analyzing both structured and unstructured scientific data, enabling teams to catalog, collaborate, and perform large-scale analyses without data silos. This solution enhances the efficiency of drug and target discovery by centralizing diverse data types and facilitating secure collaboration across research teams.
Funding
$34M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Scientific research is often hampered by data silos, where structured and unstructured data are stored in disparate systems, hindering collaboration and large-scale analysis. This fragmentation makes it difficult for teams to efficiently catalog, access, and analyze diverse data types, slowing down the pace of discovery.
Solution
TileDB provides a unified data platform that enables scientific and data teams to catalog, collaborate on, and analyze all their structured and unstructured data in a single, secure environment. By consolidating diverse data types, TileDB eliminates data silos and facilitates secure collaboration across research teams. The platform's underlying array-based storage adapts to capture the structure of all data, regardless of modality, resulting in high performance. Its serverless, elastic, distributed compute infrastructure allows users to scale as data analysis requirements grow, while optimizing the total cost of operations.
Target Audience
TileDB targets scientific and data teams in enterprises and universities who need to manage, analyze, and collaborate on large, complex datasets for drug and target discovery and other scientific research.
Features
- Unified catalog for structured and unstructured data, including genomic, single-cell, PDF, and CSV files
- Cloud-optimized storage with array-based structure and indexing for high-performance data access
- Serverless, elastic compute infrastructure for scalable data analysis
- Secure collaboration with access controls, logging, and adherence to FAIR principles
- Built-in vector search capability for AI/ML model training and management
- Support for RAG LLMs
- APIs for programmatic data ingestion and cataloging
- Integration with cloud storage providers