VectorCat - AI-driven Platform for FAIR Data Management

VectorCat is designed to help biotech and pharma organizations achieve FAIR data standards effortlessly. Our AI-driven platform streamlines knowledge management, data governance and process automation while ensuring compliance with industry regulations.

Findable data

• Data cataloging organizes all your lab and computational data in a structured, centralized repository.
• AI-powered semantic search allows users to locate relevant data instantly, even in unstructured documents like lab notes or protocols.
• Automated tagging ensures that data is quick to discover.

Accessible data

• API-based integrations connect with your existing systems, making data accessible without migration.
• Access controls ensure that the right people can access the right data without compromising security.
• Granular permission settings allow to restrict access to sensitive data yet make it findable through search, so that people can request access on the “need-to-know” basis.

Interoperable data

• VectorCat can transform existing metadata to a desired schema, harmonizing data from different sources.
• AI automations can create structured data from multiple unstructured documents, making it ready for analysis.
• Support for industry-specific data types (e.g. genomic sequencing data, assay results and imaging files) ensures seamless compatibility.

Reusable data

• AI-driven documentation agent automatically generates structured or unstructured documentation, ensuring data remains understandable and reusable over time.
• Intelligent discovery agent serves as the central repository of institutional knowledge. Whether you need to locate a specific data asset or explore all available company knowledge on a particular topic, drug or target of interest, the discovery agent efficiently compiles and delivers the relevant files, references and contextual links.

In the biotech and pharmaceutical industries, data is one of the most valuable assets. However, without proper data management, critical research insights, experimental results and regulatory information remain siloed, inconsistent or even lost.

The FAIR data principles — Findability, Accessibility, Interoperability and Reusability — provide a framework to ensure that data can be effectively shared and utilized across teams, organizations and AI-driven systems.