VectorCat

VectorCat is an on-demand data governance platform purpose-built for biotech and pharma organisations. It automates data discovery, metadata curation, compliance enforcement, and knowledge organisation across an entire data landscape — handling hundreds of millions of files and data sources so that governance teams can scale without proportional headcount growth. Trusted by market leaders, CROs, and biotech startups worldwide, VectorCat delivers first insights in under 24 hours and provides 100% audit traceability.

Based on interviews with more than 100 biotech professionals, VectorCat addresses three recurring pain points: difficulty finding and organising data buried across multiple systems, fatigue from manual metadata curation that degrades at scale, and collaboration friction caused by data silos at every handoff between CROs, universities, and internal departments. With data volumes growing 30% year over year, VectorCat treats these as symptoms of a single root cause — ungoverned data — and resolves them through a unified, automated platform.

Foundation: Infrastructure Setup

• Connect: Integrates with existing systems — cloud, on-premises, and legacy — with no data migration required.
• Secure: Operates with full isolation using a zero-trust architecture; private cloud and air-gapped environments are supported out of the box.
• Regulate: Enables organisations to define data protection frameworks, roles, and policies tailored to their specific requirements.

Data Protection Agent: Automated Compliance Enforcement

• Scan: Performs multi-stage SPI detection covering Safe Harbor, GDPR, GxP, quasi-identifiers, composite identifiers, and custom organisational policies.
• Quarantine: Immediately blocks any detected SPI or confidential data from unauthorised access upon discovery.
• Govern: Facilitates review workflows with data stewards from DPO and compliance teams to accept or adjust policy and quarantine decisions.

Data Stewardship Agent: Domain Understanding and Organisation

• Discover: Assists governance teams in uncovering data structure and building an organisational ontology.
• Map: Maps ontologies to ground truth, enabling teams to work with semantic assets rather than raw files.
• Describe: Automatically generates descriptions for discovered assets, with collaborative validation by domain stewards.

Data Annotation Agent: Metadata Enrichment and Transformation

• Enrich: Automatically assigns business metadata at any level of granularity.
• Transform: Converts unstructured data to structured formats and vice versa.
• Compose: Prepares domain-specific knowledge graphs for downstream consumption by analytics, AI, and ML systems.

Consumption: Governed Data Ready for Every Use Case

• Decisions and Audits: Enables users to find data related to specific programmes, targets, or compounds and recover fully auditable decision chains.
• Data and Analytics: Supports data cataloguing, marketplace annotation, documentation, and analytics built on top of extracted data.
• ML and AI: Provides clean, categorised data and document context-graphs for model training or for launching compliant AI agents supporting domain experts.
• Supports use cases across R&D, operations, commercial, portfolio management, data pipelines, data marketplaces, and more.

Search and Discovery Capabilities

• Natural language search across all connected systems to locate data and documents.
• Cross-language search that finds content regardless of the language in which it was written.
• Company-specific search filters customisable to an organisation's unique data structure.

Regulatory and Compliance Coverage

• Addresses GDPR, HIPAA, GxP, quasi-identifiers, composite identifiers, licensed data restrictions, internal policies, and departmental SOPs.
• Designed to prevent sensitive data — such as PHI — from being inadvertently exposed to unauthorised systems or AI tools.
• Synced with regulatory frameworks and fully GDPR and HIPAA compliant.

Integrations and Deployment

• Connects natively to SharePoint, AWS S3, Azure Blob, Google Drive, Snowflake, Databricks, Benchling, Box, network drives, and additional sources.
• Available as a proof-of-concept deployment in VectorCat's isolated cloud or within a customer's own infrastructure.
• Demonstrated to reduce LLM costs by 100x and automate more than 10,000 hours of manual governance work.