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Company description

VectorCat, developed by sci2sci, is an on-demand data governance platform purpose-built for biotech and pharma organizations. It automates data discovery, metadata curation, and compliance enforcement across an entire data landscape — processing hundreds of millions of files and data sources, organizing them into projects, and making them ready for business operations, R&D, data marketplaces, and AI agents. VectorCat is trusted by life sciences companies of all scales worldwide, including market leaders, CROs, and biotech startups.

Based on interviews with more than 100 biotech professionals, VectorCat was designed to address three recurring pain points: difficulty finding and organizing data buried across multiple systems and folders; report fatigue caused by manual metadata curation and constantly shifting schemas; and collaboration friction arising from CROs, universities, and internal departments operating on different systems and standards. These challenges are symptoms of a single root cause — ungoverned data — which compounds as data volumes grow approximately 30% year over year. VectorCat addresses this at scale, enabling traditional governance teams to process hundreds of millions of records annually for thousands of stakeholders.

Core Platform Modules

• Foundation (Infrastructure Setup): Connects to existing systems without data migration, supporting cloud, on-premises, and legacy environments. Implements full isolation with zero-trust architecture, including private cloud and air-gapped environment support. Allows organizations to define data protection frameworks, roles, and policies.
• Data Protection Agent (Automated Compliance Enforcement): Performs multi-stage sensitive personal information (SPI) detection covering Safe Harbor, GDPR, GxP, quasi-identifiers, composite identifiers, and custom policies. Immediately quarantines detected SPI or confidential data from unauthorized access. Enables review and governance workflows with data stewards from DPO and compliance teams.
• Data Stewardship Agent (Domain Understanding and Organization): Assists governance teams in discovering data structure and building ontologies. Maps ontologies to ground truth, enabling teams to work with semantic assets rather than raw files. Generates descriptions for discovered assets and supports collaborative validation with domain stewards.
• Data Annotation Agent (Metadata Enrichment and Transformation): Automatically assigns business metadata at any level of granularity. Converts unstructured data to structured formats and vice versa. Composes domain-specific knowledge graphs for downstream consumption.
• Consumption Layer (Governed Data Ready for Everyone): Supports decisions and audits by enabling users to find data related to specific programs, targets, or compounds with fully auditable decision chains. Provides data cataloging, marketplace annotation, documentation support, and analytics on extracted data. Enables use of clean, categorized data and document context graphs for ML model training and compliant AI agent deployment.

Key Capabilities

• Natural language search across all connected systems, with support for multilingual content and company-specific search filters
• Automated SPI detection synced with regulatory frameworks including GDPR, HIPAA, GxP, quasi-identifiers, composite identifiers, licensed data restrictions, internal policies, and departmental SOPs
• 100% audit traceability across all data governance decisions
• First insights delivered in under 24 hours from deployment
• LLM costs reduced by up to 100x through clean, structured data preparation
• Hundreds of millions of files processed with over 10,000 hours of work automated
• Proactive prevention of sensitive data exposure — for example, stopping PHI from being sent to external AI tools such as ChatGPT
• Support for the SAFE Framework for data protection policy management

Integrations and Deployment

• Connects natively to SharePoint, AWS S3, Azure Blob, Google Drive, Snowflake, Databricks, Benchling, Box, network drives, and additional data sources
• No data migration required — integrates directly with existing infrastructure
• Supports private cloud and air-gapped deployment environments for highly regulated or sensitive settings
• GDPR and HIPAA compliant
• Demos available in VectorCat's isolated cloud environment or within the customer's own infrastructure

Use Cases Supported

• R&D data organization and experiment traceability
• Operations and portfolio management
• Commercial data workflows and data marketplaces
• Regulatory audits with full decision chain recovery
• Data pipelines, analytics, and business intelligence
• AI agent deployment and machine learning model training on governed, compliant datasets

VectorCat offers a proof-of-concept engagement for organizations looking to understand what is hiding in their data, with the ability to run demonstrations in either a fully isolated cloud environment or directly within a customer's own infrastructure.

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Software attributed to sci2sci

• VectorCatView

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