VectorCat provides a self-curating data catalog specifically for biotech and pharma organizations. It automatically connects and maps lineage across diverse data assets, including structured data, unstructured documents, and specialized files. This unified system enables natural language search and verifiable AI agents for instant, traceable access to institutional knowledge.
Funding
Funding not disclosed
Founders
Product
Problem
Biotech and pharmaceutical organizations store experimental data, protocols, and analysis results across a fragmented landscape of network drives, ELNs, LIMS, cloud storages, data lakes, and BI dashboards. The lack of unified metadata, automated lineage, and searchable context forces scientists to spend hours locating historical experiments and increases the risk of AI‑driven hallucinations when data is accessed without verification.
Solution
VectorCat delivers a self‑curating data catalog that ingests and indexes every data asset—structured and unstructured—without requiring migration. By automatically extracting metadata, generating documentation, and constructing universal lineage graphs, the platform provides instant, context‑rich answers to natural‑language queries. Integrated AI agents can search across all sources, retrieve relevant files, and present traceable, document‑grounded evidence, reducing manual curation effort. The system respects existing user permissions, ensuring that sensitive information is only exposed to authorized roles. Developers can build and deploy custom domain‑specific AI agents using a model‑agnostic orchestration framework that runs distributed workflows on client machines, lowering infrastructure overhead. Security and compliance are embedded at the data‑access layer, allowing organizations to adopt AI‑enhanced research without compromising data governance.
Target Audience
Primary customers are R&D teams, data scientists, and informatics groups within biotech and pharmaceutical companies that need searchable, provenance‑rich data across heterogeneous systems.
Features
- Connectors for ELNs, LIMS, network drives, cloud storage, data lakes, and BI tools that ingest both structured files and unstructured lab notes, protocols, and presentations.
- Automatic metadata extraction and documentation generation that updates as data is used, eliminating manual tagging.
- Universal lineage mapping that links data processing steps to source documents, providing full provenance for regulatory and reproducibility needs.
- Natural‑language search across all indexed assets with AI‑driven relevance ranking and real‑time query answering.
- Built‑in AI agents (research assistant, data extraction, documentation) that operate on any LLM or model, with workflow visualization and distributed execution to reduce server load.
- Permission‑aware access control where AI inherits user roles, ensuring confidential data remains protected.
- No‑migration architecture that layers on existing infrastructure, allowing immediate deployment without disrupting current workflows.
- Enterprise‑grade security features including end‑to‑end encryption, audit logging, and optional on‑premise deployment so the provider never accesses raw data.