Sci2sci
VectorCat provides a neurosymbolic memory layer for enterprises, transforming documents and data into executable code to ensure AI outputs are traceable, verifiable, and free of silent inconsistencies. The platform automatically extracts facts, tracks cascade dependencies, and detects hallucinations, making it purpose-built for regulated industries like biotech and pharma. It offers full audit trails and 21 CFR Part 11 compatibility, positioning itself as a reliability layer for AI-driven knowledge management.
- Artificial Intelligence
- Data & Analytics
- Biotechnology
- Enterprise Software
- Healthcare Technology
- Regulatory & Compliance Technology
- Software Only
Funding
Raised to date
€1.2MRaised 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.
Est. $1.3Macross 1 round
RC€1.2M · Pre-Seed
Announced Sep 2026 · from article date
Disclosed cumulative: $1.3M
Founders
Product
Problem
Enterprises, particularly in regulated industries like biotech and pharma, struggle with fragmented data scattered across ELNs, spreadsheets, and cloud storage. This disconnection makes it difficult to trace the origin of facts, verify AI-generated outputs, and detect inconsistencies that can compromise compliance and decision-making. Existing solutions like RAG or knowledge graphs store information but fail to actively process, validate, and maintain the integrity of knowledge as it evolves.
Solution
VectorCat provides a neurosymbolic memory layer that turns documents and data into executable code, enabling knowledge to behave like software—versioned, traceable, and reproducible. The platform automatically extracts facts, claims, and relationships from every data source, connecting them into a unified system that updates as data changes. It performs cascade dependency tracking, surfacing downstream impacts instantly when a fact changes, and uses neurosymbolic verification to detect LLM hallucinations and pre-existing inconsistencies in ground truth. Every output is traced to its source, ensuring 100% truth-grounded results. The system is designed for regulated industries, offering full audit trails, deterministic verification, and 21 CFR Part 11 compatibility, making it a reliability layer that AI companies cannot currently provide.
Target Audience
Primary customers are biotech and pharma companies, as well as banking and other regulated industries, where data integrity and auditability are critical to operations and compliance.
Features
- Living knowledge extraction that automatically pulls facts, claims, and relationships from all data sources into a unified, self-sustaining system
- Cascade dependency tracking that surfaces downstream impacts across systems, teams, and jurisdictions in under one minute
- Neurosymbolic verification that detects LLM hallucinations and pre-existing inconsistencies, with every output traced to source
- Full audit trail and deterministic verification, compatible with 21 CFR Part 11 for regulated industries
- Executable knowledge substrate that can be versioned, diffed, and reproduced, unlike static knowledge graphs or RAG stores
- Permission-aware search that distinguishes the right to find from the right to access, with metadata and audit evidence embedded in components