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/nucleus

AI-native safety database that shifts pharmacovigilance from passive data storage to active, real-time intelligence with transparent, explainable reasoning.

Solution by Graph
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Overview

/nucleus is an AI-native pharmacovigilance safety database developed by graph safety. It is designed for pharmaceutical and life sciences organisations that manage adverse event case volumes and need to move beyond the traditional passive repository model. Rather than storing data until queried by human experts, /nucleus operates as an active safety operating system that continuously interprets incoming safety data, triggers computable logic in real time, and delivers decision-ready intelligence across the safety organisation.

The platform addresses what graph safety describes as a linear scaling trap inherent in conventional safety databases: as case volumes grow, traditional systems require proportional increases in headcount, cost, and operational complexity. /nucleus is built to enable algorithmic scale, allowing organisations to handle greater case volumes without a corresponding increase in resources.

The 9 Pillars of Algorithmic Pharmacovigilance

  • Intelligent Nervous System: Safety data triggers logic instantly upon arrival rather than waiting for batch processing, delivering immediate risk insight without delays.
  • Collaborative Intelligence: A single unified workspace supports both human experts and intelligent agents working together, replacing email-driven handoffs and manual coordination.
  • Verify by Source: Every AI-generated insight is linked back to its original source, maintaining traceability and transparency and avoiding black-box uncertainty.
  • Transparent Intelligence: Each inference produced by the system includes a human-readable rationale grounded in contextual logic, making the system audit-ready by design.
  • Auto Coding: Context-aware intelligence predicts MedDRA codes based on clinical context, supporting consistent and confident adverse event classification.
  • Networked Safety: A safety neural network connects real-world signals to molecules, patients, and adverse events, providing a connected view of the safety landscape.
  • Clinical Context Aware: Context-driven intelligence prioritises clinically relevant signals, suppresses noise, and reduces alert fatigue to support smarter triage.
  • Universal Gateway: The platform supports global regulatory submissions and integrations while maintaining a single consistent source of scientific truth across regions.
  • Persona Based Productivity: User experiences are adapted to individual roles within the safety organisation, enabling faster case reviews and deeper focus for each user type.

Key Operational Shifts Delivered by /nucleus

  • From passive to active safety operations: Safety data triggers logic immediately upon arrival rather than remaining idle until manually reviewed, enabling continuous awareness.
  • From black-box AI to transparent intelligence: Every inference is traceable to its source and grounded in clinical logic, supporting regulatory audit readiness.
  • From siloed work to collaborative execution: Disconnected, email-based workflows are replaced by a unified environment where human experts and intelligent systems operate together in real time.
  • From linear scaling to algorithmic scale: Organisations can increase case volume without proportionally increasing headcount or operational cost.

/nucleus is part of the graph safety suite, which also includes the /intake product. The platform is built to support global regulatory submissions and integrations, and its design emphasises audit readiness, source traceability, and explainability throughout the pharmacovigilance workflow.

Meta

Domain
Regulatory & Safety Documentation
Subdomain
Pharmacovigilance & Drug Safety
Software type(s)
Analytical Platform
Deployment type(s)
Cloud / SaaS
Industry vertical(s)
PharmaBiotechMedical Devices
Development stage(s)
ClinicalPost-Market & RWE
Target user(s)
QA / Regulatory AffairsClinical / Diagnostic ProfessionalMedical Affairs Professional
Compliance standard(s)
GxPICH
Tag(s)
Uses AI