Graph AI

What Graph AI does

Graph AI is the pioneer of the industry's first AI-native Patient Safety Operating System (OS), engineered to unify the fragmented pharmacovigilance (PV) landscape into a single, cohesive infrastructure for both biopharma enterprises and Contract Research Organizations (CROs). Founded by enterprise technology veterans, the company replaces legacy, labor-intensive workflows with a deterministic, inspection-ready AI architecture that explicitly aligns with the FDA’s AI Credibility Assessment Framework, the EU AI Act's high-risk data governance controls, and the CIOMS Working Group XIV recommendations. This Patient Safety OS delivers mathematical reproducibility and absolute data transparency across a comprehensive lifecycle portfolio, unifying source, intake, case processing, aggregate reporting, signal detection, risk management, and regulatory compliance into one connected intelligent ecosystem. The system is designed to elevate safety teams by reducing manual coordination and allowing reviewers to focus on clinical and regulatory judgment, while maintaining human oversight and control.

Products

• Graph Safety /intakeConverts fragmented data into validated intake, ingesting, classifying, and routing safety cases automatically across every source.
• Graph Safety /nucleusBuilds complete submission-ready safety cases by continuously structuring, validating, and connecting case evidence.
• Graph Safety /reportGenerates regulator-ready outputs automatically, from case to regulator-ready report, automatically aligned and delivered. Includes E2B(R3) generation, multi-region regulatory alignment, and gateway submission with ACK handling.
• Graph Safety /signalDetects emerging safety patterns across cases, surfacing cross-case signals, trends, and event relationships earlier.
• Graph Safety /assureValidates cases continuously during processing, identifying inconsistencies, missing data, and review gaps automatically.
• Graph Safety /complyEmbeds compliance directly into operations, maintaining auditability, traceability, and regulatory alignment continuously.
• Patient Safety Operating System (OS)An AI-native operating system that unifies the fragmented pharmacovigilance landscape into a single, cohesive infrastructure.

Key capabilities

• AI-native operating system
• End-to-end pharmacovigilance in one intelligent system
• Multi-channel ingestion
• Touchless case processing
• Mathematical reproducibility
• Absolute data transparency
• Deterministic, inspection-ready AI architecture
• Unified knowledge graph
• Automated workflows, triage, and reporting
• Expert in the loop with review, validation, and override
• Continuous validation embedded throughout processing
• Surfaces decisions, not tasks
• Human oversight and control
• Every action logged
• Every field is traceable
• Every workflow auditable
• Every decision reviewable
• Secure, scalable infrastructure
• AI models built for safety operations
• Connected intelligence layer
• Signal detection & triage
• Scientific review workflows
• Regulatory compliance automation
• Operational orchestration
• Explainability (reasoning chain behind AI recommendations)
• Governance (designed-in audit trails, access controls, regulatory alignment)
• Auditability (complete traceability of decisions, signals, actions)
• Human judgment (AI assists, humans decide)

Use cases

• Patient Safety
• Drug Safety Monitoring
• Signal Detection
• Risk Management
• Regulatory Compliance
• E2B R3 generation
• MedDRA coding
• WHO Drug coding
• Medical Device Safety
• Safety Data Management
• Unified Safety Workflows
• Intelligent Case Processing
• Aggregate & Periodic Reporting (PSUR, PBRER)
• Risk Management Planning (RMP, REMS)
• Knowledge-Driven Decision Support
• Pharmacovigilance
• Cosmetovigilance
• Meteriovigilance
• Literature monitoring, intake validation, and downstream case preparation
• Patient support workflows, adverse event processing, medical coding, and automated reporting

AI approach

Graph AI is building an AI-native Patient Safety Operating System (OS) for pharmacovigilance. Their system unifies fragmented data, automates workflows, and provides intelligent insights for patient safety. They emphasize explainability, governance, auditability, and human oversight in their AI systems, aligning with regulatory frameworks like the FDA’s AI Credibility Assessment Framework and the EU AI Act. They use a knowledge graph to connect cases, signals, and sources, and leverage domain-aware intelligence to interpret unstructured safety data.

Tech named: Vertex AI for medical reasoning, Kubernetes-native orchestration, AI models built for safety operations, Google Cloud, Gemini Enterprise Agent Platform, Machine Learning & NLP, LLMOps

Industries served

• Biopharma enterprises
• Contract Research Organizations (CROs)
• Pharmaceuticals
• Life sciences
• Healthcare
• Consumer Care

What it says sets it apart

• Industry's first AI-native Patient Safety Operating System
• Unifies fragmented pharmacovigilance landscape into a single, cohesive infrastructure
• Replaces legacy, labor-intensive workflows with deterministic, inspection-ready AI architecture
• Explicitly aligns with FDA’s AI Credibility Assessment Framework, EU AI Act's high-risk data governance controls, and CIOMS Working Group XIV recommendations
• Delivers mathematical reproducibility and absolute data transparency
• Designed to elevate safety teams, not replace human judgment
• Continuously understands, validates, and connects workflows across the pharmacovigilance lifecycle
• Maintains operational continuity by continuously connecting evidence, workflows, and downstream safety decisions
• Validates continuously, not in stages, with embedded validation throughout processing
• Surfaces decisions, not tasks, reducing manual coordination
• Keeps humans in control with embedded oversight, review, validation, and override
• Built for regulatory environments with embedded traceability, auditability, and oversight
• Engineered on enterprise-grade AI infrastructure, including Vertex AI for medical reasoning and Kubernetes-native orchestration
• AI models built specifically for safety operations
• Responsible intelligence for regulated environments with explainability, governance, auditability, and human judgment at its core

Funding rounds we track

From the AI funding tracker — rounds as reported by the linked publications.