Post-Market Surveillance
After a drug is launched, continuous monitoring ensures its safety profile remains acceptable. We offer real-time pharmacovigilance analytics combining classical methods with AI.
What we do
Signal Detection Algorithms
Apply advanced statistical methods (Bayesian, disproportionality analysis) to detect early safety signals in spontaneous reporting systems like FAERS, EudraVigilance, and VigiBase.
Real-Time Monitoring Dashboards
Visualize safety data trends in real-time using interactive dashboards, with filters for geography, time, severity, and drug-product combinations.
Adverse Event Clustering
Use machine learning to identify clusters and latent patterns in adverse event data, enabling faster root cause analysis and risk mitigation.
NLP for Case Narratives
Automatically process and summarize unstructured adverse event narratives using Natural Language Processing to extract meaningful insights from spontaneous reports.
Global Safety Signal Index
Quantify product safety in a unified risk score that consolidates frequency, severity, causality, and temporal trends.
Automated Periodic Safety Reports
Generate PSURs and PADERs automatically by compiling safety data, narrative summaries, and signal detection outcomes.
Regulatory Alert System
Trigger alerts when threshold metrics are crossed, enabling proactive engagement with regulatory authorities and internal safety teams.
Business Value
Prevents regulatory warnings, reduces product liability, and enhances corporate reputation in safety commitment.