Intelligent automation for adverse event detection, signal analysis, and regulatory compliance
Our Tools
SafetySignal AI
Real-time adverse event detection and signal analysis
AutoReport
Automated ICSR generation and regulatory submission
RiskAssessor
AI-powered risk-benefit analysis and safety profiling
LiteratureMonitor
Intelligent medical literature surveillance
Technology Stack
Natural Language Processing (NLP)
BERT and Transformers
Python and FastAPI
Apache Kafka
MongoDB and Redis
scikit-learn and XGBoost
Docker and Kubernetes
React and TypeScript
Public Databases We Integrate
FDA FAERS
FDA Adverse Event Reporting System
EudraVigilance
European adverse drug reaction database
PubMed
Biomedical literature database
ClinicalTrials.gov
Clinical trial safety data
WHO VigiBase
Global database of reported adverse effects
Case Study: Global Drug Safety Detection
Challenge
A global pharmaceutical company was struggling to monitor adverse events across 80+ countries for their portfolio of 200+ marketed drugs. Manual review of reports from multiple databases (FAERS, EudraVigilance, VigiBase) was time-consuming, inconsistent, and led to delayed signal detection.
Solution
We implemented our SafetySignalAI and AutoReport systems to continuously monitor global adverse event databases, literature sources, and social media. Our NLP models automatically extracted, standardized, and analyzed safety data in real-time, flagging potential signals and generating automated ICSRs compliant with ICH E2B standards.
Challenge
76% faster signal detection (from 14 days to 3.4 days average)
94% accuracy in automated ICSR generation, reducing manual effort by 68%
5 critical signals identified that had been missed by manual review
$4.2M annual savings through automation and improved efficiency
100% regulatory compliance maintained across all jurisdictions