Transform fragmented health data into predictive intelligence. Give your leaders the early warning and technical insights needed to protect communities.
The Status Quo
Traditional surveillance systems rely on lagging indicators. They are reactive, fragmented, and too slow to prevent modern health crises.
By the time traditional surveillance detects an outbreak, it's already spreading. Officials lack the critical advance warning needed.
Critical health signals are scattered across hospitals, pharmacies, and municipal wastewater. No single pipeline integrates this data.
Departments are chronically under-resourced. Manual data analysis means subtle threats slip through the cracks easily.
Every day of delayed response multiplies healthcare costs and strains hospital capacity. Prediction is exponentially cheaper.
The Solution
Forelytics ingests 50+ disparate health data streams into a unified intelligence platform. By applying deep learning to these massive datasets, we detect weak signals of emerging threats weeks before traditional surveillance models trigger.
Normalize inputs from EHRs, pharmacies, wastewater, and climate sensors instantly.
Project disease trajectories 2-4 weeks into the future with high confidence intervals.
Generate real-time risk scores and dispatch actionable AI briefings to stakeholders.
Architecture
Combining cutting-edge machine learning topologies with robust cloud DevOps practices to deliver reliable, HIPAA-compliant intelligence.
Deep neural networks trained on decades of historical epidemic patterns, recurring seasonal trends, and high-velocity real-time signals to forecast disease activity with unprecedented precision.
Deployed on a cloud-native architecture utilizing automated CI/CD pipelines. This ensures 99.9% availability, uncompromising data governance, and frictionless horizontal scaling during critical events.
Applications
Deploying predictive intelligence across operational vectors to protect communities from diverse threats.
Identify anomalous disease clusters and emerging outbreaks up to 4 weeks before traditional lagging surveillance indicators, enabling targeted, proactive containment strategies.
Utilize time-series forecasting to predict respiratory disease surges and emergency department demand. Optimize local hospital staffing, supply chains, and ICU bed allocations.
Identify micro-geographic hotspots and vulnerable sub-populations for highly targeted vaccination drives, maximizing coverage efficiency and preventing outbreak clusters.
Correlate social determinants of health (SDoH) against real-time epidemiological data to expose systemic disparities and deploy equitable, data-backed interventions.
Move beyond infectious diseases. Monitor macro-population trends in conditions like diabetes, hypertension, and asthma to guide long-term state prevention funding.
Aggregate pathology data to monitor antimicrobial resistance (AMR) patterns across regional lab networks, predicting emerging superbug threats and guiding antibiotic stewardship.
Get In Touch
Whether you represent a municipal agency looking to deploy Forelytics, or you're a data engineer wanting to join our mission to save lives through code, we need to hear from you.
We are actively expanding our technical team. Seeking Senior Data Scientists, Epidemiologists, and scalable Full-Stack Engineers.
View Open Roles