AI-powered surveillance that integrates with your existing infrastructure to provide real-time intelligence, equitable intervention optimization, and measurable public health impact.
Purpose-built for the realities of modern public health departments
Traditional surveillance is substantially delayed and not timely enough for early epidemic detection. Our platform provides real-time anomaly detection with 4-10 day lead time advantage over manual systems.
ED visits, lab results, and environmental signals sit in separate systems. We integrate multi-source data—including 8,000+ news articles daily—to reveal patterns invisible to single-stream surveillance.
Vulnerable populations often get overlooked. Our fairness-aware algorithms with continuous bias auditing ensure early detection works equally well in underserved communities.
Limited staff can't analyze thousands of signals daily. Automated triage and prioritization surface what matters most—saving up to 280 hours annually per use case.
Legacy systems don't talk to each other. We support HL7 FHIR, LOINC, SNOMED and integrate with CDC's National Syndromic Surveillance Program infrastructure.
Leadership needs proof of impact. Our framework provides transparent model cards, validation reports, and documentation aligned with evidence-based public health deployment practices.
We connect to your existing systems via secure APIs:
Privacy-first: Differential privacy and federated learning keep sensitive data on-premises while enabling collaborative intelligence.
Our ensemble models run continuously:
Proven approach: Similar to systems like EPIWATCH (providing signals before official announcements) and BlueDot (forecasting outbreaks through aviation/climate analysis).
Epidemiologists receive:
Models improve with your feedback:
Proven results from actual AI deployments in public health (2024-2025)
98% reduction in manual analysis time for critical surveillance tasks
(CDC TowerScout: 4 hours → 5 minutes per area)
= 280+ hours saved annually per use case = More time for high-value investigation
4-10 days earlier outbreak detection vs traditional surveillance
(Validated across multiple AI systems including EPIWATCH)
= Earlier interventions, reduced transmission, faster containment
8,000+ articles/day analyzed for outbreak signals
(CDC news surveillance automation)
= Global situational awareness impossible through manual monitoring
The CDC's deployment of generative AI for public health operations demonstrated:
Source: CDC Data Modernization Initiative (2025). This represents one use case; combined AI applications show multiplicative value.
Conservative estimates based on real-world AI deployment data:
Note: These are projected outcomes based on published CDC results and peer-reviewed literature. Actual impact varies by jurisdiction, data infrastructure, and implementation approach. We commit to transparent validation and outcome reporting.
From scoping to production in 12-16 weeks
Collaborative pilot opportunities for qualified state/local health departments
Transparency Commitment: All performance claims on this page are based on published CDC implementations, peer-reviewed studies, and real-world AI system validations (2024-2025). Projected outcomes for your jurisdiction may vary. We commit to rigorous validation, honest reporting of limitations, and continuous improvement based on evidence.