CYGNVS AI

Where innovation meets enterprise-grade data

Many AI-driven incident response solutions fail because they lack real-world incident data. By partnering with Marsh, the world's leading insurance broker and risk advisor, CYGNVS securely incorporates anonymized and aggregated incident patterns into model training. By combining CYGNVS innovation with Marsh’s real-world insight, enterprises benefit from a purpose-built AI model that helps teams prepare, respond, and report with greater speed, clarity, and confidence.

Real-World Incidents

AI trained on over 20,000 real-world incidents and outages

Privacy First

Anonymized and aggregated incident patterns with no shared client data

Purpose-Built AI

Designed specifically for readiness, tabletop exercises, live responses, and reporting

Action In Minutes

Move with speed, confidence, and coordination during high-pressure incidents

Faster Readiness

CYGNVS AI helps organizations build, assess, and continuously improve response playbooks so teams are ready when a crisis begins.

Benchmark and improve playbooks against industry standards
Produce department, incident type, or geography-specific playbooks

Real-Time Guidance

During live incidents, CYGNVS AI delivers role-specific guidance and real-time summaries that help teams take the right action faster.

Deliver real-time, role-specific incident response summaries
Provide incident specific guidance to help teams act faster and smarter

Superior Reporting

CYGNVS AI accelerates post-exercise and post-incident reporting by turning outcomes into actionable insights and compliant documentation.

Generate after-action reports from tabletop exercises
Quantify exercise participation, gaps, and recommendations
Prepopulate regulatory reports

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Real-World incident insights, protected

Marsh independently develops model weights and tokens by analyzing its library of loss data, then internally vets the training outputs to ensure client information remains private and secure. CYGNVS aggregates the resulting anonymized insights and patterns with its own independent sources to create an integrated training set for the CYGNVS AI model. This approach gives organizations access to data-driven incident insight while maintaining strict controls over privacy and security.