All-source monitoring
Geofence assets and continuously collect satellite, weather, environmental, public-source, cyber/OT, logistics, local incident, carrier, and asset-owner signals.
All-source infrastructure risk intelligence for insurance
Argovis monitors risk as fast as infrastructure risk changes.
A dynamic insurance decision-support layer for P&C carriers, reinsurers, brokers, captives, and self-insured infrastructure owners. Argovis fuses asset data, satellite imagery, weather, environmental feeds, OSINT, cyber-physical signals, incident datasets, and policy terms into underwriting-ready action.
Insurance decision support for assets whose risk changes faster than underwriting cycles.
Argovis sits between exposure intelligence, underwriting, risk engineering, actuarial pricing, portfolio management, and reinsurance. It supplies explainable risk movement, not automated underwriting authority.
Validated risk fusion
Score every signal for source reliability, corroboration, recency, location confidence, asset relevance, and connection to policy terms.
Insurance translation
Convert risk movement into estimated loss, payout exposure, PML shift, technical premium indication, sublimit guidance, confidence score, and recommended action.
End-to-end flow
From policy intake to portfolio governance
- 1Ingest asset, location, insured values, BI value, policy terms, exclusions, current premium, renewal date, and mitigation posture.
- 2Construct an asset graph of physical components, dependencies, nearby exposures, supply-chain links, and BI pathways.
- 3Monitor all-source signals around the asset and fuse them into explainable scenario-level risk movement.
- 4Model drone, flood, wildfire, storm, environmental release, sabotage, cyber-physical outage, industrial, and logistics scenarios.
- 5Return renew, reprice, require mitigation, refer to risk engineering, add exclusion, reduce limit, reinsure, or decline recommendations.
Canonical demo: oil refinery all-risk monitoring. Refineries combine property damage, machinery breakdown, environmental liability, political violence, business interruption, and reinsurance accumulation in one asset class.
Agentic AI thesis
Agents turn scattered external data into governed insurance evidence.
The next insurance advantage is not another static dashboard. It is a managed workforce of AI agents that collects evidence, checks source quality, normalizes data to asset-level exposure, updates models, tests scenarios, and prepares underwriting recommendations every hour of every day.
The output is an evidence trail: raw signal, corroborated signal, insurance-relevant trigger, model impact, confidence level, and recommended underwriting action.
Built for the risk universe around critical assets.
The initial wedge remains drone, sabotage, and geopolitical risk, but the full platform monitors all available signals that can change expected loss, BI exposure, PML, reserves, and renewal pricing.
Drone activity, sabotage, terrorism, war-adjacent events, civil unrest, perimeter breaches, and comparable conflict-zone losses.
Storm tracks, wind, hail, flood, wildfire, drought, heat, snow load, and asset-level vulnerability to physical damage and BI.
Pollution releases, chemical plumes, water contamination, fires, explosions, maintenance signals, outage reports, and PML movement.
ICS/SCADA alerts, ransomware indicators, supplier incidents, port disruption, pipeline interruption, sanctions, and contingent BI.
Make infrastructure risk measurable, explainable, and actionable.
Use Argovis to convert all-source infrastructure signals into validated risk scores, premium movement, mitigation requirements, confidence levels, and carrier-ready recommendations.