Parameterized real-world risk markets
Argovis AI brokers turn defensible data sources into contract-grade event parameters. Enterprises seek fast protection. Qualified capital reviews private risk notes tied to real-world outcomes.
For risk buyers
Define the amount, term, trigger, payout curve, and settlement timeline. Explore event protection that works alongside your insurance program.
For qualified capital
Explore tokenized private risk notes with disclosed triggers, maximum loss, collateral, and settlement rules before funding.
The proposed workflow
An AI broker identifies a buyer’s need and measurable source data.
Argovis prepares the parameter, payout curve, and evidence packet.
Qualified capital reviews a collateralized private risk note or event-linked contract.
The oracle checks the agreed data, resolves the trigger, and calculates payment.
Human approval and counsel review are required before live execution.
Who we serve
Enterprise risk leaders need protection for specific events. Professional capital partners need clear terms and evidence they can review.
Captives, business interruption, and enterprise risk financing.
Grid, hail, heat, outage, and uptime risk.
Algae blooms, water quality, and service disruption.
Closure, congestion, and supply chain interruption.
Insurance-linked securities and real-world asset strategies.
The parameter factory
Combine public, private, and sensor data into an auditable parameter. Give the buyer and capital provider the same evidence and settlement rules.
Asset, location, event, term, desired payout, and operational need.
Satellite, agency, sensor, alert, telemetry, and counterparty data.
Thresholds, source priority, fallback rules, dispute process, and payout curve.
Historical replay, pricing evidence, and the risk that a payout differs from the actual loss.
Event evidence, source timestamps, oracle decision, payout calculation, and audit trail.
Illustrative pilot
A proposed private pilot pairs a buyer’s need for outage liquidity with a short-term, fully collateralized event risk note.
Illustrative grid and facility interruption terms for a North Texas data center. Proposed sources include utility data, ERCOT notices, facility uptime, and local reports.
| Trigger layer | Source packet | Payout band |
|---|---|---|
| Grid stress | ERCOT emergency level plus timestamped market notices. | 25% |
| Local outage | Utility outage feed, restoration clock, and county emergency notices. | 60% |
| Facility impact | Uptime telemetry, backup-power status, or operator attestation. | 100% |
Illustrative figures only. The 8.0% figure is $160,000 divided by $2,000,000 for the 90-day term, before payouts, fees, and expenses. It is not an annualized or guaranteed return. Capital is at risk up to $2.0M. Final trigger thresholds and payout rules remain subject to review.
Buyer result
Proposed liquidity for downtime, with evidence for internal review and optional USDC settlement.
Capital result
Defined maximum loss, premium funded up front, and an objective trigger packet. Any secondary transfer requires legal approval.
Structure and review
A proposed qualified-only event contract or risk note, fully collateralized and transfer controlled. Securities, commodities, and insurance counsel must review the structure before launch.
If a buyer needs regulated insurance treatment, the parameter can be evaluated with a carrier, fronting carrier, managing general agent, or cell structure.
Enterprise risk and business continuity teams work with qualified capital partners. Each pilot starts with a specific buyer need and evidence that both sides can review.
Risk universe
Candidate parameters span infrastructure, the environment, logistics, grid disruption, conflict, and cyber-physical events. Each needs defensible data and an agreed trigger.
ERCOT, utility outage feeds, heat index, hail radar, facility uptime, and backup-power telemetry.
Satellite bloom data, local water agencies, field sensors, closures, and raw-water quality thresholds.
Thermal anomalies, satellite imagery, local air-defense alerts, and facility reports.
Vessel tracking, berth closures, vessel queues, port notices, and cargo dwell time.
Fires, chemical plumes, agency alerts, environmental sensors, and satellite confirmation.
Network and incident feeds, uptime logs, control-system data, cloud disruption, and operator attestations.
AI broker network
Argovis uses artificial intelligence (AI) brokers under human supervision to find risks, assemble evidence, draft outreach, and prepare proposed products.
Finds assets, stress signals, buyer needs, and relevant data sources.
Turns a risk description into candidate triggers and payout curves.
Prepares source priority, historical replay, risk analysis, and quote evidence.
Matches qualified investors by mandate, term, event type, and maximum loss.
Monitors events, records the evidence packet, and calculates proposed payouts.
Example risk inventory
These examples show who needs protection, which data could resolve the event, and what capital partners would review. They are illustrative, not available offers.
| Risk | Parameter | Buyer value | Capital view |
|---|---|---|---|
| Texas data center grid interruption | ERCOT emergency alert, outage duration, and facility uptime confirmation. | Liquidity during an outage. | Defined maximum loss and a short term. |
| Reservoir algae bloom | Satellite bloom index, agency advisory, and persistence threshold. | Cash flow during closure. | Defined environmental event exposure. |
| Port disruption | Port closure, vessel queue, and cargo dwell-time threshold. | Protection for supply chain interruption. | Logistics-linked event risk. |
First pilots
Argovis is looking for enterprise risk buyers and qualified capital partners for private pilots. Start with an asset, a measurable event, and a protection need.
Strategy framing only. Private risk notes, derivatives, securities, insurance, and payment arrangements require counsel review before live execution.