PredgePoint / Insurance-layer
01 · what this is02 · why insurance model03 · underwriting logic04 · risk pooling05 · workflow06 · precedent07 · initial use case08 · what this is (really)
PredgePointInsurance-layer Offering

protection policies against event-specific business risks.

PredgePoint writes protection policies against event-specific business risks (fuel costs, rate resets, weather, regulatory outcomes) and hedges the resulting exposure internally using prediction market contracts. The client buys a business outcome, not a hedging mechanism.

01what this is

the client buys an outcome, not a mechanism.

PredgePoint writes protection policies against event-specific business risks (fuel costs, rate resets, weather, regulatory outcomes) and hedges the resulting exposure internally using prediction market contracts. The client buys a business outcome, not a hedging mechanism.

  • CLIENT BUYS"Fuel cost protection for your project"
  • Client does NOT seeprediction markets, contract selection, 2FA, account funding
  • PredgePoint sellsrisk transfer, priced as a premium
  • PredgePoint's cost basisprediction market contracts + basis risk
02why insurance-model

business model.

DimensionPM-as-product (spread/mgmt fee)Insurance model (premium)
Margin~10bps market access fee% of risk removed
Sales narrative"Prediction market hedge on diesel""Fuel cost protection"
Client frictionAccount creation, PM literacyNone — pays premium, done
PredgePoint's book1 hedge per clientNetted/pooled across clients
ScalabilityLinear with clientsSub-linear — diversification benefit compounds
03the underwriting logic

portfolio construction, not insurance.

Five clients with fuel exposure ≠ five hedges. PredgePoint runs:

  1. 01
    Normalize
    convert each client's idiosyncratic exposure (gallons, project duration, equipment mix) into a common risk-factor vector (e.g., $ loss per 1% oil move — "oil beta")
  2. 02
    Aggregate across clients
    sum risk-factor exposures into one portfolio-level exposure by factor (oil, rates, inflation, weather)
  3. 03
    Net across time
    monthly exposure ≠ sum of all client exposures, since projects don't fully overlap. Peak exposure is the monthly max, not the annual sum
  4. 04
    Net across clients
    opposite exposures cancel (e.g., trucking company long-diesel-risk vs. oil producer short-diesel-risk) → net exposure is a fraction of gross
  5. 05
    Build one hedge book
    a single basket of contracts (e.g., 40% oil / 30% supply-shock / 20% gasoline / 10% cash) serves the entire book, rebalanced continuously — not repurchased per client

Net effect: PredgePoint hedges a portfolio distribution, not N separate contracts. This is the capital efficiency and the margin.

04risk-pooling mechanics

the moat.

Netting
Opposing client exposures offset before any hedge is placed.
Correlation diversification
Clients across diesel / rates / weather / FX rarely trigger simultaneously; required capital is well below the sum of gross exposures.
Exposure aggregation
One large hedge instead of many small ones improves execution and MM negotiating leverage.
05workflow

river-integrated reference model.

  1. 01
    Intake
    customer type, revenue/cost drivers, duration, geography, exposure size → structured input
  2. 02
    Discovery
    pull relevant prediction market contracts (River API / internal DB) — discovery only, not execution
  3. 03
    Hedge design
    map contracts to exposure via transmission chain (e.g., Hormuz → Brent → wholesale diesel → retail diesel → client margin); output basket + rationale + stated basis risks
  4. 04
    Human approval
    portfolio-level sign-off, not per-policy
  5. 05
    Execution
    via River (screen or OTC)
  6. 06
    Monitoring / reporting
    ongoing, feeds back into portfolio rebalancing
06reference precedent

generalizing a proven pattern.

Existing River client: a licensed underwriter writes policies on sports-outcome-dependent revenue (e.g., parking lots near NBA venues losing revenue if a series doesn't reach Game 7), hedging the resulting exposure through River. PredgePoint generalizes this pattern beyond sports to any client whose insurance-ineligible, event-driven treasury/procurement exposure can be mapped to a prediction market transmission chain.

07initial use case

construction fuel protection.

  • Ticket size$25K–$100K exposure per client, ~$20K max collateral
  • Instrumentdiesel exposure hedged via WTI beta (no direct diesel contracts exist on Kalshi/Polymarket — confirmed)
  • Path to scale5 → 20 → 200 contractors moves PredgePoint from "hedge per client" to "manage a distribution" (current exposure, avg duration, avg fuel sensitivity, peak monthly exposure, expected payout, 99% stress loss)
08what this business actually is

a hedge sold as a policy.

PredgePoint isn't a brokerage and isn't an insurer in the pure sense — internally, it's an underwriting and portfolio-optimization engine that prices and pools treasury-style exposures across clients; from the client's standpoint, it's a hedge against a business exposure, packaged and sold like a policy (premium in, payout on trigger).