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Risk infrastructure for prediction markets

Purpose-built risk engines for firms managing exposure on event contracts. API-first. Deterministic. Built from the ground up.
Global prediction markets
Risk Management Engine·User Rating Engine·Simulation Fill Engine·Execution Balancer·Real-time Event Risk Index·Event Risk Compliance Layer·Risk Management Engine·User Rating Engine·Simulation Fill Engine·Execution Balancer·Real-time Event Risk Index·Event Risk Compliance Layer·

Built for operators

Purpose-built risk infrastructure for firms who run the book, not just trade it.

Startups

Building a prediction market or event contract platform from scratch and needing production-grade risk infrastructure without an 18-month internal build.

Hedge Funds

Expanding into event contracts and discovering that none of your existing risk stack—stop-loss logic, VaR models, drawdown limits—transfers to binary-outcome instruments.

Brokerages & Fintechs

Adding event trading to your product and needing compliance-ready, auditable risk controls before you can launch, without an 18-month internal build.

OUR PLATFORMOUR PLATFORMOUR PLATFORM
Risk Management Engine

Deterministic exposure control

How do you enforce exposure limits on instruments where max loss is always known?

A decision-service that evaluates every trade intent and returns Accept, Clip, or Reject with approved size and reason codes. Enforces a hierarchy of caps—per-trade, per-trader, per-market, program-wide—using worst-case risk in USD. Deterministic O(1) exposure management.

EvaluateEnforceAudit
Trade IntentBUY 5,000 YES @ ¢42
Worst-case risk$2,100.00
Trader limit remaining$4,800.00
DecisionACCEPT
Latency< 1ms
User Rating Engine

Behavioral intelligence

How do you distinguish informed traders from adversarial flow before damage is done?

Turns behavioral data into enforceable controls. Produces trust tiers, limit multipliers, and integrity flags. Output plugs directly into RME: max_limit = base_limit × limit_multiplier.

ProfileSignalsActions
Trader IDUSR-38472
Trust tierTIER 2
Limit multiplier0.65x
Win rate (30d)78.4%
EnforcementCLIPPED
Simulation Fill Engine

Execution realism

How do you make evaluation results predictive of live trading performance?

Uses lightweight market snapshots to compute deterministic fills including slippage, partial fills, and price impact. Eliminates simulation-only edge.

SimulationFillsImpact
OrderBUY 10,000 YES
Simulated fill¢56.2
Slippage+1.2¢
Partial fill8,200 / 10,000
Price impact+2.1¢
Execution Balancer Engine

Firm-level inventory control

How do you prevent same-side crowding across your entire book?

Firm-level inventory balancer. Decides execution volume based on aggregate YES/NO inventory across all traders per market, plus policy caps. Prevents concentration risk.

InventoryAllocateExecute
Firm YES exposure$84,200
Firm NO exposure$31,500
Imbalance2.67:1
New YESBLOCKED
New NOOPEN
Real-time Event Risk Index

Correlated exposure mapping

How do you track correlated exposure across thousands of structurally linked events?

Maps logical and structural correlations between event contracts in real time. Captures deterministic relationships—if event A implies event B, the exposure compounds.

CorrelationExposureAlerts
Event clusterUS-ELECTION-2026
Linked events47
Compound exposure$340,200
Risk factorHIGH
Event Risk Compliance Layer

Audit-ready records

How do you produce audit-ready decision records for every enforcement action?

Full compliance infrastructure for event contract risk decisions. Generates deterministic decision records, documented enforcement logic, and complete audit trails.

RecordsLogicExport
Decision IDDEC-20260322-4829
ActionREJECT
Reason codeLIMIT_BREACH_PROG
Audit statusCOMPLETE

Every engine, built from first principles

Event contracts are deterministic instruments. Maximum loss is bounded. Settlement is binary. Correlation is structural, not statistical.

NSEW

Nothing ported from legacy systems

The failure modes are structural, not parametric, and they surface the moment real adversarial flow hits your book.

Read our documentation →
USE CASES04 SCENARIOSUSE CASES

Everything you wanted to know

The Toxic Flow Problem
Prediction Market Operator · URE, RME

A mid-size prediction market operator was absorbing outsized losses from a small percentage of users. URE identified adversarial flow patterns. RME enforced exposure gates before losses compounded. Full decision audit trails.

The Ported Model Problem
Prop Firm · RME, SFE, URE, EBE

A futures prop firm wanted to expand into event contracts. Their existing risk infrastructure was built around stop-loss logic. Full engine stack deployed via API in weeks without touching existing infrastructure.

The Adverse Selection Problem
Brokerage / Fintech · RME, URE, ERCL

A retail brokerage entering event markets had a compliance problem before they had a product. Nevooa's infrastructure served as the compliance-ready control layer. Full audit trails, deterministic decision records.

The Correlated Exposure Problem
Market Maker · RERI, RME, EBE

A market maker quoting across 4,000+ correlated events was managing exposure manually. Real-time correlated exposure tracking. Deterministic position limits across the full book. Zero manual intervention.

THE PROBLEMWHY NEVOOATHE PROBLEM

Why traditional risk infrastructure fails here

Wrong distribution model
Traditional risk models assume continuous, normally distributed prices. Event contracts resolve to exactly 0 or 1. VaR, Greeks, and drawdown models produce meaningless output.
Stop losses don't exist
Stop-loss logic assumes you can exit before max loss. Event contracts have binary, instantaneous settlement. Risk must be managed at the point of entry.
Correlation is deterministic, not probabilistic
In equities, correlation is estimated from price history. In event markets, correlation is logical: "candidate wins state A" and "candidate wins election" are structurally correlated by the rules of the event itself.
Worst-case loss is always knowable
Maximum loss equals the entry price. A genuine structural advantage—but only if your infrastructure enforces deterministic bounds rather than probabilistic ones.
Informed flow is invisible until it isn't
Adversarial participants don't show up in price impact metrics. Detection requires behavioral modeling at the user level, not price signal analysis at the instrument level.

A bit about us

If you operate in event markets, you already know the problem. The infrastructure that powers traditional derivatives was built for instruments that behave nothing like binary-outcome, deterministic-settlement contracts. Porting it doesn't work.

The failure modes are structural, not parametric. We built Nevooa from first principles—purpose-built risk engines for firms managing risk exposure on event contracts. API-first. Deterministic. Built from the ground up.

If you're running exposure in event markets without purpose-built infrastructure, let's talk.