The participant
An honest number, explained plainly.
Clean cross-venue probabilities, a plain-language resolution rule, and a signal that the market has real liquidity behind it.
About
The infrastructure layer prediction markets were missing.
Billions in volume flow through fragmented platforms every month. The data exists. What is missing is the layer that makes it usable at institutional scale. Three problems define that gap.
01 / The fragmentation problem
Scattered across venues, normalized nowhere.
Prediction market data lives across Polymarket, Kalshi, Manifold, and dozens of emerging platforms. Each venue has its own contract structures, resolution frameworks, fee models, and data formats. No single layer normalizes, correlates, or structures this data for professional use. Every institution that wants to take prediction markets seriously ends up rebuilding the same plumbing from scratch.
02 / The intelligence gap
Abundant data. Scarce signal.
Serious traders lose significant time to manual cross-platform research. Screens full of tabs. Spreadsheets stitched together by hand. Arbitrage opportunities that vanish in the time it takes to reconcile two venues. The venues solve for execution, as they should. They do not solve for the cross-venue read, the liquidity-aware comparison, or the calibration study. That work falls to users.
03 / The infrastructure opportunity
Every maturing market needs its integration layer.
Equities got Bloomberg and Reuters. Crypto got Chainalysis, Kaiko, and Coin Metrics. Each built quiet, durable infrastructure that made the asset class legible to professionals. Predictive Labs is building the normalization engine, the API, and the command center for that data layer.
The market
Prediction markets are past the toy phase.
Volumes have crossed the threshold where serious capital pays attention. The stack now builds itself out in a predictable order: venues first, then data, then analytics, then derivatives. What is emerging is the native, on-chain and regulated-venue edge of event-driven finance, and it needs the same layer equities and crypto eventually got.
Who we serve
One intelligence layer. Many readers.
Prediction market data matters to anyone trying to answer a single question: what does the market actually think? The answer is more useful the less each reader has to reassemble it themselves. Predictive Labs sits one layer beneath all of them.
The trader
Edges that survive venue boundaries.
Cross-venue arbitrage candidates, normalized fee-and-spread math, and a read on which venue is leading price discovery right now.
The developer
One API. One data model.
A single, normalized surface across every venue, instead of nine venue-specific integrations to build and maintain.
The fund
A data layer event-driven desks can act on.
Event-level canonical prices, correlation structure between markets, and an audit trail built for institutional review.
The press
Real-time probabilities with provenance.
Live signal from an election, a Fed decision, or a macro event, with citation-grade sourcing. Not a screenshot of whatever venue happened to be open in a tab.
The AI
Structured intelligence a model can consume.
Machine-readable signal with consistent schemas across venues and events, ready to ingest into a downstream model or inference pipeline.
Same infrastructure. Different surfaces. The work of reconciliation, weighting, and validation happens once, at our layer, so every reader gets the same honest read on what prediction markets are saying.