Building a Real-Time Consensus Engine for Prediction Markets — Architecture Deep Dive

Prediction markets are now a $1.3 trillion industry. But if you want to know the consensus probability on an event — a basketball game, a Fed rate decision, an election — you have to manually check a dozen different platforms. I built Meridian Edge to solve this. It's a REST API that aggregates prediction market data from regulated sources and computes a real-time consensus probability for each event. This post walks through the architecture. The problem Prediction market data is fragmented. Each platform has its own API (if it has one at all), its own data format, and its own pricing. There's no unified view. If you're a researcher studying probability calibration, a developer building an AI agent that needs real-time event probabilities, or an analyst covering multiple categories — you're stuck doing manual work. The data pipeline The system processes data in three stages: Stage 1: Collection Snapshot jobs run every 14 seconds, pulling current state from each tracked source. We norma