AI-Driven Dispute Resolution
for Prediction Markets
The Challenge
Prediction-market platforms such as UMA and Polymarket thrive on fast, bias-free resolution of event outcomes. Manual reviews were slow, expensive, and occasionally contentious—limiting liquidity and user trust.
Our Approach
Updoot built an Agentic Resolution Engine™, a self-auditing AI ensemble that:
- 1.Harvests multi-source evidence in real time.
- 2.Runs hybrid language-model juries to reach probabilistic verdicts.
- 3.Continuously A/B tests model variants in a closed-loop experimentation lab—deploying the best performer without exposing proprietary prompts or heuristics.
The result is near-perfect accuracy at micro-pricing, delivered in seconds.
Solution Architecture
Evidence Graph Builder
Cloud crawler that transforms raw web data into structured fact graphs.
Resolution Core
Ensemble of foundation models and lightweight heuristics orchestrated by a meta-controller; plugs in GPT-4o-mini or fully open-source LLMs.
Explainability Layer
Generates human-readable rationales & confidence scores for every verdict, enabling audit and governance.
Business Impact
- Market Efficiency — faster verdicts increase settlement velocity, boosting trading volumes and fee revenue.
- Cost Compression — automated reviews cost < 1% of manual arbitration.
- Liquidity Magnet — trust scores and transparent rationales attract professional traders and deeper liquidity pools.
- Scalability — system parses thousands of simultaneous markets across Politics, Sports, Crypto, and Science with no performance cliff.
What This Means for You
Whether you run a prediction market, an oracle network, or a compliance desk, our experimentation-driven AI resolution framework can cut operating costs while raising decision trust to institutional grade.
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