Valence's AI instantly finds you the best price across every prediction market exchange so you never overpay on a trade.

Valence's smart order routing engine uses machine learning models trained on real-time order book data from multiple prediction market venues—including Kalshi, Polymarket, and Crypto.com—to determine optimal trade execution paths. The system ingests streaming Level 2 order book snapshots, historical fill rates, latency profiles, and fee structures from each connected exchange, then applies reinforcement learning–based routing algorithms to split and route orders in a way that minimizes slippage, maximizes fill probability, and captures micro-arbitrage when the same event contract is mispriced across venues. The ML pipeline continuously retrains on execution outcome data (actual fill prices vs. predicted optimal prices) to adapt to changing liquidity conditions, venue-specific behavior, and market microstructure shifts. During high-volatility events—such as election nights or major economic announcements—the system dynamically adjusts routing weights and can pre-position liquidity signals to anticipate order book imbalances. This infrastructure is what enables Valence to process over 1 billion contracts and serve as the essential execution layer for professional trading teams who would otherwise need to manually monitor and trade across multiple fragmented interfaces.

It's like having a travel agent who instantly checks every airline, finds the cheapest flight, and books it for you before the price changes—except for prediction market trades happening in milliseconds.

Valence uses AI to crunch data from every prediction market and news source to tell you which bets are most likely underpriced before the crowd catches on.

Valence's predictive analytics engine builds ensemble machine learning models that fuse pricing signals from all connected prediction market venues with external data streams—including real-time news APIs, social media sentiment (Twitter/X, Reddit), economic indicator releases, polling data, and historical event outcome patterns. The system uses a combination of gradient-boosted trees (XGBoost/LightGBM) for structured feature-based forecasting and transformer-based NLP models for parsing and weighting breaking news and social sentiment in real time. A meta-learning layer calibrates the ensemble outputs against actual market-implied probabilities and realized outcomes, producing a continuously updated "fair value" estimate for each contract. When the model detects statistically significant divergence between its fair value estimate and current market prices across any venue, it surfaces these as actionable trading signals to users—ranked by expected edge, confidence interval, and time sensitivity. The system also powers Valence's analytics dashboards, including whale tracking (detecting large positional shifts by sophisticated wallets), market momentum indicators, and scenario analysis tools that let institutional users stress-test portfolios against correlated event outcomes. This capability transforms Valence from a pure execution layer into an intelligence platform, increasing user stickiness and positioning it to offer premium strategy subscription tiers.

It's like having a sports analyst who watches every game, reads every stat sheet, and whispers in your ear which team the oddsmakers are underrating—except it does this across thousands of real-world events simultaneously.

Valence lets you set up AI trading bots that automatically buy and sell prediction market contracts across every exchange based on your chosen strategy—so you can trade while you sleep.

As AI agents account for over 30% of wallets on platforms like Polymarket, Valence is building dedicated infrastructure to let users—from retail hobbyists to professional quant teams—deploy autonomous trading agents through its unified platform. The agent infrastructure provides a managed runtime environment where users can configure pre-built strategy templates (e.g., mean-reversion arbitrage, momentum following, news-reactive hedging) or upload custom agent logic via Valence's developer APIs. Each agent operates within a sandboxed execution environment with defined risk parameters (position limits, drawdown stops, venue allocation rules) and connects to Valence's smart routing layer for multi-venue execution. The ML component powers the pre-built strategy templates themselves—using reinforcement learning agents trained on historical prediction market data to learn optimal entry/exit timing, position sizing, and cross-venue allocation under varying market regimes. A monitoring layer uses anomaly detection models to flag agent behavior that deviates from expected performance envelopes, automatically pausing agents that trigger risk thresholds. Valence plans to layer on a strategy marketplace where top-performing agent configurations can be shared or sold as subscriptions, and a copy-trading feature where users can mirror the positions of high-performing agents or human traders. This positions Valence not just as a trading terminal but as the operating system for autonomous prediction market participation—a critical differentiator as the sector shifts toward algorithmic and agent-dominated trading.

It's like setting up a fleet of self-driving cars that each know a different shortcut through the city—you pick your destinations and risk tolerance, and they handle all the driving across every road simultaneously.