ORO AI

Train and evaluate autonomous commerce agents in simulated economic environments

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You can use ORO AI to train, test, and benchmark autonomous artificial intelligence agents within simulated commerce and economic environments. The system evaluates how models spend money, manage inventory, and handle dynamic buyer contexts across specialized benchmarks like ShoppingBench and AssistantEval. Researchers and developers can access millions of scored decision trajectories, run human-in-the-loop autoresearch competitions, and analyze agent behavior across changing stock and delegated service tasks. It provides verifiable performance tracking to measure how effectively models make complex financial and shopping decisions.

What ORO AI does

Benchmark autonomous commerce agents against standardized shopping tasksScore decision trajectories from reinforcement learning modelsSimulate dynamic pricing and fluctuating store inventoryTest delegated purchasing actions in sandboxed environmentsEvaluate model spending habits across complex product catalogsSubmit agent models to open-source competition leaderboardsRequest custom evaluation samples and research dataSimulated commerce environments for testing decision-making modelsShoppingBench evaluation suite for product and pricing decisionsORO Bench for testing buyer context and fluctuating inventoryAssistantEval for measuring delegated actions and service interactionsHuman-in-the-loop autoresearch tracking model hillclimbingExtensive trajectory database recording agent decision pathsCompetitive leaderboards tracking miner and validator performance