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Closed-loop RLHF with regret analysis, case memory, and automatic policy reinforcement. Every verdict teaches the next one.
Five stages that continuously improve decision quality — automatically.
Each decision captures the scenario, context, domain, risk factors, model reasoning, and verdict — forming the foundation of the learning corpus.
Every tool you need to make your AI decision-making measurably better over time.
Export SFT and DPO datasets directly from your decision history. Fine-tune models on your organization's judgment patterns.
Track and rank agent performance across decision quality, risk calibration, and outcome alignment. Identify your best and worst-performing agents.
A single score (0–100) that reflects your AI decision quality. Based on risk calibration accuracy, feedback alignment, and outcome correlation.
Before you decide, see the probability of regret, success likelihood, and risk forecasts based on similar historical decisions.
Are your risk thresholds too tight or too loose? Domain-by-domain calibration analysis with automatic adjustment recommendations.
Compare decision quality, success rates, and latency across model providers. Get automatic promotion/demotion recommendations.
100 free decisions to see the engine learn. OmegaGrade starts improving from decision one.