Context Evals
Every correction makes the next output better.
Your domain experts set the quality bar. Context measures every output against it, learns from corrections, and improves automatically. No labeling teams. No synthetic data.
EXPERT RUBRICS
Quality standards that train the system
Rubrics built from accepted work, not academic benchmarks. Finance tracks compliance accuracy. Legal tracks citation fidelity. Engineering tracks diagnostic correctness. Each rubric becomes a reward function for continuous optimization.
REGRESSION DETECTION
Catch degradation immediately
Every run is evaluated against rubrics automatically. When a runbook change, model update, or context shift degrades output quality, the system catches it. No weeks of silently degraded output.
CONTINUOUS LEARNING
Corrections become training signal
Accepted outputs become golden examples. Corrections produce structured preference data. The system proposes concrete improvements: updated runbook steps, reweighted retrieval, refined context. All validated against held-out traces before deployment.
CONFIDENCE-GATED ROUTING
Mature workflows get cheaper automatically
As rubric scores stabilize, the system routes proven tasks to faster, more efficient models, reducing inference cost 10–20x while maintaining quality thresholds. One enterprise deployment saw compute costs drop 59% in four months.
PROPRIETARY MODELS
Proprietary enterprise models
Once traces and rubrics reach critical mass, train domain-specific models calibrated to your procedures, exceptions, and decision criteria. Owned by your enterprise, versioned with full lineage, deployable on your infrastructure.
Deploy where you need it
Fully managed cloud, private VPC, or air-gapped on-premises. Wherever your security requirements demand.
Request a demo →Compliance & Security
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