Orchestra

Evaluate production workflows and train custom specialist models to cut inference costs

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You can use Orchestra to observe production workflows, capture live traces, and evaluate machine learning models against benchmark datasets. The system collects expert corrections directly from daily operations to benchmark model performance head-to-head. You can refine prompts or train dedicated specialist models that your team completely owns, bypassing reliance on expensive frontier models. Once your custom models meet specific standards for quality, latency, and operational expense, you can ship them to handle high-volume data classification, extraction, and structured output tasks across your organization.

What Orchestra does

Capture live execution traces from production applicationsBenchmark candidate models against held-out test datasetsTrain custom specialist models on proprietary team dataCompare prompt revisions head-to-head across modelsDeploy validated model checkpoints to production environmentsMonitor inference expenses and response latency metricsGenerate structured data outputs for enterprise workflowsCaptures execution traces and human expert corrections from live systemsTests models against held-out benchmark examples to measure qualityCompares model outputs head-to-head against frontier baselinesTrains and fine-tunes custom specialist models with full weights ownershipLowers inference expenses compared to major frontier modelsValidates structured data outputs for automated workflowsMonitors latency and accuracy thresholds before deployment