Orchestra

Orchestra is a platform that evaluates a team's production AI work and then improves the models behind it. It captures traces and expert corrections from live workflows, tests each model against held out examples, and either refines prompts to compare models head to head or trains a specialist model for a recurring task. The vendor states that customers always own the weights of the specialists it trains. It is aimed at teams running repeated, high volume AI tasks such as classification, extraction, labeling, structured outputs and action oriented agents, where cost and latency matter as much as quality. The workflow runs in four steps: observe, evaluate, improve and deploy, with a new model shipped only when it meets quality, cost and latency standards and a fallback retained. Existing OpenAI or Anthropic SDK code can be pointed at the Orchestra gateway by swapping the API key and base URL, and provider or managed model access is configured in the app. Orchestra generates benchmarks specific to a customer's task, for example checking whether a model updated the right record or followed a policy, rather than relying on generic leaderboards. Its published case study claims lower inference cost per task than a frontier baseline. Pricing is not published. The site offers a sign in to the app and a route to talk to the team.