Orchestra

Companies running AI-driven workflows such as classification, extraction, labeling, and agent-based actions often default to frontier models, which carry high inference costs even when the task does not require that level of capability. Running every task through a frontier model at business-scale volume becomes expensive, and teams lack an easy way to identify which tasks could be handled by a cheaper, more efficient model without sacrificing quality. Orchestra points to a specific case where its optimized model cost $0.000074 per task versus $0.000444 for a frontier baseline, an 83.4% reduction, suggesting this inefficiency is common enough to be worth solving.

Orchestra observes production workflows by capturing traces and expert corrections, then evaluates models by setting a benchmark and testing candidates against held-out examples. It helps teams improve by refining prompts for head-to-head model comparisons or training a new specialist model, and supports deployment once a model meets quality, cost, and latency standards. The specialist models produced through this process are owned by the customer as their own weights, rather than being locked into a vendor's frontier model. Orchestra positions this for action-oriented agents, precision operations requiring valid structured outputs, and enterprise-scale repeated classification, extraction, and labeling tasks.