Self-optimizing neocloud that cuts LLM costs by 100x
Orchestra is an inference cloud. We capture your AI work traces, train smaller models, and automatically deploy them (only when they outperform the expensive model you are currently paying for). Instead of renting the same frontier model forever, your AI infrastructure gets faster, smarter, and cheaper every time it runs. Suppose your product uses Claude for a repetitive legal workflow. Orchestra observes successful production runs, learns the specific tools and judgment required for that workflow, and trains a specialized open-weight model. Once that model matches or beats Claude on a held-out evaluation, Orchestra starts routing production traffic to it. As more work is completed, the model keeps improving. We started Orchestra because we believe every company using AI should be accumulating intelligence, not accumulating API bills.
Founder/CEO
Founder at Orchestra (YC S26). Previously founding Member of Technical Staff (and founding sales guy) at Gumloop, closing ~$2M in first year. Led Ads ML and also consumer AI as first PM at Instacart's Carrot AI. Prior co-founder/CEO at Ones and Zeros (text-to-SQL using transformers). Early at Wildfire (acquired by Google), then 5 years across Google's ads ML and programmatic platforms. SVP Solutions at VideoAmp building TV measurement infrastructure.
Founder/CTO
Founder at Orchestra (YC S26). 10 years at Instacart. Built the ads serving and ML infrastructure, auction platform, and experimentation framework. 2nd time YC Founder. MS in Computer Science from Georgia Tech.
