Built by engineers who ran multi-agent systems in production
We watched orchestration logic sprawl from one function to several thousand lines of imperative retry handling, hardcoded model names, and hand-rolled budget arithmetic. That code breaks differently every time. We built OrchVynt so engineering teams don't have to keep rebuilding it.
Make production multi-agent AI controllable
Enterprise AI systems are moving from single-model APIs to multi-agent workflows. The tooling hasn't kept pace. Engineers re-implement routing logic in every application, budget enforcement in every service, HITL gates in every workflow. That's not sustainable.
OrchVynt's thesis: orchestration is infrastructure. It belongs in a dedicated control plane, not scattered through application logic. When you extract it, you get consistency, visibility, and the ability to change policy without redeploying your agents.
The people building OrchVynt
Previously built and operated LLM inference infrastructure handling high-volume production traffic. Led platform engineering at a large-scale AI deployment org before co-founding OrchVynt.
Distributed systems background. Spent several years building observability and control plane software for microservice architectures. Brought that infrastructure thinking to the AI orchestration layer.
Product background spanning developer tooling and compliance-adjacent SaaS. Focused on making complex orchestration configuration legible to the engineers who actually run production workflows.
What guides the way we build
Infrastructure thinking
Orchestration is a platform concern, not an application concern. We build it the way infrastructure engineers build reliability systems — with explicit APIs, structured telemetry, and no magic.
Declarative over imperative
Routing policy and budget rules should be declared in config, not encoded in code. When behavior lives in YAML, you can version it, audit it, and change it without a deploy.
Observable by default
Every product decision defaults to maximum observability. Teams should be able to answer "what routing decision was made on that invocation?" from telemetry — not from memory.
Doesn't touch agent code
Changing the control plane should never require changes to your agents. OrchVynt is a middleware layer — add it, remove it, or reconfigure it without your agents knowing.
What OrchVynt is not
We are not a model provider. OrchVynt does not host or run language models — it routes to the models you already use via your existing API keys.
We are not training infrastructure. OrchVynt has no fine-tuning, RLHF, or dataset management surface. If you need those, look at dedicated ML training platforms.
We are not a chatbot builder. OrchVynt is infrastructure that runs underneath your AI applications — not a tool for building end-user chat interfaces. Your product team won't use OrchVynt; your platform engineering team will.
We're building in the open
We write publicly about what we're learning — in our engineering blog, in our docs, and in direct conversations with the teams using OrchVynt.