Canyon Code
Canyon Code provides a Workflow Intelligence Layer for multi-agentic AI applications, giving enterprise engineering teams visibility into compute costs at the workflow level rather than token level. The platform enables optimization through workflow-aware model serving that eliminates GPU scheduling waste, and governance through per-workflow, per-user-persona policy controls. It integrates with agent-building platforms like LangGraph and AutoGen.
- Artificial Intelligence
- Developer Tools
- Enterprise Software
- Software Only
Funding
Founders
Founder details are not available yet.
Product
Problem
Enterprise AI infrastructure has not kept pace with agent-building platforms like LangGraph and AutoGen, which make it easy to create workflows but leave the runtime layer that makes those workflows visible, efficient, and governable largely absent. Engineering teams are operating on instinct rather than insight, lacking visibility into where compute money is actually going and unable to control AI costs or behavior at scale.
Solution
Canyon Code provides the Workflow Intelligence Layer for multi-agentic applications, taking enterprises on a journey toward increasing understanding and control of their AI. At the Observe level, it gives teams $/workflow cost visibility, tracing cost spikes to specific workflows. At the Optimize level, it identifies GPU scheduling waste that token-level infrastructure never sees, using workflow-aware model serving to route and schedule intelligently. At the Govern level, it enables per-workflow, per-user-persona policies that let teams manage AI at enterprise scale, balancing latency and accuracy requirements. The platform helps enterprises see where compute is actually going, optimize it more holistically than token metrics allow, and govern it with precision.
Target Audience
Primary customers are enterprise engineering and platform teams building and operating multi-agentic AI applications at scale, particularly those using agent orchestration frameworks like LangGraph and AutoGen who need runtime visibility, cost optimization, and governance controls.
Features
- Workflow-level cost tracking that traces cost spikes to specific workflows instead of token-level metrics
- Workflow-aware model serving that routes and schedules intelligently to cut avoidable GPU spend
- Per-workflow, per-user-persona policy controls for latency, accuracy, and capacity allocation
- Integration with agent-building platforms such as LangGraph and AutoGen
- Enterprise-scale governance that transforms AI into a strategic asset