Canyon Code

Canyon Code provides an Observe, Optimize, and Govern platform for enterprises running multi-agentic AI applications at scale. The platform offers workflow-aware model serving to cut avoidable infrastructure spend, application-level observation across workflows, and per-workflow, per-user-persona policy controls for granular AI governance. It is designed for engineering and platform teams needing cost optimization and policy-based compliance management.

Traction –/10

Sunnyvale, United States · HQ

Artificial Intelligence
Enterprise Software
Regulatory & Compliance Technology
Developer Tools
Software Only

Updated 6 days ago

Funding

Funding rounds are not available yet.

Founders

Founder details are not available yet.

Product

Problem

Enterprises deploying multi-agentic AI applications face soaring infrastructure costs and governance gaps. Token-level cost monitoring fails to identify workflow-level inefficiencies, and managing AI at scale without granular policy controls creates compliance and security risks.

Solution

Observe Optimize Govern provides a platform that gives enterprises end-to-end visibility and control over their multi-agentic applications. The Observe capability tracks application behavior across workflows, while the Optimize capability uses workflow-aware model serving to route and schedule workloads intelligently, eliminating waste that token-level infrastructure cannot detect. The Govern capability delivers enterprise-scale policy management with per-workflow and per-user-persona controls, enabling teams to enforce their own governance rules rather than relying on generic guardrails.

Target Audience

Primary customers are engineering and platform teams at enterprises deploying multi-agentic AI applications at scale who need infrastructure cost optimization and policy-based governance.

Features

Workflow-aware model serving with intelligent routing and scheduling to reduce infrastructure waste
Per-workflow and per-user-persona policy controls for granular AI governance
Application-level observation across multi-agentic workflows
Enterprise-scale management designed for team-based AI operations