SUNNYVALE, Calif., May 26, 2026 (GLOBE NEWSWIRE) -- Today Canyon Code emerged from stealth to enable granular controls on multi-agentic apps. Enterprises can now decide on various aspects of their apps on a per-app basis: what should each app optimize, how should that depend on the identity of the user, how should app access be controlled, when should it get updated and how should it be secured. Enterprises who are deploying multi-agentic apps are looking to inferencing them as efficiently as possible. Improving efficiency at the app level requires a new layer above the existing model serving layer which can only optimize at the model level. This layer will make it possible for enterprises to optimize the apps not just for cost but also for latency and accuracy.  Ravikiran Gopalan, a third time founder, previously built enterprise-grade multi-agentic apps and saw the nascent need for app-level optimization, which has since hyper-accelerated in the last year. This led him to cofound Canyon Code with Professor Aditya Akella, who is broadly recognized for research in ML systems and operating systems. "Enterprises are crossing the dependability-thresholds with agentic systems and are beginning to deploy more and more multi-agentic apps at scale. However, they don’t have an easy way to set policies of behavior for these apps on a per-app and per-persona basis. Canyon Code’s technology will allow them to do exactly that,” said Ravikiran Gopalan, co-founder and CEO of Canyon Code.  Canyon Code is building an enterprise-grade workflow intelligence layer that observes the dependence between multi-agentic apps and the LLM calls made by the individual agents. By maintaining this dependence graph and tracking the progress of each of the agents, the layer provides additional context to the model serving layer and affects the scheduling and orchestration of the LLM calls and management of its contextual memory. This allows the layer to manage the overall app towards optimizing the metrics set by its enterprise owner. Canyon Code Raises $5 Million in a Pre-Seed Round Today Canyon Code also announced that it raised $5 million in pre-seed funding led by Cota Capital. Newbuild Venture Capital and Blackhorn Ventures also participated. Canyon Code will use this money to build the workflow intelligence layer and hire across R&amp;D. "Canyon Code goes beyond simple GPU optimization to build application-aware GPU optimization — understanding how models, agents and infrastructure interact across a multi-agentic application to optimize the whole rather than the parts,” said Aditya Singh Partner, Cota Capital. “We led because the next durable layer in AI infrastructure will be built around workflow-aware execution.” About Canyon Code Canyon Code gives enterprises the controls to optimize, manage and govern their multi-agentic apps at scale. The company is backed by Cota Capital, Newbuild Venture Capital and Blackhorn Ventures. For more information, go to: https://canyoncode.ai. Media and Analyst Contact: Amber Rowland amber@therowlandagency.com +1-650-814-4560 <img alt="" src='https://ml.globenewswire.com/media/MGZiNTVmMjYtZDhjMC00Mzg5LWFjMDYtZDJjY2ZjNWI3YzYzLTUwMDE2MzE1OC0yMDI2LTA1LTI2LWVu/tiny/Canyon-Code.png' referrerpolicy='no-referrer-when-downgrade' />

Canyon Code Launches to Give Enterprises the Controls to Optimize, Manage and Govern Their Multi-Agentic Apps at Scale With $5 Million in Pre-Seed Funding

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