Actualyze AI has emerged from stealth with $7 million in seed funding and launched an enterprise platform designed to govern, secure, operate, and optimize AI model usage through a common infrastructure layer.
The company is backed by Storm Ventures, Canaan Partners, Morado Ventures, and Jerry Yang’s AME Cloud Ventures.
Actualyze sits between an organization’s employees, applications, AI agents, and the AI models they access. The platform is designed to give companies a governed pathway through which every AI inference request can be identified, inspected, authorized, routed, tracked, and attributed to the appropriate budget.
The approach addresses a growing enterprise challenge as companies use more models and AI agents. While conventional API infrastructure can authenticate and count requests, Actualyze argues that enterprises need additional visibility into prompts, potential sensitive-data exposure, model selection, and which teams or applications are generating AI expenses.
Actualyze can associate every request passing through it with the user, team, or application initiating it. The platform then applies access controls and security checks, assigns costs to the appropriate budget, records the interaction, and routes the request to an eligible model provider.
Actualyze organizes its platform around four functions: governance for access and spending policies, security through inference scanning and guardrails, operations around model deployment and performance, and optimization through intelligent model routing based on capability, cost, and quality.
The company’s hosted platform is available in Early Access through its Design Partner Program.
Actualyze was founded by CEO Rafi Khardalian and CTO Sean Lynch, who previously founded managed private cloud company Metacloud, which was acquired by Cisco.
KEY QUOTES:
“It’s clear that AI has become a new layer of the enterprise stack. A model call looks like any other API request, a key, an SDK, an invoice at month’s end, but the resemblance is the trap. The systems that govern the rest of an enterprise’s request traffic can authenticate a model call and count it, but not read the prompt inside it, know if data is leaking, know the best place to route it, or charge the call to the team behind it. So anyone with a key can call a model, and all that spend pools into one bucket with no visibility into who spent it or accountability for it. Agents raise the stakes, fanning a single task into dozens of autonomous calls. That’s the problem we built Actualyze to solve”
Rafi Khardalian, CEO and Co-Founder of Actualyze AI
“You bet on a market this big by betting on the people who’ve already tamed one like it. We built Actualyze because enterprises need one governed path for every AI request, and this is the team that’s spent a career building the infrastructure entire businesses run through. The market is ready, and so are we.”
Sean Lynch, CTO and Co-Founder of Actualyze AI
