Creao AI is pitching a better loop. The startup just closed a $10 million round led by Prosperity7 Ventures – the $3 billion diversified venturing fund of Aramco Ventures – bringing total funding to $25 million across three rounds in under a year. The bet: that the AI industry’s real bottleneck isn’t intelligence. It’s the gap between a chatbot answering a question and an agent actually running your work while you sleep.
“Everyone knows AI promises an explosion of productivity,” says CEO Kai Cheng, who spent a decade building production AI systems for over 250 enterprise clients before co-founding Creao AI. “But the industry is stuck in two traps. If humans still operate AI tools step-by-step, productivity hits a ceiling. And if humans are still the only ones building the tools, the real AI revolution hasn’t even started.”
Creao AI’s answer is a closed-loop system where AI does both: builds the tools and runs them.
How it works
CREAO starts as a conversation. Users describe a task to what the company calls a “super agent” – a cloud-based AI that doesn’t just answer questions but executes. It writes code, calls APIs, connects integrations, and delivers results in a sandboxed environment.
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The twist is what happens next. Successful work gets saved as a reusable Agent App – a persistent, schedulable unit of automation with its own memory. That SEO pipeline you ran on Tuesday? It runs again on Friday. Automatically. Without you.
CREAO’s product architecture has three layers:
- The Coding Agent — AI builds the tools. Users create Agent Apps through conversation, no traditional coding required. This is what the team calls breaking the “builder bottleneck.”
- Autonomous Execution — AI uses the tools. Agent Apps run on schedule, trigger workflows automatically, and chain tasks without manual follow-up. This breaks the “operator bottleneck.”
- The Workspace — Humans orchestrate. A persistent environment where Agent Apps live, where memory accumulates across runs, and where one person can manage what previously required a team.
“If AI builds a tool but a human still has to click ‘run’ every time, we haven’t won,” Cheng says. “The whole point is a closed loop: AI builds it, AI runs it, humans steer.”
Five pivots to get here
The founding team didn’t start with this clarity. They earned it through failure.
Creao AI launched in September 2025 as a vibe-coding tool. By December, they’d scrapped it and rebuilt CREAO around the agent-app model. But that pivot was just the latest in a series. Since forming in mid-2024, the team cycled through synthetic data, workflow builders, and natural-language coding before the super-agent thesis clicked.
“We kept thinking the problem was one thing – data, workflows, code – and every time, the real problem was one level deeper: how humans and agents actually work together,” says co-founder Clark Gao, who runs GTM and previously built data teams at LinkedIn and Tencent.
What makes the pivot story unusual is the team’s pedigree. CTO Peter Pang was a research scientist on Meta’s Llama 3 team — one of the most influential open-source AI projects in history – and before that worked on multimodal models at Apple. He brought a conviction the others initially thought was dramatic: “This is the very beginning of a new evolution. The velocity will be faster than anyone can anticipate.”
Pang has since built Creao AI’s engineering culture around an AI-first philosophy that he argues increases the value of engineers rather than diminishing it. “AI does not reduce the value of engineers – it changes where that value sits,” Pang wrote in a recent company memo. “The value of an engineer is no longer measured by how much code they type. It is measured by clarity of thinking and quality of decisions.”
SOURCE: Businesswire