Backed by Prosperity7 Ventures, the “super agent” startup — has raised $25M in under a year and hit 200,000 users by turning AI conversations into autonomous apps.
Most AI startups pitch a better model. 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.
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.”
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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.”
Execution as moat
Cheng has an unusual take on strategy for a startup CEO: he doesn’t think it matters much right now.
“The pace of AI development is so staggering that any direction that looks promising can be recognized and replicated by competitors tomorrow,” he says. “A purely conceptual product direction is no longer a defensible advantage.”
Instead, he points to an analogy from hardware manufacturing. “Why has it been so difficult for competitors to surpass China in hardware manufacturing? It isn’t a conceptual lead in design. It’s a massive, structural efficiency advantage in the supply chain. For AI startups, there is only one true strategic advantage: who can adopt AI first, internalize it, and use it to multiply their own efficiency by 100x.”
Creao AI practices what it preaches. The company runs its own operations — influencer marketing, SEO pipelines, content production — on CREAO itself. A 20-person team operating at a capacity that would traditionally require multiples of that headcount.
One agent replaced a three-person SEO workflow overnight — keyword research, copywriting, page design, deployment. Another ran a content pipeline for two days before anyone noticed the output was garbage. Both happened in the same week. The team calls themselves “crash test dummies for the future of work.” It’s not entirely a joke.
“Every internal failure becomes a product insight,” Cheng says. “Every broken workflow becomes a feature spec. We have to be the ultimate AI-driven company internally before we can sell that reality to the world.”
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Why Prosperity7
Prosperity7 Ventures, the diversified venturing fund of Aramco Ventures, has been building a portfolio of artificial intelligence companies, including enterprise-focused investments like Arcee AI and Spirit AI. Creao AI aligns with the fund’s strategy of backing scalable platforms with global reach across the world.
“As AI adoption matures, the focus is shifting from models to execution. Creao AI is building the infrastructure layer that allows agents to operate autonomously, reliably, and continuously. We believe platforms that enable this transition will play an important role in the next generation of AI systems.” Said Raed Twaily, Executive Managing Director, Prosperity7 Ventures
A red-hot market — and a different bet
The AI agents market is projected to hit $52 billion by 2030, growing at over 46% annually. And the capital flooding in reflects it.
Manus, the general-purpose AI agent that topped the GAIA benchmark, was acquired by Meta for $2 billion — validating that autonomous AI agents are worth platform-level investment. Genspark hit unicorn status with a $275 million Series B at a $1.25 billion valuation, racing to replace Microsoft Copilot with an AI workspace that reached $50 million in ARR just five months after launch. Last week, Gumloop closed a $50 million Series B from Benchmark for its no-code, drag-and-drop agent builder targeting enterprise automation. And Relevance AI raised $24 million to build what it calls an agent “operating system” for pre-configured enterprise teams.
Creao AI rides the same wave but makes a fundamentally different bet. Where Gumloop asks users to wire together visual workflows on a canvas and Relevance offers pre-built agent teams, CREAO skips the builder interface entirely. There’s no canvas, no drag-and-drop, no node editor. You talk, the agent executes, and the result becomes infrastructure.
“Everyone else is building better ways to instruct AI,” Cheng says. “We’re building the layer where AI work compounds — where every run feeds memory into the next, and successful work becomes an app that runs itself.”
Where Manus proved that a single powerful agent can accomplish complex tasks, CREAO bets the next step is making that power persistent and repeatable. And where Genspark built a workspace for knowledge workers, CREAO’s workspace is designed for orchestration — not using AI tools, but managing autonomous agents that use tools on your behalf.
CREAO’s 200,000 users arrived entirely through organic adoption since the September 2025 launch, without paid marketing spend. The new funding will go toward expanding the engineering team, deepening enterprise integrations, and scaling agent-to-agent collaboration.
