Naïve has raised $28.5 million to automate the setup and daily operations of a company. The startup, which allows AI agents to handle the bulk of business administration, signed up over 30,000 developer customers within months of its launch.
The setup process
The company packages the assembly of payments, email accounts, phone numbers, cloud infrastructure, storage, and company incorporation behind a single API. Developers send a prompt to tools like Cursor, Claude Code, or Codex. These tools connect to Naïve’s APIs to provision the necessary infrastructure.
An agent can orchestrate the formation of a U.S. LLC. It supplies details such as the state, industry code, business description, and proposed names. Users must still complete KYC and KYB processes and make any required payments. The rest of the setup, including email inboxes, virtual cards, phone numbers, databases, computing resources, and connections to services like Stripe and QuickBooks, is handled by AI.
A governance layer helps users set budgets, restrict agent capabilities, and require human approval before sensitive actions occur. The platform also provides templates for AI SEO, full-stack SaaS apps, recruiting, accounting, customer support, and a mobile emulator. This emulator lets agents operate smartphone apps on emulated devices.
Funding and growth
Naïve has scaled its annual run-rate revenue by 10x to the low double-digit millions over the past six months, according to CEO and co-founder Sean Dorje. Nexus Venture Partners led the Series A round. TechCrunch has learned of the deal exclusively. Other participants included Y Combinator, Zetta, Liquid 2, and angel investors Gokul Rajaram, Apollo.io co-founder Tim Zheng, and former HubSpot COO JD Sherman.
Dorje says customers use the tool for autonomous businesses. These include AI automation agencies, online content channels on TikTok and YouTube, and a rental car agency. One customer runs an entire rental-car agency without human intervention. Another channel posted AI-generated videos of cats and dogs dancing and boxing.
The fastest growing segment is AI automation agencies. Many founders start by selling agents to other small businesses. Dorje notes that running an autonomous company makes inference costs the biggest line item. Consequently, demand for inference and serverless agents is the highest growing area.
Cost efficiency
Naïve is using the capital to build infrastructure that makes agent loops more efficient. It is developing a model router to send queries to the most efficient model for a task. This preserves and replays already reasoned data. A memory system stores and surfaces business context as agents need it. An orchestrator divides work among agents.
The company is also building a serverless runtime. This runs agents within lightweight JavaScript environments rather than assigning each one a complete virtual machine. Customers pay primarily when an agent is active. This approach reduces the cost of deploying large numbers of agents.
While the autonomous company toolkit is in high demand, Dorje says optimizing inference costs is one of its fastest growing sources of demand. Dorje added that enterprises are showing interest, though he did not name any.
Developers may initially use Naïve to deal with the tedium of setting up a company. As they grow, they may care more about reducing the recurring cost of operating a horde of agents. Enterprises with established businesses may find value in that front too.
Naïve currently has 10 full-time employees. Dorje says the Series A proceeds will be used to hire researchers. The company will develop four infrastructure projects: virtualized sandboxes for agents; model routing and inference optimization; a memory layer; and governance and orchestration. The funding brings the company’s total capital raised to roughly $32 million.
What it means
The new funding supports a shift from simple automation to cost management. As businesses rely on larger numbers of agents, the expense of running them becomes the primary concern. Naïve is positioning itself to solve the financial bottleneck of keeping these systems active.
