Naïve raises $28.5M for AI infrastructure automating company setup and operations

Developers have long sought ways to eliminate repetitive work, and Naïve is taking that impulse further—offering infrastructure that enables AI agents to handle much of the labor involved in starting and operating a business. The company has already attracted more than 30,000 developer customers since its recent launch.

What Naïve Does#

The platform consolidates business essentials—payments, email, phone numbers, cloud infrastructure, storage, and company incorporation—behind a single API layer. Developers can feed a prompt to tools like Cursor or Claude Code, which then connect to Naïve's APIs to provision all the foundational pieces needed to launch.

The system can handle LLC formation across U.S. states (with humans still required for KYC/KYB verification and payment processing). Beyond incorporation, AI agents can autonomously set up email accounts, issue virtual cards, provision phone numbers, configure databases, allocate computing resources, and integrate with services such as Stripe and QuickBooks. Naïve also includes a governance layer to enforce budgets, limit agent actions, and require human approval for sensitive operations.

Early Adoption and Use Cases#

The traction has been striking. CEO and co-founder Sean Dorje disclosed that the company has grown annual run-rate revenue 10x to the low double-digit millions over six months. Real-world customers are using the platform in ways both practical and unconventional—from AI-powered automation agencies and faceless social-media channels on TikTok and YouTube to fully autonomous rental-car operations. In one instance, Naïve's infrastructure powered a TikTok account posting AI-generated animal videos.

The Broader Opportunity#

While business setup tools grab initial attention, the larger challenge is cost efficiency. Running fleets of AI agents that repeatedly call expensive models, manage large context windows, and idle between tasks creates mounting expenses. Naïve is channeling its new capital into infrastructure to tackle this problem directly—building a model router to optimize which AI service handles each query, a memory layer to store and retrieve business context, and a serverless runtime that runs agents in lightweight JavaScript environments rather than full virtual machines.

This shift toward cost management may ultimately prove more valuable than the initial setup toolkit. Enterprise customers could find particular value in reducing the operational overhead of running multiple agents at scale.