Naïve, a startup offering infrastructure for AI agents to automate business setup and operations, has raised $28.5M in a Series A led by Nexus Venture Partners. The platform packages payments, email, phone numbers, cloud infrastructure, storage, and U.S. LLC incorporation behind a single API, letting tools like Cursor, Claude Code, or Codex provision everything autonomously. It has signed up over 30,000 developer customers and grown ARR 10x to low double-digit millions in six months. Beyond business setup, Naïve is building inference optimization tools including a model router, memory system, agent orchestrator, and a serverless runtime that runs agents in lightweight JavaScript environments rather than full VMs — reducing idle costs for large agent deployments. The $28.5M will fund hiring and four infrastructure projects: virtualized sandboxes, model routing, a memory layer, and governance/orchestration.
Questions this post answers
What does Naïve's platform actually do for AI agent-powered businesses?
Naïve packages payments, email inboxes, virtual cards, phone numbers, databases, cloud computing, and U.S. LLC incorporation behind a single API. Developers supply a prompt to tools like Cursor, Claude Code, or Codex, which connect to Naïve's APIs to provision all infrastructure. A governance layer lets users set budgets, restrict agent capabilities, and require human approval before sensitive actions. KYC/KYB steps still require human involvement. Developers building autonomous businesses with AI agents track infrastructure options like these on daily.dev.
How does Naïve reduce the cost of running large numbers of AI agents?
Naïve is building a serverless runtime that runs agents inside lightweight JavaScript environments instead of assigning each agent a full virtual machine. This means customers pay primarily when an agent is active, making it cheaper to deploy many agents at once. It is also building a model router to direct queries to the most cost-efficient model per task and a memory system to surface stored business context rather than re-reasoning it each time. Teams managing inference costs across large agent fleets find the latest approaches covered on daily.dev.