Naïve Raises $28.5 Million to Build the Plumbing for AI-Run Companies

Updated August 7, 2026 6:24 pm

Naïve has raised $28.5 million to expand software that helps developers use AI agents to set up and operate companies, a bet that the next wave of automation may move beyond coding tasks and into the mechanics of running a business. The startup says more than 30,000 developers have already signed up, and it is now channeling new funding into tools designed to make agent-driven businesses cheaper, safer and easier to manage.

The Series A, led by Nexus Venture Partners, lifts Naïve’s total funding to about $32 million and arrives after the company says its annual recurring revenue grew tenfold in six months to the low double-digit millions. For a young startup serving the fast-moving AI developer market, that combination of user growth and revenue acceleration suggests a product that has found immediate demand — even if the long-term opportunity may be much larger than simply helping people form an LLC.

What Naïve actually does

Naïve is building infrastructure that lets AI agents do much of the administrative work involved in launching and operating a business. The company packages a wide range of setup tasks behind a single API, so developers can plug the system into coding tools and have agents coordinate the necessary services.

According to the company, that stack can include company formation, payments, email accounts, phone numbers, cloud infrastructure, storage, databases, and connections to business software such as Stripe and QuickBooks. In practical terms, Naïve is trying to remove the repetitive setup work that usually sits between an idea and a functioning company.

How the workflow is designed

The startup provides prompts that developers can feed into agentic tools such as Cursor, Claude Code or Codex. Those tools can then call Naïve’s APIs to provision business infrastructure and carry out a large share of the setup process.

For incorporation, the system can help assemble the information needed to form a U.S. limited liability company, including the state, industry code, business description and a shortlist of proposed names. Users still have to participate in identity verification and complete required payments, so the process is automated but not fully hands-off.

That distinction matters. Naïve is not claiming to replace all human involvement in company formation. Instead, it aims to reduce the number of manual steps and the time required to move from planning to execution.

CEO and co-founder Sean Dorje said the company’s customers are using Naïve to launch autonomous businesses ranging from AI automation agencies to faceless content operations on TikTok and YouTube, and in some cases even a rental car business.

Why investors are paying attention

Naïve’s fundraising reflects the speed at which developers are adopting AI tools that reduce busywork. The startup says more than 30,000 developers have signed up within months of launch, a figure that signals strong early interest in business automation tools built for agentic workflows.

That demand appears to have translated into commercial momentum. Dorje said the company’s revenue run rate has increased tenfold over the last six months, putting annualized revenue in the low millions. For investors, the combination of rapid usage growth and fast monetization likely made the company easier to back despite the still-early stage of the market.

The round was led by Nexus Venture Partners, with participation from Y Combinator, Zetta, Liquid 2 and a group of angel investors including Gokul Rajaram, Apollo.io co-founder Tim Zheng and former HubSpot COO JD Sherman.

Naïve said the deal brings in roughly $32 million in total capital raised.

How are customers using Naïve today?

Today’s most obvious use case is launching what Naïve describes as autonomous businesses. That can mean AI-first agencies that sell automation services to small companies, content channels that generate and publish media without a visible human brand, or service businesses that rely on software agents to coordinate operations.

Dorje said AI automation agencies are growing particularly quickly. In his view, many first-time founders are starting with the idea of selling AI agents to other businesses that want to save time and cut costs.

He also pointed to examples that range from digital content to offline services. In one instance, he said, he found Naïve infrastructure supporting a TikTok channel filled with AI-generated clips of dancing and boxing cats and dogs. In another, a rental car operation was reportedly running with significant autonomy.

The examples illustrate both the novelty and the ambiguity of the market. Some customers are using Naïve to launch new kinds of businesses that barely existed a few years ago. Others may simply be applying the same automation layer to ordinary small-business functions.

What makes agentic businesses expensive?

One of the biggest challenges in operating AI agents is cost. Every action can require a model call, context has to be passed between tasks, and agents can burn resources even when they are idle. At scale, those expenses can quickly overwhelm the economics of the business.

That problem is becoming central to how the next generation of AI infrastructure is built. While many startups focus on making agents more capable, Naïve is betting that cost control, orchestration and memory management will matter just as much once companies start deploying fleets of them.

The company says that as customers move beyond experimentation, they increasingly care about whether automation can actually improve margins. That makes infrastructure for inference optimization and serverless execution potentially more valuable than the initial convenience of forming a company with a prompt.

What is Naïve building with the new money?

Naïve plans to spend the Series A on four core infrastructure projects: a model router, a memory layer, an orchestration system and virtualized sandboxes for agents. The company is also hiring researchers to support those efforts.

These projects are meant to tackle the hidden complexity of large-scale agent use. Rather than letting every agent act independently and expensively, Naïve wants to route tasks to the most efficient model, preserve useful reasoning, store business context for later use and coordinate work across multiple agents.

Model routing and inference optimization

The model router is designed to send each task to the most efficient model for the job. If the system can preserve and replay already processed reasoning, it may reduce redundant token usage and lower the cost of recurring tasks.

That kind of optimization has become important as more companies experiment with agent loops. The bigger the system grows, the more expensive poorly designed routing can become.

A memory layer for business context

The memory layer is intended to store useful information about a business so agents do not need to rebuild context from scratch every time they take action. In a real company, context matters: the same agent may need to know customer preferences, budget limits, supplier details or prior approvals.

By making context easier to retrieve, Naïve hopes to reduce both cost and error rates.

Orchestration and governance

Naïve is also building tools to divide work among agents and enforce guardrails. The company says users will be able to set spending limits, constrain what agents can do and require human approval before sensitive actions are executed.

That governance layer is likely to be essential if customers are trusting software agents with corporate operations, financial workflows and external communications.

Serverless agents in lightweight JavaScript environments

One of the company’s most distinctive technical bets is a serverless runtime that runs agents in lightweight JavaScript environments rather than assigning each one its own full virtual machine. The idea is to let customers pay mostly when an agent is active, which could make it much cheaper to deploy large numbers of agents at once.

That approach may not matter much for someone setting up a single company. It could matter a great deal for firms that want to run many parallel agent workflows at scale.

Why does the company think enterprise demand will grow?

Naïve is currently strongest among developers and founders who want to automate company setup, but Dorje said interest is also coming from larger businesses. He did not identify any enterprise customers, but he suggested that cost efficiency could become the bigger market over time.

That logic is straightforward. Startups may first adopt Naïve because it saves time on incorporation and setup. Enterprises, by contrast, are more likely to care about reducing the ongoing cost of running many agents across customer service, operations or back-office workflows.

If Naïve can prove that its infrastructure makes agent systems meaningfully cheaper, it may find a more durable position in the AI stack than a tool that simply automates business formation.

How does Naïve compare with the broader AI infrastructure race?

Naïve sits at the intersection of several fast-growing AI categories: agent infrastructure, model optimization, workflow orchestration and business automation. Each is attracting intense interest because companies want to move from chatbots and demos to systems that can perform real work.

The startup’s pitch reflects a larger shift in the market. As AI agents become more capable, the focus is moving from individual prompts to the infrastructure required to manage them reliably, securely and cheaply.

That includes:

• Lowering inference costs through smarter routing
• Preserving memory across workflows
• Separating and coordinating tasks across agents
• Adding approval controls for risky actions
• Running agents in environments that scale efficiently

Naïve is not alone in chasing that layer, but its business-focused packaging gives it a distinct angle. Instead of selling generic agent tooling, it is leaning into the idea that entire companies can be assembled and run through software.

Timeline: Naïve’s rapid rise

The company’s rise has been unusually fast even by AI startup standards. Its progress over a short period helps explain why the round drew attention from both large venture firms and well-known angels.

1. Launch: Naïve introduces an API for business setup and agent-driven operations.
2. Early adoption: More than 30,000 developers sign up within months.
3. Revenue growth: Annual run-rate revenue climbs tenfold over six months.
4. Fundraising: The company closes a $28.5 million Series A led by Nexus Venture Partners.
5. Expansion: Naïve commits to building routing, memory, orchestration and serverless agent infrastructure.

What this says about the future of AI companies

Naïve’s strategy points to a bigger shift in how founders think about automation. The first wave of AI companies focused on generating text, code or images. The next wave is increasingly about coordination: connecting models to real-world systems and letting them take action.

That creates a new infrastructure problem. Once agents can open accounts, send emails, create infrastructure and manage workflows, companies need controls, memory, cost management and human oversight. Naïve is positioning itself as the layer that makes that possible.

There is still a long road between a promising startup and a broadly trusted platform for AI-run businesses. Questions remain about reliability, security, accountability and whether some business functions can truly be delegated to software without introducing new risks. But the appetite for experimentation is clearly there.

For now, Naïve’s funding round suggests that investors believe the market for agentic business infrastructure is expanding quickly. If the company can translate early developer enthusiasm into a robust operating layer for autonomous businesses, it could become part of the core tooling behind how AI companies are launched and managed.

Why this round matters now

Naïve’s latest raise matters because it captures two separate trends at once: the rush to automate the mundane work of starting a company, and the growing need to make AI agents affordable enough to run continuously. The first may be flashy, but the second may prove more enduring.

In that sense, the startup is not just selling convenience. It is trying to define the operating system for companies that are increasingly built, and perhaps eventually run, by software agents.