Naïve Raises $28.5M Series A to Let AI Agents Set Up and Run Entire Companies

On August 6, 2026, Naïve, a Palo Alto-based AI infrastructure company, announced a $28.5 million Series A funding round led by Nexus Venture Partners . The round—which also included Y Combinator, Zetta Venture Partners, Liquid 2 Ventures, DEEPCORE, and angel investors Gokul Rajaram, JD Sherman, and Tim Zheng—brings Naïve's total capital raised to roughly $32 million, including a prior ~$2 million seed round .

What made investors open their checkbooks? Naïve had already signed up over 30,000 developer customers within months of launch, with annual run-rate revenue growing tenfold to the low double-digit millions over the preceding six months . Strong adoption of its unified API by AI coding tools like Cursor, Claude Code, and Codex was a key signal that the product had found real product-market fit .

What Naïve Does: A Single-API "Operating System" for Companies

Naïve's core insight is that starting and running a business today requires stitching together a dozen separate vendor APIs—for incorporation, banking, email, cloud hosting, and more. Naïve collapses all of that into one unified API and configuration file . A developer can prompt an AI coding agent (using Cursor, Claude Code, or Codex) with a single instruction, and Naïve's platform automatically provisions a full operational stack .

That stack includes:

• Company incorporation — U.S. LLC formation with state filing and EIN, plus KYC/KYB compliance
• Payments & banking — virtual cards with spend caps, payment processing, and bank accounts
• Business identity — email accounts and phone numbers
• Cloud infrastructure — Postgres databases, authentication, object storage, and edge functions
• Agent cloud infrastructure — the runtime layer where agents execute their logic, including orchestration, model routing, and memory

The human still completes KYC/KYB checks and makes required payments, but after that compliance handoff, the AI handles everything else—from spinning up email inboxes to integrating with Stripe and QuickBooks .

The Inference Optimization Layer: Cutting the Cost of AI Agents

A significant portion of the Series A proceeds is earmarked for building five infrastructure components designed to make operating AI agents dramatically cheaper . This addresses what the industry recognizes as the core bottleneck limiting agent deployment today: runaway inference costs .

Here is what Naïve is building:

1. Inference Optimization — Techniques to lower per-call compute costs across all agent workloads .

2. Model Router — Automatically sends each query to the most cost-effective LLM for that task. Simple lookups go to cheap small models; only complex reasoning tasks hit frontier models. The router also preserves and replays already-computed data to avoid recomputation . This approach mirrors industry best practices that report 40–85% cost reductions while maintaining 90–95% of output quality .

3. Memory System — Stores and surfaces business context across sessions so agents don't re-derive the same information, dramatically reducing redundant model calls .

4. Work Distribution Orchestrator — Coordinates which agent handles which task and manages parallel execution, preventing idle loops and wasted inference time .

5. Serverless Runtime — Runs agent code in lightweight JavaScript environments instead of full virtual machines, slashing idle costs for large-scale agent deployments .

Together, these components are designed to make autonomous businesses (and any enterprise using Naïve's agent infrastructure) significantly cheaper to operate at scale. The company plans to use the funding to hire researchers and expand these projects, currently operating with just 10 full-time employees .

What This Means for the AI Agent Landscape

Naïve's approach is notable because it combines two distinct value propositions: a business automation layer that simplifies company creation and operations, and an inference cost-reduction layer that makes running the underlying AI agents economically viable at scale. If successful, the platform could lower the barrier to launching a fully AI-operated business from weeks to minutes, while keeping the ongoing compute costs under control.