As generative AI explodes across workplaces, a brand new class of infrastructure is emerging to tame the chaos. Unbound, a San Francisco-based startup, has secured a $4 million seed round to assist enterprises embrace AI on their very own terms—safely, observably, and cost-effectively.
The round was led by Race Capital, with support from Wayfinder Ventures, Y Combinator, Massive Tech Ventures, and a notable roster of angels including Google board member Ram Shriram and cybersecurity veterans from Cloudflare and Palo Alto Networks. The corporate is positioning itself on the forefront of AI governance—an increasingly urgent sector as businesses grapple with AI adoption at scale.
The Shadow IT Crisis of AI
From marketing teams using ChatGPT to engineers running code through Copilot, AI tools have develop into indispensable—and infrequently ungoverned. This “shadow AI” adoption is introducing real risks: leaking proprietary data, racking up unmonitored costs, and introducing third-party models without security reviews. IT teams are sometimes left in the dead of night, unable to implement policy or protect sensitive data.
Unbound was born out of this problem. The platform acts as an AI Gateway, a secure middleware layer that integrates directly with popular enterprise AI tools resembling Cursor, Roo, and internal document copilots. Fairly than blocking access to generative models, Unbound introduces fine-grained controls, real-time redaction, model routing, and robust usage analytics—all without breaking existing workflows.
AI Redaction and Model Routing—Explained
One in all Unbound’s most progressive features is real-time prompt redaction. When users interact with AI tools, Unbound scans requests for sensitive content like passwords, API keys, or personal data. As a substitute of flagging or blocking them (as traditional Data Loss Prevention tools do), the system mechanically redacts secrets and routes sensitive prompts to internal models hosted on platforms like Google Vertex AI, AWS Bedrock, or private LLMs contained in the enterprise’s secure environment.
This architectural decision reflects a growing trend: treating AI traffic like network traffic, complete with routing, failover, observability, and price controls.
Unbound’s routing logic is powered by usage patterns and model performance metrics. For example, high-stakes requests (resembling infrastructure code generation) may be routed to top-tier models like Gemini 2.5, while lighter tasks (e.g., grammar editing) are offloaded to open-source LLMs—cutting down on unnecessary premium license usage.
In practice, this capability translates into measurable results. Early adopters within the tech and healthcare sectors have used Unbound to:
- Prevent over 7,000 potential data leaks, including secrets, credentials, and PII.
- Achieve as much as 90% detection accuracy for sensitive content.
- Cut AI seat license costs by as much as 70%, due to smart routing and model optimization.
As a substitute of shopping for blanket licenses, firms can selectively provision access, ensuring model usage aligns with business priorities.
Founders with Deep Security and Infrastructure DNA
Behind the platform are co-founders Rajaram Srinivasan (CEO) and Vignesh Subbiah (CTO)—each veterans of enterprise software and security. Srinivasan previously led data security product teams at Palo Alto Networks and Imperva, while Subbiah helped scale platforms from seed to growth stage at Tophatter and Shogun before joining Adobe.
Their mission was clear: construct a system that permits AI innovation without compromising enterprise-grade security. said Subbiah.
From Chaos to Coordination within the AI Stack
The broader market is validating Unbound’s vision. As enterprise AI usage grows, so too does the necessity for centralized management, transparency, and fail-safes. Recent studies estimate the worldwide AI governance industry will balloon from $890M in 2024 to $5.8B by 2029—a forty five% CAGR.
Unbound is positioning itself as mission-critical infrastructure on this latest stack. Features like redundant routing during LLM downtime (when providers like OpenAI or Anthropic experience throttling), team-level usage analytics, and per-request model orchestration transform AI adoption from a free-for-all right into a controlled, intelligent system.
said Srinivasan.
What’s Next for Unbound
With this funding, Unbound plans to:
- Expand integrations across 50+ enterprise AI applications.
- Add deeper observability features for team and department-level insights.
- Support full orchestration of internal and open-source models across confidential computing environments.
In a world where every department is becoming an AI power user, Unbound provides the infrastructure to maintain that power in check—and in keeping with business objectives.
said Edith Yeung, General Partner at Race Capital.
As generative AI continues to expand across enterprise workflows, the demand for tools that manage its risks is growing in parallel. Unbound’s $4M seed round reflects a broader shift within the industry toward constructing infrastructure that may bring visibility, control, and governance to AI adoption. With growing interest in secure, adaptable AI frameworks, Unbound joins a rising cohort of startups addressing the complex challenge of integrating AI responsibly at scale.
