As generative AI explodes across workplaces, a new class of infrastructure is emerging to tame chaos. Unbound, a San Francisco-based startup, has earned $4 million in seed rounds to help businesses perform AI on their own terms (and so, observing and cost-effective.
The round was led by Racial Capital and was supported in a well-known angel lineup including Wayfinder Ventures, Y Combinator, Massive Tech Ventures, and a well-known angel lineup including Google Board member Ram Shriram and cybersecurity veterans from Cloudflare and Palo Alto Networks. The company positioned itself at the forefront of AI governance – an increasingly urgent sector with large-scale adoption of AI by enterprises.
The shadow IT crisis of AI
From marketing teams using CHATGPT to engineers running code through Copilot, AI tools have become essential and often unlimited. This “shadow AI” adoption introduces real risks: leaking proprietary data, increasing the cost of non-surveillance, and introducing a third-party model without security review. IT teams often stay in the dark, unable to enforce policies or protect sensitive data.
Unbound is born out of this problem. The platform acts as an AI gateway, a secure middleware layer that integrates directly with popular enterprise AI tools such as cursors, ROOs, and internal document replicas. Instead of blocking access to the generative model, Unbound introduces fine-grained controls, real-time fixes, model routing and powerful usage analysis without breaking existing workflows.
Revisions and Model Routing of AI – Explanation
One of the most innovative features of Unbounded is Real-time and timely revisions. When a user interacts with an AI tool, an unbound scan requests sensitive content, such as passwords, API keys, or personal data. Instead of tagging or blocking them (like traditional data loss tools), the system automatically routes secret and route-sensitive prompts to internal models hosted on platforms such as Google Vertex AI, AWS Bedrock, or private LLM in an enterprise security environment.
This building decision reflects a growing trend: handling network traffic such as AI traffic, including routing, failover, observability, and cost control.
Unbound’s routing logic is powered by usage patterns and model performance metrics. For example, high-risk requests (such as infrastructure code generation) can be routed to top models such as Gemini 2.5, while lighter tasks (such as syntax editing) are offloaded to open source llms, gradually reducing unnecessary premium usage.
In fact, this function translates into measurable results. Early adopters in technology and healthcare use unlimited use:
- prevention 7,000 potential data leaksincluding secrets, certificates and PII.
- achieve Up to 90% detection accuracy Used for sensitive content.
- cut AI seat licensing fee up to 70%thanks to intelligent routing and model optimization.
Instead of purchasing a blanket license, companies can optionally provide access, ensuring model usage is consistent with business priorities.
Founder of Deep Security and Infrastructure DNA
Behind the platform are co-founders Rajaram Srinivasan (CEO) and Vignesh Subbiah (CTO) – veterans of enterprise software and security. Srinivasan previously led the data security product team at Palo Alto Networks and Imperva, while Subbiah helped Tophatter and Shogun seed-to-growth scale platforms before joining Adobe.
Their task is clear: build a system that enables AI innovation without compromising enterprise-level security. “Blanket ban on AI tools is outdated,” explain Subbiah. “With unbound restrictions, we provide surgical security controls for every AI request, allowing businesses to move quickly without trust.”
From chaos to coordination in the AI stack
The broader market is validating Unbound’s vision. As enterprise AI usage grows, so does the need for centralized management, transparency and failure protection. Recent research estimates that the global AI governance industry will grow at a CAGR of 45% from $890 million in 2024 to $580 million in 2029.
In this new stack, Unbount positions itself as a mission-critical infrastructure. Redundant routing during LLM downtime (at the time, such as OpenAI or Anthropic Specion Throttling), team-level usage analytics, and AI that requires model orchestration each time to transform AI from free all adoption to controlled intelligent systems.
“Think of us as a reverse proxy for enterprise AI,” explain Srinivasan. “We sit between users and models, ensuring privacy, performance and cost-effectiveness without friction.”
What’s next
With this funding, unlimited plans:
- Extend integration in over 50 enterprise AI applications.
- Add deeper observability capabilities to team and department-level insights.
- Supports complete orchestration of internal and open source models across confidential computing environments.
In a world where every department becomes an AI Power user, Unbound provides the infrastructure to control that power and meet business goals.
“We are proud to support Rajaram, Vignesh and the team,” explain Edith YoungGeneral Partner of Race Capital. “Unbound is building the AI governance layer that businesses desperately need, namely, secure, observable, and built for the real world.”
As generative AI continues to expand across enterprise workflows, the demand for tools to manage risk is growing simultaneously. Unbound’s $4 million seed round reflects a broad shift in the industry to building infrastructure that can bring visibility, control and governance to AI adoption. With growing interest in security, adaptive AI frameworks, Unbound Crom joins a range of emerging startups to address the complex challenges of integrating AI responsibly.
