Vellum Raises $20M Series A to Accelerate Reliable Enterprise AI Development

Vellum, an end-to-end AI development platform designed to help enterprises confidently build, test, and deploy mission-critical AI applications, has raised $20 million in Series A funding. The round was led by Leaders Fund, with participation from Socii Capital, Y Combinator, Rebel Fund, Pioneer Fund, and Eastlink Capital.

This new funding comes at a pivotal moment for AI across industries. While nearly every enterprise is exploring generative AI, over half still lack a comprehensive strategy, and most are stuck in prolonged prototyping phases. Vellum is stepping in to close this gap—offering a purpose-built development environment that brings rigor, speed, and observability to AI engineering.

Bridging the AI Readiness Gap

Founded by Akash Sharma and built on the insight that traditional software development principles like versioning, testing, and collaboration are missing in most AI tooling, Vellum gives enterprises a structured, test-driven approach to AI. Its platform enables cross-functional teams—including engineers, product leads, and subject matter experts—to work together through:

• A visual UI builder and SDK to define AI workflows
• Native evaluation and testing to catch failures before deployment
• One-click versioned releases with rollback support
• Real-time monitoring to continuously improve model performance

“Generative AI has redefined how companies think about software, but the infrastructure to build AI systems responsibly just hasn’t kept up,” said Akash Sharma, CEO of Vellum. “That’s why we created Vellum—to bring the same predictability, control, and clarity we expect from traditional software into AI development.”

A Purpose-Built Platform for Enterprise AI

Vellum is more than a prototyping tool—it’s a full-stack AI operating environment. Teams can orchestrate entire workflows using a visual graph-based editor that prioritizes control flow over data flow, allowing them to manage complex behavior like branching, recursion, parallel execution, and streaming.

This composability makes Vellum especially valuable in highly regulated industries, where security, reliability, and traceability are critical. By unifying orchestration, prompting, retrieval-augmented generation (RAG), evaluation, and monitoring into a single platform, Vellum helps enterprises deploy AI systems that are robust, observable, and easy to maintain.

Trusted by Top Enterprise Teams

Vellum is already delivering results for leading organizations such as Drata, Swisscom, Redfin, and Headspace, powering AI initiatives across customer service, compliance automation, internal tooling, and content generation.

Drata, a leader in security and compliance automation, uses Vellum to support over 7,000 customer environments with secure, explainable AI workflows.

“Vellum gives us the infrastructure to move fast without sacrificing performance or trust,” said Lior Solomon, VP of Engineering at Drata. “Their test-driven system helps us iterate quickly while keeping our AI accurate and compliant.”

Leaders Fund, which led the round, brings deep operational expertise to the table, having backed multiple companies from early traction to $100M+ ARR.

“Vellum is solving the core problem slowing AI adoption in the enterprise—how to make AI systems reliable, repeatable, and production-ready,” said Gideon Hayden, Managing Partner at Leaders Fund. “They’ve built what every enterprise AI team needs, and we’re excited to help them scale it globally.”

Building the Standard for AI Development

As artificial intelligence shifts from experimental to operational, the pressure is mounting for companies to adopt infrastructure that supports long-term reliability, governance, and maintainability. The complexity of deploying AI systems—especially those involving large language models, retrieval-augmented generation, or agent-based architectures—requires more than ad hoc scripts and prompt engineering. The need for robust orchestration, controlled experimentation, and continuous monitoring is becoming non-negotiable.

This is ushering in a new phase in enterprise AI: one that mirrors the structured software development practices of DevOps, but adapted for a dynamic and probabilistic medium. Instead of building AI in silos or relying solely on intuition, teams are now turning to tools that treat AI development as an iterative engineering discipline—complete with versioning, test coverage, and visibility into every output and decision.

Technologies that enable this shift are laying the foundation for what will likely become the new standard in AI operations. In the near future, AI applications will be expected to have transparent logic flows, audit-ready outputs, real-time error handling, and seamless integration with evolving models and datasets. As regulation tightens and use cases grow more complex, the industry will demand development environments that combine flexibility with accountability—allowing teams to move fast without losing control.

This evolution reflects a broader maturation of the AI field itself. The promise of generative AI was never just about impressive demos—it was about integrating intelligent systems into the core fabric of business. The next generation of platforms will be judged not just by how powerful they are, but by how safely and predictably they can scale across real-world environments.