Vellum Raises $20M Series A to Bridge the Prototype-to-Production Gap in AI Development
Vellum raised $20 million in Series A funding led by Leaders Fund to address the fundamental infrastructure bottleneck preventing enterprise AI teams from moving beyond prototypes to production-ready systems.
The New York-based platform has worked with over 150 companies across industries, from bleeding-edge startups to household names including Swisscom, Redfin, Drata, and Headspace. The funding validates what engineering teams consistently experience: building AI demos is straightforward, but deploying reliable, mission-critical AI systems requires specialized development infrastructure that doesn’t exist in traditional software engineering.
The Prototype-to-Production Deployment Bottleneck
Enterprise AI development suffers from a fundamental gap between what works in proof-of-concept environments and what survives in production systems. Traditional software development practices fail when applied to AI because models behave unpredictably, the underlying technology landscape shifts constantly, and quality requirements demand rigorous testing that most teams lack infrastructure to implement.
“Developing AI feels like writing software in quicksand—the ground keeps shifting and teams struggle just to stay afloat,” said Akash Sharma, CEO and co-founder of Vellum. “Since March 2020, we’ve been building AI applications with LLMs and we’ve bumped into the same roadblocks every time: what works in a demo often breaks in production because models behave unpredictably.”
The infrastructure crisis manifests across enterprise deployment cycles: teams spend months on projects that work in development but fail quality standards when deployed, engineering teams become bottlenecks because non-technical experts can’t contribute to AI development, and organizations struggle to maintain consistency as new models and orchestration techniques emerge.
Test-Driven Development Infrastructure for AI
Vellum’s platform brings structured, cross-functional development practices to AI through comprehensive tooling that enables both technical and non-technical teams to collaborate on AI development lifecycles. The architecture spans AI workflow definition, end-to-end evaluation, safe deployments, and live monitoring with continuous improvement feedback loops.
Cross-functional Collaboration Architecture: A visual builder enables product managers and domain experts to shape AI behavior without writing code, while engineers use an SDK to manage and control environments with confidence. Both teams work in the same space, sharing context and staying synchronized as they build, evaluate, and iterate on AI systems.
Production-grade Testing Infrastructure: A robust testing suite catches failures and edge cases before they reach production, with synthetic test case generation that automatically creates test scenarios and evaluation frameworks that ensure models meet quality, cost, and latency requirements.
Version Control and Safe Deployment: Teams push updates and publish new versions without risky redeploys, with precise version control that works even in highly complex environments. Every change is tracked and versioned for clear history and explainability.
Real-time Monitoring and Feedback Loops: Live observability shows how systems behave in the real world, with monitoring that feeds directly into testing infrastructure to enable continuous improvement based on production performance.
Enterprise Production Validation
The platform has enabled enterprise teams to cut deployment time from quarters to weeks while maintaining quality standards that traditional development approaches couldn’t achieve. Customer deployments demonstrate the infrastructure’s ability to solve the coordination and reliability challenges that block AI adoption at scale.
Swisscom has made Vellum a core part of their AI platform, providing Swiss banks and governments with secure and reliable infrastructure for building AI applications in regulated industries. Drata builds and secures over 7,000 isolated knowledge bases to drive compliant GRC automation across tenants, with product managers and engineers collaborating in Vellum for rapid validation and deployment.
Redfin rolled out “Ask Redfin” to millions of users across 14 markets by enabling their domain experts to evaluate conversational agents using thousands of test cases, eliminating the traditional bottleneck of engineering teams managing both development and domain expertise requirements.
DeepScribe cuts clinician note iteration time by 20-40% using feedback loops and regression testing to ensure accuracy and trust in healthcare workflows. Rely Health went from multi-engineer, multi-month builds to deploying healthcare workflows in days, automating voice agents, smart triage, and charting through Vellum’s tracing and decoupled deployments.
AI Development Infrastructure Standardization
The funding round validates the emergence of AI development as a distinct infrastructure category requiring specialized tooling that doesn’t exist in traditional software engineering. Unlike general-purpose development platforms, AI systems require infrastructure that handles the unique challenges of working with large language models: unpredictable behavior, rapid technological change, and quality requirements that demand extensive testing.
The test-driven development standard that Vellum implements transforms best practices into everyday workflows, enabling teams to build systems they can trust and control as they grow. This approach turns every update into a learning opportunity, helping teams gain deeper understanding of how AI works in practice while adapting quickly as needs change.
Market validation extends beyond customer deployments: Vellum operates the world’s #1 ranking LLM Leaderboard, sharing model performance data across different use cases, and provides best practices guides, webinars, and live training to help product, ML, and engineering teams build production-ready AI systems.
Looking Forward: Infrastructure-First AI Development
The Series A funding positions Vellum to accelerate the transformation from experimental AI development to infrastructure-first approaches that enterprises can rely on for core business functions. The company plans to increase the number of AI use cases deployed through their platform, lower time-to-production for each deployment, expand presence in new verticals and geographies, and establish Vellum as the foundational layer in the AI stack.
This infrastructure standardization addresses the fundamental challenge preventing enterprise AI adoption: the gap between prototype excitement and production reliability. As AI native companies like Cursor and Lovable demonstrate rocketship growth, and industry leaders like Salesforce integrate AI into core strategy, the bottleneck isn’t AI capability but infrastructure that enables reliable, scalable deployment.
The development platform category emergence reflects a broader shift in how organizations approach AI: from experimental projects managed by individual engineers to systematic infrastructure that enables cross-functional teams to build, test, and deploy AI systems with the same confidence they have in traditional software development.
Enterprise AI Orchestration Infrastructure
The challenge Vellum addresses—bridging the gap between AI prototypes and production systems—reflects the broader infrastructure transformation happening across enterprise AI deployment. While companies like Vellum build the development infrastructure layer, platforms like Overclock focus on the orchestration layer, enabling teams to coordinate AI agents and workflows through natural language playbooks that eliminate complex technical setup.
Both approaches recognize the same fundamental insight: enterprise AI adoption requires specialized infrastructure that handles the unique coordination, reliability, and deployment challenges that traditional software engineering tools can’t address. As organizations move beyond experimental AI to business-critical deployments, this infrastructure-first approach becomes essential for teams building AI-native operations at scale.