Vellum is an AI development platform that helps organizations build, evaluate, and deploy applications powered by large language models. As artificial intelligence becomes a larger part of software products and business workflows, developers face new challenges related to prompt management, testing, quality control, and production deployment. Vellum addresses these requirements through a platform that provides tools for managing the lifecycle of AI applications from experimentation through production use.

Large language models have introduced new possibilities for software development. Organizations are building AI assistants, search systems, content generation tools, customer support applications, and workflow automation solutions. While language models provide powerful capabilities, creating reliable AI applications often requires more than selecting a model and writing prompts. Developers must evaluate outputs, manage prompt versions, monitor performance, and ensure that applications behave as expected under real-world usage.

These requirements have created demand for platforms specifically designed for AI development workflows. Vellum serves this growing category by providing infrastructure that helps organizations organize, test, and manage language-model-powered applications. Through prompt engineering tools, evaluation capabilities, workflow orchestration, and deployment support, the platform helps developers move AI projects from experimentation to operational use.

Managing Prompts Across Development Workflows

Prompt design plays a major role in determining how language model applications behave. Small changes in wording, instructions, context, or formatting can produce significantly different outputs. As applications grow, managing these prompts becomes a more structured process requiring documentation, version control, and testing.

Vellum provides functionality that allows developers to create, organize, and manage prompts within a centralized platform. Rather than maintaining prompts across spreadsheets, code repositories, and separate documentation systems, organizations can manage prompt assets through dedicated workflows. This structure helps development groups track changes and maintain visibility into prompt evolution over time.

Prompt management becomes particularly important when multiple stakeholders contribute to application development. Product managers, developers, data specialists, and business users may all participate in refining prompts for specific use cases. Maintaining a structured repository helps ensure consistency while reducing confusion related to prompt versions and deployment status.

Version tracking also supports experimentation. Organizations frequently test multiple prompt variations to determine which configuration produces the most suitable outcomes. By maintaining historical records and organized prompt libraries, Vellum enables developers to compare iterations and monitor how modifications influence application behavior.

As AI applications become larger and more sophisticated, prompt management has become an important discipline within development workflows. Dedicated tooling helps organizations manage this growing requirement while maintaining oversight of production-ready assets.

Evaluating AI Outputs Through Structured Testing

Evaluation represents one of the most significant challenges associated with AI application development. Traditional software can often be validated through deterministic testing, where expected outputs are known in advance. Language models operate differently, generating responses that may vary depending on prompts, context, and user interactions.

Vellum addresses this challenge through evaluation functionality designed specifically for AI systems. Developers can create testing workflows that assess outputs across a variety of scenarios and performance criteria. This allows organizations to evaluate applications before deployment and monitor behavior as systems evolve.

Testing often involves comparing multiple prompts, model providers, or workflow configurations. Structured evaluation helps developers understand how these variations affect output quality and consistency. Rather than relying solely on subjective reviews, organizations can establish repeatable evaluation processes that support more informed decision-making.

Quality assurance becomes particularly important when AI systems are used within customer-facing products or operational workflows. Organizations may require validation procedures related to accuracy, relevance, safety, formatting, or business requirements. Evaluation tools help developers assess these factors through organized testing frameworks.

As AI adoption expands across industries, evaluation has become a significant component of development workflows. Reliable testing helps organizations reduce uncertainty while supporting deployment decisions based on observed performance rather than assumptions. Vellum contributes to this process through tools designed to support structured analysis of AI-generated outputs.

Supporting Production Deployment and Workflow Orchestration

Moving AI applications from experimentation to production often introduces additional challenges. Development groups must manage workflows involving prompts, language models, external data sources, retrieval systems, and application logic. Coordinating these elements requires infrastructure capable of supporting production operations.

Vellum provides workflow orchestration capabilities that help organizations design and manage AI application logic. Developers can create workflows involving multiple steps, model interactions, decision points, and integrations with external systems. This functionality supports applications that require more than a single prompt-response interaction.

Production deployment also requires operational oversight. Organizations need visibility into application behavior, prompt usage, workflow execution, and output generation. By providing tools that support deployment management, Vellum helps developers maintain greater control over production AI systems.

Workflow orchestration has become particularly important as organizations build retrieval-augmented generation applications, intelligent assistants, content processing systems, and automated business workflows. These systems often depend on interactions between multiple services rather than a single language model request. Dedicated orchestration capabilities help developers manage these interactions more effectively.

The platform also supports experimentation and deployment across different AI providers. Organizations frequently evaluate multiple language models to determine which options best align with business requirements. Supporting these workflows through a unified platform helps simplify development while maintaining flexibility regarding model selection.

Providing Infrastructure for Enterprise AI Development

The rapid growth of generative AI has created demand for infrastructure that supports the entire lifecycle of AI application development. Organizations are seeking tools that help manage prompts, evaluate outputs, orchestrate workflows, and deploy applications without relying solely on custom-built internal systems.

Vellum addresses these requirements through a platform designed specifically for AI development and operations. By supporting prompt management, structured evaluation, workflow orchestration, and deployment processes, the company provides functionality that aligns with the needs of modern AI development groups.

As organizations move beyond experimental projects and deploy AI applications at scale, operational requirements become more significant. Managing prompt assets, evaluating outputs, maintaining quality standards, and coordinating workflow execution are all important aspects of production AI systems. Dedicated platforms help organizations address these requirements while reducing reliance on fragmented tooling.

Today, AI applications are being deployed across customer service, enterprise search, content generation, workflow automation, research assistance, and knowledge management. These applications require infrastructure capable of supporting reliable development and operational processes. Through tools focused on prompt lifecycle management, evaluation frameworks, workflow orchestration, and deployment support, Vellum provides organizations with technology designed for the growing demands of enterprise AI development.

Akash Sharma, Founder & CEO, Vellum