Vellum is a Conversational AI Platform and LLM Orchestration environment designed to build, test, and deploy production-ready AI applications. The solution functions as a centralized infrastructure layer that allows teams to develop Agentic Workflows and conversational interfaces by chaining Large Language Models (LLMs) with external tools, logic, and proprietary data sources.

Key Capabilities

Visual Workflow Orchestration: Features a low-code canvas for architecting complex, multi-step AI logic, including LLM calls, Python code snippets, and Vector Search integrations.
Model-Agnostic Sandbox: Facilitates Prompt Engineering and side-by-side testing across multiple model providers (e.g., OpenAI, Anthropic, Google) to evaluate performance, cost, and latency variations.
Automated Evaluation Framework: Implements a rigorous testing environment for Backtesting prompt iterations and model upgrades against "ground truth" datasets to ensure output consistency and safety.
Agentic Workflows with RAG: Natively supports Retrieval-Augmented Generation (RAG) through integrated document knowledge bases, allowing conversational agents to ground responses in proprietary enterprise data.
Streaming and Async Processing: Supports real-time output streaming for conversational interfaces and asynchronous execution for complex, long-running agentic tasks.
Deployment via API-First Architecture: Decouples AI logic from application code, allowing developers to update prompts and workflows as API endpoints without requiring backend redeployments.
Enterprise Observability: Captures and monitors 100% of LLM interactions to provide telemetry on token consumption, error rates, and hallucination detection across production environments.

Audience & Use Cases

Audience: Product Engineers, AI Developers, and Product Managers building conversational interfaces and autonomous agents.
Use Case: Developing production-grade AI Chatbots that require structured reasoning, external tool access, and grounding in enterprise knowledge bases.
Use Case: Transitioning from experimental prompts to a data-driven development lifecycle with automated quality assurance and regression testing.
Use Case: Scaling conversational AI features by managing multiple model providers and versions through a unified orchestration layer.

Technical Specifications

Architecture: Middleware and orchestration layer with managed Cloud or self-hosted deployment options.
Integration Framework: Unified API for model access, workflow execution, and document management.
Security & Privacy: Support for SOC 2 Type II and HIPAA compliance with zero-retention data processing protocols.

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