AI21 Labs

End-to-end enterprise AI solution design and deployment service covering architecture design, customization to enterprise data, and post-deployment optimization.

Novel scaling techniques applied at inference time to push an agent's performance-cost pareto frontier.

Automatically identifies the optimal agent model-harness configuration from countless possible options.

A system that dynamically routes AI calls among an ensemble of models to cut costs while maintaining frontier-level quality output.

A frontier language model optimized for reliability and speed, available for training and deployment.

An open foundation model optimized for private enterprise deployment with high reliability and security.

Family of efficient open foundation models for long-context processing. Delivers reliable outputs at high speed while maintaining enterprise data security and compliance.

A compact 3B parameter model delivering enterprise-grade reasoning with record latency and extended 256K context window support.

An open foundation model optimized for private enterprise deployment, combining the Jamba hybrid Mamba-Transformer architecture with a 256K context window for document processing and knowledge base search.

A compact 3B parameter open foundation model optimized for reliability and steerability in on-device applications and agentic workflows.

An efficiency-optimized open foundation model blending reliability and steerability for core enterprise workflows.

An AI agent orchestration platform that optimizes model routing, execution strategies, and harness selection to scale agents cost-effectively while maintaining quality.

A system for scaling state-modifying agents with shared state management using Model Context Protocol workspaces.

An orchestrated approach to elevating long-horizon agentic tasks by applying additional compute at inference time.