Metric AI Lab

Metric is a multimodal AI Research Lab. We are building the world's most accurate universal multimodal embedding model — a single representation layer for search, retrieval, and understanding across any type of data.

Current AI systems are fundamentally fragmented. Enterprises juggle separate models for text, images, video, audio, tabular data, documents, and sensor streams. This patchwork creates brittle integrations, high maintenance costs, and inconsistent performance across data types.

We're developing the first family of foundation models designed to produce uniform, high-precision embeddings for all modalities. Our approach treats text, images, documents, UI screenshots, audio, video, and structured datasets as equally important — enabling seamless semantic search, classification, clustering, and recommendation without the complexity of multiple specialized models.

One Model, All Modalities

Instead of managing dozens of task-specific models, we deliver a single embedding model that powers the full spectrum of retrieval and similarity workloads — from finding a moment in a movie where an actor smiles, to pinpointing the second a car crosses an intersection, to discovering a relevant chart buried inside a thousand-page technical report.

Our embeddings are designed for real-world integration — fast enough for interactive applications, scalable to billions of items, and deployable in both cloud and edge environments. This means privacy-conscious organizations can keep sensitive data in-house without sacrificing accuracy or speed.

Built for Real-World Impact

Cross-modal search and retrieval is mission-critical for industries from media to healthcare, from scientific research to industrial monitoring. Our models handle the complexity and variety of real-world data while maintaining consistent performance and interoperability across all formats.

Whether you're connecting video frames to sensor readings, matching voice notes to written reports, or unifying document archives with live camera feeds — our embeddings form the semantic backbone for your applications.

Science Meets Engineering

Research excellence drives everything we do. We're building at the frontier of multimodal representation learning, combining cutting-edge self-supervised learning with the scalability and robustness required in production.

Our approach is empirical and iterative. We measure success not only by benchmarks, but by how effectively our models power real-world products, developer ecosystems, and enterprise pipelines. The most important breakthroughs come from understanding what truly matters when unifying data at scale.