Our Customers
Leading embedding performance
Robust to noisy data
Noisy data often contains errors, outliers, and irrelevant information that hinder an embedding model’s ability to discern meaningful patterns or relationships within the data. Our Embed model understands your data’s nuances, making it highly accurate even when dealing with noisy real-world datasets.
Better retrievals for RAG
The effectiveness of RAG is dependent on multiple components, including embedding models that power search systems to retrieve relevant information. Embed’s elevated accuracy facilitates highly relevant and fewer search results, saving time and computational resources for retrievals.
What’s possible with Embed
Semantic Search
Use embeddings to enable searching by meaning, which better incorporates context and user intent than previous keyword-matching systems.
Retrieval-augmented generation
Improve RAG systems by using a performant embedding model that’s specifically tuned for search.
Clustering
Make sense of large datasets by grouping similar texts and images based on their meaning (as captured by embeddings). Uncover patterns within commonly asked questions or groupings of similar issues.
Text classification
Build systems that automatically classify text and image data into complex categories. Use these systems to efficiently route tickets, moderate content, and more.
Language Models Optimized for Semantic Search
Use Embed with a wide variety of vector databases that directly integrate with the Embed model.
Embed resources
Cohere docs
Cohere models: Embed
