Embed
Top-tier vector embeddings for semantic search and retrieval.
Embed translates text into high-dimensional vector space, enabling state-of-the-art semantic search, clustering, and retrieval-augmented generation (RAG).
Core Capabilities
- Multi-lingual support across 100+ languages
- Optimized for semantic similarity tasks
- Consistent vector representations
- Scalable indexing compatibility
Strategic Overview
Unlock the value of your unstructured data. Embed allows systems to 'understand' the meaning behind words, facilitating lightning-fast and accurate search across massive datasets.
Technical Specifications
Outputs dense vectors optimized for cosine similarity and FAISS-based indexing.
Spec 01
1024-dimensional embedding space
Spec 02
Sub-millisecond inference per token
Spec 03
Normalized vector outputs
Spec 04
Batch processing support
Enterprise Use Cases
We accelerate the path to production for AI-first enterprises.
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