RAG Embeddings for Enterprise Search

High-dimensional embeddings for search, RAG and clustering.

Top-ranked retrieval embeddings, optimised for RAG quality.

Self-supervised vision foundation model for image features.

Multimodal multilingual embeddings for enterprise RAG.

Multilingual long-context embedding model.

Open multi-functional, multilingual embedding model.

Open MoE multilingual text embeddings.

RAG-based AI search and insights over enterprise backup data.

Unified data platform for AI with embedded vector database and compute.

End-to-end platform for building and serving custom AI agents on the lakehouse.

Managed vector database powering RAG and semantic-search apps.

Open-source vector database with hybrid search and modules.

GenAI assistant across Elastic Search, Observability and Security.

Enterprise search and AI agents across Jira, Confluence and 3rd-party SaaS.

Enterprise-search and work-AI assistant across SaaS data.

Managed LLM service on OCI featuring Cohere and Meta Llama models.

Runtime and lifecycle management for AI workloads on SAP BTP.

Unified customer data foundation powering Einstein and Agentforce.

Unified platform to build, evaluate and deploy AI agents and models on Azure.

Vector and hybrid retrieval engine for grounding LLMs on enterprise data.

Generative-AI assistant grounded on enterprise data and SaaS connectors.

End-to-end platform to build, train and deploy ML and foundation models.

Unified platform for Gemini, Model Garden, agents and ML on GCP.

In-warehouse ML and GenAI directly on BigQuery data via SQL.

Studio to train, tune and deploy foundation models including Granite.

Open data lakehouse optimized for AI workloads and RAG.

Generative-AI assistant for data management, integration and governance.

Build agentic apps grounded on governed Snowflake data with hosted LLMs.

Tooling to build, evaluate and govern compound AI agents on the lakehouse.

On-prem GenAI reference architecture co-engineered with NVIDIA and IBM.

Universal search and AI assistant across SaaS apps and content.

In-database analytics and GenAI orchestration on Teradata VantageCloud.

Native vector search in MongoDB Atlas for RAG and semantic apps.

Low-latency vector database and semantic cache for GenAI apps.

Embeddings-based product search powering natural-language storefront discovery.

AI-powered CDP predictions joining warehouse data to real-time activation.

Private AI services for VCF — model serving, RAG and vector DB on-prem.

Serverless GPU inference platform running open models at the edge.

Meta's recommendation model tracked by Epoch, focused on recommender system.