EMBED

Activate your enterprise data with secure AI retrieval

Embed turns text and images into embeddings to enable semantic retrieval in search systems, RAG architectures, and agentic applications — powering answers, insights, and action across the enterprise

Trusted by industry leaders and developers worldwide

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Salesforce Logo
RBC Logo
Borderless AI Logo
Oracle Logo
Notion Logo
Longshot Logo
Jasper Logo
Accenture Logo
Helvia Logo
BambooHR Logo
Fujitsu Logo
DeepJudge Logo
LG CNS Logo
McKinsey & Company Logo
Casetext Logo
TD Bank Logo
Flowrite Logo
Tabnine Logo
Johnson Lambert Logo
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Transform fragmented data into actionable knowledge

From fetching relevant content for Q&A to surfacing task-critical context for agents, Embed enables fast, accurate retrieval across your data.

Fuel enterprise AI agents

Power AI agents that understand your business — retrieving the right data to support reasoning, tool use, and generation across enterprise domains.

AI Agents UI

The foundation for semantic retrieval in real-world AI systems

Built to handle complexity and scale, Embed delivers precise retrieval across noisy, multilingual, and multimodal data.

Built for business documents

Generate a single embedding for mixed-modality docs containing text, graphs, and tables — simplifying your pipeline and improving accuracy by eliminating data pre-processing.

Generate a single embedding for mixed-modality docs containing text, graphs, and tables

Multilingual by design

Embed retrieves relevant content in 100+ languages even when queries and source languages don’t match — returning accurate results without any need to identify language or translate.

Embed retrieves relevant content in 100+ languages even when queries and source languages don’t match

Advanced image understanding

Map visual assets and written content into the same embedding space — making charts, dashboards, and design files just as searchable as any block of text.

Map visual assets and written content into the same embedding space — making charts, dashboards, and design files just as searchable as any block of text.

Industry-specific performance

Embed handles high-context business content with precision — from financial filings to healthcare records — surfacing what’s most relevant, not just what matches the query keywords.

Embed handles high-context business content with precision — from financial filings to healthcare records — surfacing what’s most relevant, not just what matches the query keywords.

Secure, fast, reliable — Everything you need for production-ready retrieval

  • Privately deployable: Run Embed in your virtual private cloud (VPC) or on-premises environment to keep sensitive data secure — or deploy via major cloud services.
  • Efficient at scale: Compress embeddings by up to 96% without sacrificing quality — reducing vector database storage costs and improving performance at the scale of billions of embeddings.
  • Robust in production: Deliver accurate results across noisy, multilingual, and multimodal enterprise data — even on fragmented or domain-specific data.

Workplace solutions built on Embed

Global organizations choose Embed for search and retrieval

"Hunt Club's Atlas product lets customers navigate their sprawling professional networks and find talent within them. AI is essential in searching across complex candidate profiles and making sense of messy data to find ideal matches. Cohere's Embed 4 enables us to search these profiles more precisely, showing a +47% relative improvement over the already-strong performance of Embed 3. We are extremely impressed!"

— James Kirk, VP of AI, Hunt Club

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Ready to put your data to work?

Talk to our team to learn how Embed can integrate with your stack, improve retrieval accuracy, and scale to production.

  • Discover how Embed performs across your languages, formats, and domains

  • Explore how Embed, Command, and Rerank can work together in RAG and agentic pipelines

  • Determine the right deployment path for your infrastructure and security needs

  • Get support integrating Embed and scaling semantic search and retrieval to production

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