Cohere Platform

Command Models (Generation) - Command R+: Most capable, multilingual (10+ languages), long context (128k tokens) - Command R: Balanced performance for production use - Command Light: Fast, cost-effective for simple tasks

Embed Models (Embeddings) - Embed v3: State-of-art text embeddings for semantic search - Multilingual support (100+ languages) - Compression-aware embeddings (efficient storage)

Rerank (Search Optimization) - Rerank 3: Reorder search results by relevance - Improves retrieval accuracy by 20-40% - Critical for production RAG systems

Enterprise Features - Data stays in your cloud or on-premise - Private deployments available - No training on customer data - SOC 2, ISO 27001, GDPR compliant

Embeddings & Search Expertise - Best-in-class embeddings (Embed v3) - Rerank dramatically improves search accuracy - Purpose-built for semantic search and RAG - Multilingual embeddings (100+ languages)

Enterprise-First Approach - Private deployment options - Data sovereignty guarantees - No training on customer data (key differentiator) - Enterprise SLAs and support

RAG-Optimized Stack - Embed (semantic search) + Command (generation) + Rerank (relevance) - Integrated workflow for retrieval-augmented generation - Grounded generation (cite sources, reduce hallucination)

Cost-Effective for Search - Embeddings cheaper than competitors - Rerank more cost-effective than re-embedding - Efficient for high-volume search use cases

Embed Models (Semantic Search)

Convert text to vectors for semantic search. Find documents by meaning, not keywords. Embed v3 outperforms OpenAI and others on retrieval benchmarks. Supports compression for efficient storage.

Rerank (Search Optimization)

Take search results (from Embed or any search system) and reorder by true relevance. Dramatically improves accuracy—20-40% better than embeddings alone. Essential for production RAG systems where precision matters.

Command Models (Generation)

Generate responses based on retrieved context. Command R+ handles long context (128k tokens), multilingual tasks, and complex reasoning. Command Light for simple, fast generation.

Grounded Generation (RAG)

Generate answers with citations to source documents. Reduces hallucination, provides transparency. Users see which documents informed the answer.

Good fit when: - Search and retrieval are primary use cases - Building RAG (retrieval-augmented generation) systems - Need multilingual embeddings (100+ languages) - Data sovereignty or private deployment required - Want guarantee of no training on your data - Cost-sensitive for high-volume search

Consider alternatives when: - Need absolute latest generative capabilities (GPT-4/Claude ahead) - Simple chatbot without search (→ OpenAI, Claude, Dialogflow) - Vision or multimodal required (→ GPT-4V, Claude 3, Gemini) - Want single-vendor LLM + embeddings (OpenAI has both, less specialized)

Enterprise Knowledge Search

Semantic search across internal documents, wikis, SharePoint, Confluence. Embed for retrieval, Rerank for precision, Command for conversational answers with citations. Improvement: 30-50% better search accuracy vs keyword search

Customer Support with RAG

Answer customer questions using help docs, manuals, past tickets. Retrieve relevant content (Embed), reorder by relevance (Rerank), generate answer with sources (Command). Benefit: Grounded responses with citations reduce hallucination

Legal Document Discovery

Search case law, contracts, legal documents by semantic meaning. Multilingual support for cross-border legal work. Rerank ensures most relevant documents surface first. Time saving: 60-80% reduction in manual document review

Multilingual Content Search

Search content in 100+ languages with single embedding space. Query in English, find documents in any language. Useful for global organizations. Coverage: 100+ languages vs OpenAI's ~50

Research & Academic Search

Semantic search across research papers, journals, academic databases. Embed papers, Rerank by relevance to query, summarize findings (Command). Accuracy: 20-30% better retrieval than keyword search

Step 1: Index Documents (Embed) - Chunk documents into passages - Generate embeddings with Embed v3 - Store in vector database (Pinecone, Weaviate, Qdrant, pgvector)

Step 2: Retrieve Candidates (Embed) - User asks question - Generate query embedding - Retrieve top 20-50 candidate documents from vector DB

Step 3: Rerank for Precision (Rerank) - Pass query + candidates to Rerank 3 - Reorder by true relevance - Select top 3-5 for context

Step 4: Generate Answer (Command) - Send query + reranked documents to Command - Generate grounded answer with citations - Return answer + source references

Result: Higher accuracy than embeddings alone, transparent sourcing

Cohere Embed v3: - Stronger retrieval performance (MTEB benchmarks) - Compression-aware (efficient storage) - 100+ languages vs OpenAI's ~50 - No training on customer data guarantee

OpenAI text-embedding-3: - Simpler if already using GPT-4 - Good performance, less specialized - Single vendor for LLM + embeddings

Recommendation: Cohere for search-heavy, multilingual, or privacy-sensitive. OpenAI for simplicity if already using GPT-4 and search is secondary.

Embed v3: - $0.10 per 1M tokens (cheaper than OpenAI's $0.13) - High volume discounts available

Rerank 3: - $2.00 per 1k searches - Cost-effective vs re-embedding or larger context

Command R+: - $3 per 1M input tokens, $15 per 1M output tokens - Similar to GPT-4 Turbo pricing

Typical Usage (RAG system, 100k queries/month): - Embed: £20-50/month - Rerank: £150-300/month - Command: £200-1k/month depending on response length - Total: £370-1.35k/month

What makes Cohere different from OpenAI or Anthropic?

Cohere specializes in embeddings and retrieval. Embed v3 outperforms competitors on search benchmarks. Rerank is unique (others don't offer it). Command models are good but not leading edge like GPT-4/Claude. Choose Cohere if search/RAG is primary, OpenAI/Claude if generation is primary.

Do we need Rerank if we have good embeddings?

Yes, for production systems. Embeddings retrieve candidates but aren't perfect at ranking. Rerank improves accuracy by 20-40% in our testing. Small cost (£2 per 1k searches) for significant quality improvement. Essential if precision matters.

Can Cohere work with our existing vector database?

Yes. Cohere Embed works with all major vector databases: Pinecone, Weaviate, Qdrant, Chroma, pgvector, Milvus. Generate embeddings with Cohere, store wherever you prefer. Rerank works regardless of search backend.

What about data privacy and training?

Cohere guarantees no training on customer data (key differentiator vs some competitors). Private deployments available (AWS, Azure, GCP, on-premise). Data stays in your environment. SOC 2, ISO 27001, GDPR compliant.

How long to deploy a Cohere RAG system?

Simple search (Embed only): 3-4 weeks. Full RAG (Embed + Rerank + Command): 6-8 weeks. Complex enterprise search with multiple sources and custom UI: 10-14 weeks. Includes indexing, integration, testing, and refinement.

What does it cost to build with Cohere?

Initial build (RAG system): £25k-£50k depending on complexity. Ongoing API costs: £370-1.35k/month for typical usage (100k queries/month). Vector database hosting: £100-500/month. More cost-effective than OpenAI for search-heavy applications.

Does Cohere support languages other than English?

Yes, 100+ languages for Embed v3 (more than competitors). Command R+ supports 10+ languages for generation. Strong for multilingual search and global deployments. Quality varies by language (English, Spanish, French, German best).

1. Use Case Assessment (Free consultation) Discuss search and retrieval needs, document volume, languages, privacy requirements.

2. Proof of Concept (4-6 weeks, £15k-£25k) Build focused RAG system with Embed + Rerank + Command. Test with your documents, measure search accuracy, validate approach.

3. Production Deployment (8-12 weeks, £30k-£60k) Full implementation, index documents, integrate with systems, deploy RAG pipeline, add monitoring and optimization.

Book consultation to discuss Cohere for enterprise search, embeddings, and RAG systems.

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