Enterprise RAG Specialist

Command R+ RAG-Optimized Enterprise AI

Deploy Cohere's advanced model designed specifically for enterprise retrieval-augmented generation. Built-in citation capabilities and superior document understanding.

See RAG Demo Technical Architecture

RAG Architecture

Documents

Databases

APIs

R+

Command R+

Retrieval + Generation

Grounded Generation

Auto Citations

"According to the Q3 report [1], revenue increased by 23%. The product roadmap [2] indicates new features launching in Q4."

[1] Q3-2024-Report.pdf, page 12
[2] Product-Roadmap-2024.docx, section 3.2

104B

Parameters

128K

Context Window

10 Languages

Full Support

Citations

Built-in Feature

Purpose-Built for Enterprise RAG

Command R+ is specifically designed for retrieval-augmented generation workflows

Grounded Generation

Responses are always grounded in your provided documents, eliminating hallucinations

  • Facts verified against sources
  • No unsupported claims
  • Confidence scoring included
Key Feature

Native Citations

Automatic inline citations with precise source attribution for every claim

  • Page-level precision
  • Multiple source tracking
  • Compliance-ready audit trail

Multilingual RAG

Query in one language, retrieve from documents in any supported language

  • Cross-lingual retrieval
  • 10 languages supported
  • Unified knowledge base

See RAG in Action

Experience how Command R+ transforms enterprise document search and Q&A

Sample Query

User Question:

"What were our Q3 revenue figures and what factors contributed to the growth compared to Q2?"

Document Corpus

Q3-2024-Earnings-Report.pdf
Sales-Analysis-Q2-Q3.xlsx
Market-Conditions-2024.docx

Command R+ Response

Based on the Q3 earnings report [1], our revenue for Q3 2024 was $127.3 million, representing a 23% increase from Q2's $103.5 million [2].

The key factors contributing to this growth were:

  • • New Enterprise Clients: 12 new Fortune 500 accounts signed in Q3 [1]
  • • Product Expansion: 34% increase in existing customer spending [2]
  • • Market Conditions: Favorable AI adoption trends in financial services [3]

Sources:

[1] Q3-2024-Earnings-Report.pdf, pages 3-4, 12

[2] Sales-Analysis-Q2-Q3.xlsx, "Summary" sheet, rows 15-22

[3] Market-Conditions-2024.docx, section 2.3 "Industry Trends"

Enterprise RAG Architecture

Deploy Command R+ with your existing infrastructure for scalable RAG

Data Sources

SharePoint

S3 Buckets

Databases

APIs

Processing Pipeline

Document Ingestion

  • • OCR Processing
  • • Format Conversion
  • • Metadata Extraction

Chunking & Embedding

  • • Smart Chunking
  • • Embedding Generation
  • • Index Creation

Vector Storage

  • • Pinecone/Weaviate
  • • Metadata Filtering
  • • RBAC Integration

AI Processing

R+

Command R+ 104B

Query Understanding

Intent detection, entity extraction

Retrieval Ranking

Multi-stage retrieval, reranking

Generation + Citations

Grounded responses, auto-citation

< 100ms

Retrieval Latency

99.9%

Citation Accuracy

10M+

Documents Indexed

Enterprise RAG Use Cases

How organizations leverage Command R+ for knowledge management

Legal & Compliance

Analyze contracts, regulations, and case law with precise citation tracking

Example Query:

"Find all clauses related to data retention in our vendor contracts"

→ Returns specific clauses with contract references

Technical Documentation

Search across codebases, APIs, and technical specs with context awareness

Example Query:

"How do we handle authentication in our microservices?"

→ Returns implementation details with code references

Financial Research

Analyze earnings reports, market data, and financial statements

Example Query:

"Compare revenue growth across our product lines for the last 4 quarters"

→ Returns comparative analysis with report citations

Customer Support

Instant answers from knowledge bases, manuals, and support tickets

Example Query:

"Customer reporting error X123 on version 3.2"

→ Returns troubleshooting steps with KB articles

Command R+ vs Traditional RAG

Purpose-built features that set Command R+ apart for enterprise RAG

Feature Command R+ Traditional LLM + RAG
Citation Generation Native, Automatic Manual Implementation
Grounding Accuracy 99.9% 85-90%
Context Window 128K tokens 4K-32K tokens
Multilingual RAG Built-in Limited
Hallucination Prevention Architecture-level Prompt Engineering
Enterprise Support 24/7 Dedicated Varies

Quick Implementation Guide

Get started with Command R+ RAG in your enterprise

1

Deploy Command R+ with LLMDeploy

docker run -d \
  --gpus all \
  -p 8080:8080 \
  -v /path/to/models:/models \
  llmdeploy/command-r-plus:latest \
  --model-path /models/command-r-plus-104b \
  --enable-citations \
  --context-length 128000
2

Connect Your Document Sources

from sovereign_ai import CommandRPlus, DocumentConnector

# Initialize model
model = CommandRPlus(base_url="http://localhost:8080")

# Connect document sources
connector = DocumentConnector()
connector.add_source("sharepoint", credentials)
connector.add_source("s3", bucket_config)
connector.add_source("confluence", api_key)

# Index documents
index = connector.create_index(
    chunk_size=512,
    overlap=50,
    metadata_fields=["author", "date", "department"]
)
3

Implement RAG with Citations

# Query with automatic citations
response = model.rag_query(
    query="What is our data retention policy?",
    index=index,
    num_documents=10,
    citation_mode="inline"
)

print(response.text)
# Output: "According to the Data Governance Policy [1], 
# customer data must be retained for 7 years..."

print(response.citations)
# [{"id": 1, "source": "Data-Governance-Policy.pdf", 
#   "page": 12, "confidence": 0.98}]

Expected Results

90%

Query Accuracy

< 2s

Response Time

100%

Citation Coverage

Transform Your Enterprise Knowledge Management

Deploy Command R+ for accurate, citation-backed AI responses at scale

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