Introducing Command R+: Our new, most powerful model in the Command R family.
CHALLENGE
A global consultancy wanted to build an internal knowledge assistant using retrieval-augmented generation (RAG) to help its consultants find information and generate reports from millions of proprietary documents. The firm also wanted the assistant to help optimize project staffing with an intelligent directory tool that can quickly find and provide details on consultants with specific domain knowledge.
CHALLENGE
A global consultancy wanted to build an internal knowledge assistant using retrieval-augmented generation (RAG) to help its consultants find information and generate reports from millions of proprietary documents. The firm also wanted the assistant to help optimize project staffing with an intelligent directory tool that can quickly find and provide details on consultants with specific domain knowledge.
SOLUTION
Cohere worked with the global consultancy to build a custom RAG solution based on Cohere Command, Embed, and Rerank. Using an intelligent assistant tool powered by Cohere models with RAG, consultants can ask conversational questions and get fast, accurate answers, along with citations to previous work by the experts – all based on millions of unstructured documents, notes, and authorship data.
SOLUTION
Cohere worked with the global consultancy to build a custom RAG solution based on Cohere Command, Embed, and Rerank. Using an intelligent assistant tool powered by Cohere models with RAG, consultants can ask conversational questions and get fast, accurate answers, along with citations to previous work by the experts – all based on millions of unstructured documents, notes, and authorship data.
How it works
STEP 1.
Unstructured knowledge base documents are embedded by Embed and stored in a vector database.
STEP 2.
Command interprets a user’s requests and creates queries across legacy and vector search sources.
STEP 3.
Rerank re-orders the responses based on relevance to original queries, improving the accuracy of the search results.
STEP 4.
Command synthesizes a conversational response, along with citations, back to the user.
Impact
Improved consultant productivity
Faster knowledge sharing
Better project staffing and client satisfaction
The Cohere Difference
Leading model accuracy
Cohere’s retrieval prioritizes accurate responses and citations
Accelerated enterprise deployment
Cohere’s models come with connectors to common data sources
Customization
Cohere’s models can be fine-tuned to further improve domain performance
Scalability
Cohere’s powerful inference frameworks optimize throughput and reduce compute requirements
Flexible deployment
Cohere models can be accessed through a SaaS API, on cloud infrastructure (Amazon SageMaker, Amazon Bedrock, OCI Data Science, Google Vertex AI, Azure AI), and private deployments (virtual private cloud and on-premises)
Multilingual support
Over 100 languages are supported, so the same topics, products, and issues are identified the same way