Introducing Command R+: Our new, most powerful model in the Command R family.
CHALLENGE
A global financial consultancy wanted to build an executive AI assistant for a national telecom company that could support executive decision-making. To meet the client’s requirements, the solution needed to extract information from internal document stores and real-time data sources, and then perform calculations on the fly. Therefore, a purely model-based solution would be insufficient.
CHALLENGE
A global financial consultancy wanted to build an executive AI assistant for a national telecom company that could support executive decision-making. To meet the client’s requirements, the solution needed to extract information from internal document stores and real-time data sources, and then perform calculations on the fly. Therefore, a purely model-based solution would be insufficient.
SOLUTION
Using Cohere Command, Embed, and Rerank, with retrieval-augmented generation (RAG), the firm was able to leverage Command’s multi-step capabilities and external agents (e.g., calculators and stock price sources) to retrieve and manipulate structured data. With this custom solution, the models break retrieval and computation tasks into multiple steps, allowing executives to ask complex requests like “Explain how my services margins compare to regional competitors.”
SOLUTION
Using Cohere Command, Embed, and Rerank, with retrieval-augmented generation (RAG), the firm was able to leverage Command’s multi-step capabilities and external agents (e.g., calculators and stock price sources) to retrieve and manipulate structured data. With this custom solution, the models break retrieval and computation tasks into multiple steps, allowing executives to ask complex requests like “Explain how my services margins compare to regional competitors.”
How it works
STEP 1.
Unstructured knowledge base documents are embedded by Embed and stored in a vector database
STEP 2.
Command creates a structured work plan and queries to answer a user’s request to retrieve information from connected data sources and tools (e.g., calculators)
STEP 3.
Rerank re-orders the responses from search tools based on relevance to original queries, improving the accuracy of the search results
STEP 4.
Command triggers another step in the work plan if required, otherwise it synthesizes a conversational response, along with citations, back to the user
Impact
Greater executive and staff productivity
Faster decision-making
Immediate answers to complex, real-time questions
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