Founded in 2019 in Toronto by ex-Google Brain employees and co-authors of the seminal paper on Transformer Architecture, Attention is all you need, Cohere AI has spent the past few years establishing itself as a key player in the artificial intelligence (AI) and Natural Language Processing (NLP) industry, developing advanced products and solutions for businesses. Their multilingual models are designed for enterprise use cases, prioritising data privacy, accuracy, and customisability at the core of their offering.

Cohere’s aims to thrive where other Generative AI providers might be falling short, aiming to reduce hallucinations and offer affordable pricing models. Their models can be seamlessly integrated into private environments, allowing users to deploy their AI technology directly to their workflows.

Cohere’s models

Cohere’s models are specifically designed to optimise business efficiency in order to improve outcomes and streamline operations. Accuracy is at the core of Cohere’s offering. From RAG to fine-tuning, they provide users with methods to improve the relevancy and detail of their outputs.

Command

Cohere’s ‘Command’ models are text-generation LLMs that can be deployed for a variety of text-based use cases, including text summarisation, chatbots, and content generation. Within this suite, Cohere has built out 3 models, including Command, Command R, and Command R+, which differ in terms of their context length. The latter two models also deliver high-performance RAG capabilities, allowing users to interact with the models based on a predefined list of documents that the model can refer to in order to generate outputs.

Command models are multilingual, trained on 10 different languages, thus making them accessible to global teams and allowing them to generate outputs from a variety of datasets in spite of their language.

Embed

Cohere’s ‘Embed’ models are designed to improve language understanding for semantic search through increased accuracy and RAG. They parse through data, understand its nuances, and return highly accurate outputs. Embed, and in particular Embed3 which is the latest and most advanced in the suite, is able to evaluate a query against a document and assess its relevance, allowing the model to prioritise documents by quality, making for a more accurate and fast retrieval. Like Command, Embed is trained on multilingual datasets.

Rerank

Finally, Cohere’s Rerank models are built to optimise enterprise search and retrieval, allowing users to extract the most relevant information from extensive and complex datasets. The model also offers high-performance RAG capabilities, sourcing the most relevant information from users’ proprietary data.

Cohere and RAG

Businesses are increasingly deploying RAG workflows into their AI strategies, and Cohere is at the forefront of these advancements. Retrieval Augmented Generation (RAG), is a technique that leverages knowledge and datasets outside of the vast volumes of data that the model is trained on. What does this look like in practice? With a RAG system, users can upload knowledge bases (datasets, documents, etc.) in order to provide the model with specific, internal, or proprietary information. This way, the model can generate the most accurate response for the user, with the most relevant data available. 

RAG systems improve the accuracy of responses and reduce the risk of model hallucination. Therefore, by integrating retrieval mechanisms, Cohere models can provide more accurate and contextually appropriate responses. Cohere R+ is particularly optimised for advanced RAG and is the most suitable model in their suite for technical and complex workflows.

Due to their extensive training, and the ability for RAG and fine-tuning, Cohere models are equipped for a range of use cases. For example:

• Knowledge Assistants: Using RAG capabilities, businesses can build knowledge assistant agents based on internal data that can engage in informative conversations, aimed at educating users or answering customer queries.
• Language translation: Cohere’s training on multilingual datasets make it perfect for language translation tasks, such as text generation in various languages for a multilingual content strategy, or customer assistance across different languages for global clientele.
• Content generation: Cohere’s language models are ideal for generating marketing copy, product descriptions, and blog pots. 

What Are the Benefits of Cohere Models for Businesses?

Specifically designed to enhance operational efficiency, Cohere also provides a range of other benefits for businesses.

1. Training: Cohere’s models are trained on diverse and extensive datasets to ensure a comprehensive understanding of language. These datasets include a wide range of languages, enabling the models to perform well in multilingual contexts. Training data comprises a mix of publicly available texts, web pages, books, and other forms of written content, ensuring the models are exposed to various writing styles and domains. This training also subjects Cohere’s models to extensive bias mitigation to ensure that outputs are free from judgement or inaccuracies. 
2. Data privacy: Cohere are committed to maintaining the privacy and integrity of their users’ data. Cohere won’t access customer data unless explicitly given access, and they use Private LLM deployment and opt-out options for users not wanting to share their data.

Cohere aims to stand out in the artificial intelligence industry against other key players, offering enterprise users access to a secure, cloud-agnostic platform in which they can leverage their own company data to receive optimised responses for the scalability and success of their business.