Senior Solution Architect at Hitachi Systems India Private Ltd
Real User
Top 5
Dec 12, 2025
We adopted Cohere primarily for their command model to support enterprise-grade text generation and NLP workflows. There was a use case for one of our customers where they required automated text generation and summarization of long documents and draft creation for internal content, so we used Cohere's command model with AWS Bedrock. For another customer, there was a similar use case but they also wanted semantic search and RAG, and instruction-based responses for chat and workflow automation were required, so we used Cohere's command model for that.
Senior Data Scientist at a tech vendor with 10,001+ employees
Real User
Top 10
Dec 4, 2025
My main use case for Cohere is for LLM and chatbot development. I use Cohere to fill boxes about documents, specifically about tenders. Cohere helps me fill boxes about documents, and I work with docx documents for a private company.
My main use case for Cohere is to use a Cohere embedded model to create our own vector databases and check conversations. A specific example of how I use Cohere's embedding model for our vector databases or conversation checking involves abilities that take customer approvals and convert that information into vectors. I save this information in our own systems and also store small vectors on customer devices to use during custom customer requests. My use case involves indexing and saving small portions of information.
We founded this company two and a half years ago, and since the middle of 2022, we foresaw the trending of generative AI and large language models, so my startup is working on developing generative AI applications for our clients, including enterprises and a few other startups across America and Canada. I started using Cohere when we first got information from the community about their reranking models almost one and a half years ago. In some clients' projects, we were required to introduce reranking model in the RAG flow (Retrieval-augmented generation). In this flow, we use different components to allow users to select and pick up from the UI components, drag and drop to their flow to enhance their RAG pipeline. That's where we introduced Cohere models as one of the providers for reranking.
Sr Test engineer at a tech vendor with 10,001+ employees
Real User
Top 10
Oct 8, 2025
I am working on test automation, specifically an intelligent test automation framework. Based on the existing framework, which is handled in TypeScript and Selenium, I used Cohere intelligence to create new tests based on the test data and test cases that we provide. It will read through all the test cases in natural language, process them, analyze the internal working of our existing framework, and create the artifacts, test data, and test source based on the existing framework. Currently, we are using Cohere APIs. First, I used the chat in the application itself to identify how it works by providing RAG sources, including PDF and text files. After confirming it worked fine, we moved to find an API, and we are using that API to handle all these tasks. The APIs are very functional for all our current use cases, mainly the intelligent test automation.
I use it for a personal project, a Discord bot for my Discord server. I haven't used it that much, but so far it's amazing. I like the support team. They are very good.
Cohere provides a robust language AI platform designed for efficient implementation in various domains, offering advanced features for automation and data analysis.
Cohere delivers a scalable AI language model that facilitates automation in data-driven environments. Highly adaptable to industry-specific requirements, it supports tasks such as text generation, summarization, and anomaly detection. This flexibility, along with its integration capabilities, makes it valuable for tech-savvy users...
We adopted Cohere primarily for their command model to support enterprise-grade text generation and NLP workflows. There was a use case for one of our customers where they required automated text generation and summarization of long documents and draft creation for internal content, so we used Cohere's command model with AWS Bedrock. For another customer, there was a similar use case but they also wanted semantic search and RAG, and instruction-based responses for chat and workflow automation were required, so we used Cohere's command model for that.
My main use case for Cohere is for LLM and chatbot development. I use Cohere to fill boxes about documents, specifically about tenders. Cohere helps me fill boxes about documents, and I work with docx documents for a private company.
My main use case for Cohere is to use a Cohere embedded model to create our own vector databases and check conversations. A specific example of how I use Cohere's embedding model for our vector databases or conversation checking involves abilities that take customer approvals and convert that information into vectors. I save this information in our own systems and also store small vectors on customer devices to use during custom customer requests. My use case involves indexing and saving small portions of information.
We founded this company two and a half years ago, and since the middle of 2022, we foresaw the trending of generative AI and large language models, so my startup is working on developing generative AI applications for our clients, including enterprises and a few other startups across America and Canada. I started using Cohere when we first got information from the community about their reranking models almost one and a half years ago. In some clients' projects, we were required to introduce reranking model in the RAG flow (Retrieval-augmented generation). In this flow, we use different components to allow users to select and pick up from the UI components, drag and drop to their flow to enhance their RAG pipeline. That's where we introduced Cohere models as one of the providers for reranking.
I am working on test automation, specifically an intelligent test automation framework. Based on the existing framework, which is handled in TypeScript and Selenium, I used Cohere intelligence to create new tests based on the test data and test cases that we provide. It will read through all the test cases in natural language, process them, analyze the internal working of our existing framework, and create the artifacts, test data, and test source based on the existing framework. Currently, we are using Cohere APIs. First, I used the chat in the application itself to identify how it works by providing RAG sources, including PDF and text files. After confirming it worked fine, we moved to find an API, and we are using that API to handle all these tasks. The APIs are very functional for all our current use cases, mainly the intelligent test automation.
I use it for a personal project, a Discord bot for my Discord server. I haven't used it that much, but so far it's amazing. I like the support team. They are very good.