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Ihor NikolenkoAlmost every business owner I talk to about language models asks the same question within the first two minutes: "Where does my data go?" Contracts, client correspondence, financial reports, a knowledge base — you want to hand all of it to a model so it can answer questions from employees and customers. And it's terrifying to hand any of it to a service built for millions of consumers rather than for companies with lawyers and regulators to answer to.
This question has become urgent right now because AI has stopped being a marketing department's experiment and has moved into processes where a mistake or a leak costs real money: support, document management, internal search, working across multiple languages. At this stage, choosing a platform is no longer about "which model is smarter" — it's about "which model will work inside my own infrastructure, with my documents, and show me where its answer came from."
This guide covers Cohere: a company that built language models for enterprises from day one, not for chatting with the general public. Where it came from, how its model lineup is structured, what RAG with citations means and why it matters for business, how to deploy a model inside your own infrastructure, and when it makes sense to choose Cohere over bigger names.
Where Cohere Came From
Cohere was founded in 2019 in Toronto by three researchers who came out of Google Brain: Aidan Gomez, Nick Frosst, and Ivan Zhang. Gomez is one of the co-authors of "Attention Is All You Need," the paper that introduced the transformer architecture — the same foundation every modern language model is built on. Working at Google, they saw firsthand that large language models could transform how we work with text, and at the same time, they saw the gap: the technology existed, but any business that wasn't Google had no access to it.
The strategic decision that defined the company was to not compete for consumer attention. While OpenAI and Anthropic were grabbing headlines with chatbots, Cohere focused on a single segment: enterprises that need powerful, customizable, and secure language capabilities without the constraints of consumer platforms. Everything else follows from that — from the model lineup to the deployment options.
What Sets Cohere Apart from Other Platforms
In short: its priorities. Where a consumer platform optimizes for the chat experience, Cohere optimizes for four things that specifically hurt companies.
- Data privacy and control. The models can be used through an API, through major cloud providers' marketplaces, or deployed inside your own private environment — your own cloud VPC or your own servers. For a bank, a healthcare company, or a government agency, this often isn't an "option" — it's the only condition under which a project is possible at all.
- Customization. Fine-tuning on your own data and terminology, tailored to specific tasks rather than a one-size-fits-all assistant for everything.
- Multilingual support. The generation, search, and ranking models work natively across dozens of languages — not "English plus translation," but native text handling. For a company operating in multiple markets, that removes an entire layer of problems.
- Integration with what you already have. The platform was built as a set of building blocks to embed into existing systems — search, support, document management — rather than a standalone product you have to "migrate" to.
The Model Lineup: Who Handles What
Cohere's main advantage for business isn't a single model — it's a chain of specialized models that cover the full cycle of working with corporate data. Here's how they break down.
| Family | Task | Where It's Used |
|---|---|---|
| Command | Generation, conversation, instruction-following, tool calling, document-grounded answers | Support assistants, agents, document processing, RAG |
| Embed | Turning text into vectors for semantic search | Knowledge base search, clustering, classification |
| Rerank | Reordering retrieved passages by relevance to the query | Improving search precision before generating an answer |
| Aya | Open multilingual models from the research division | Research, low-resource languages |
The Command family consists of the flagship generation models: the Command R generation, Command R+, and then Command A. They're built for long-context work, tool calling, and, most importantly, generating answers with source citations. Embed is what makes document search work by meaning rather than keyword matching. Rerank is the model that takes dozens of retrieved passages and puts the most accurate ones on top; in practice, it's usually the piece that turns "search that finds something" into "search you can trust." Aya is Cohere's research division's family of open multilingual models, notable for covering languages that major platforms usually ignore.
RAG with Citations: Why It's the Killer Feature for Business
RAG — Retrieval-Augmented Generation — is an approach where the model doesn't answer from "memory," but instead relies on documents that were retrieved and placed into its context. For a business, this is fundamental: the model shouldn't "know" your vacation policy — it should find it in your database and paraphrase it. Cohere built its entire platform around this idea, and here's what the full chain looks like.
- Indexing. Your documents are split into chunks, each chunk is turned into a vector through Embed, and stored in a vector database.
- Retrieval. The user's query is also turned into a vector, and the closest matching chunks by meaning are pulled from storage.
- Reranking. Rerank reorders the retrieved chunks so the ones that actually answer the question end up on top, not just the ones that are thematically similar.
- Generation with citations. Command receives the query and the selected chunks and produces an answer in which every claim is tied to a specific source.
That last step is what most "chat with your documents" tools are missing. An answer with no citation can't be verified, which means you can't trust a decision to it. An answer with citations can be checked by an employee in ten seconds, and they'll see right away if the model is relying on an outdated document. That's the difference between a toy and a tool you can put in front of a lawyer.
Where to Apply This in Your Business
- A support knowledge base. An agent, or the customer directly, asks a question and gets an answer with a link to the relevant section of a manual. Complex cases get escalated to a human along with the context that's already been found.
- Search across internal documents. Policies, contracts, technical documentation — semantic search instead of "Ctrl+F across fifty PDFs."
- Contract and report analysis. Extracting key terms, comparing versions, answering "where in this contract are the penalty clauses" with a precise reference to the section.
- Working across multiple languages. One knowledge base, answers in Ukrainian, Polish, German, or English, with no separate translated copies to maintain.
- Agents for business processes. Cohere is also building out a platform for agents that don't just answer questions but take actions inside corporate systems, under the same data-control principles.
Cohere or OpenAI: How to Decide
This isn't a question of "which one is smarter." It's a question of which constraints are non-negotiable for you. I recommend answering five questions — the answer usually becomes obvious on its own.
- Can your data leave your infrastructure? If not, you need a platform with private deployment, and Cohere is in a strong position here.
- How central is document search to your task? If the core job is finding and citing, the Embed-plus-Rerank-plus-Command chain gives you a ready-made pipeline instead of assembling one from different vendors.
- How many languages are you working in? Multilingual support is a core property of Cohere, not an add-on.
- Do you need a broad consumer ecosystem? Plugins, ready-made assistants, a huge community — OpenAI has the edge here.
- Are you ready for engineering work? Cohere is a platform for embedding into systems, and it pays off in the hands of a team building a system, not one that just wants to "plug in a chatbot."
In practice, companies often use both: one for general tasks, and the other for the parts of the system dealing with sensitive data.
How to Get Started
Getting started is simple: sign up at the official cohere.com, get an API key, use the dashboard's sandbox to play with the models without writing code, and grab the SDK for your preferred programming language. The first pilot I recommend is RAG on your own documents: take twenty or thirty real policies or manuals, build the chain with indexing, reranking, and citation-backed generation, and have five employees ask ten real questions each. Within a week, you'll know whether this works for you — and you'll have evidence, not just impressions.
And if you'd rather first figure out exactly which processes in your business are worth handing to a language model, which aren't, and how to fit this into your existing system, book a 60-minute consultation: we'll break down your processes, pick a first pilot, and put together a 30-day plan.
The Short Version
- Cohere was founded in 2019 in Toronto by former Google Brain researchers, including a co-author of the paper that introduced the transformer architecture.
- The company has built models for enterprises from the start: privacy, customization, multilingual support, and embedding into existing systems.
- The lineup is a chain of specialized models: Command for generation and agents, Embed for search, Rerank for precision, and Aya for open multilingual research.
- The key advantage is RAG with citations: every claim in an answer is tied back to a source document.
- Deployment options include API, cloud marketplaces, or your own infrastructure — decisive for regulated industries.
- The choice between Cohere and OpenAI comes down to data constraints, how central search is, language needs, and your team's readiness for engineering work.
- The best first pilot is RAG on your own documents, tested with real questions from real employees.
Frequently Asked Questions
Can I use Cohere without sending my data to the cloud?
Yes — that's one of the platform's main distinguishing features: the models can be deployed inside your own private environment, whether that's your own cloud VPC or your own servers. The exact terms and options depend on your contract, so if you're in a regulated industry, discuss this with Cohere's team before starting the project.
Why is RAG with citations better than a regular "chat with your documents" tool?
Because you can verify the answer. A regular chat produces confident-sounding text, and you have no way of knowing if it's grounded in your document or in "general knowledge." An answer with citations shows exactly which chunk each claim came from, so an employee can immediately spot an outdated or wrong source. For decisions someone is accountable for, that's a non-negotiable feature.
Why do I need a separate Rerank model if search already found the documents?
Because vector search finds things that are thematically similar, not necessarily the ones that actually answer the question. Rerank looks at the query and each retrieved chunk together and puts the ones that truly answer it on top. In practice, it's one of the cheapest ways to meaningfully improve answer quality without changing anything else.
Is Cohere suitable for a small business, or is it just for large enterprises?
The API is available to any company, and a small RAG pilot can be built by a single developer. But the platform's full value — private deployment, customization, handling sensitive data — really pays off where those requirements genuinely exist. If your volume is small and you have no data restrictions, compare it against alternatives based on the task at hand, not the brand name.
Which task should I start a pilot with?
Start with one that has a lot of documents, a lot of repeated questions, and a measurable outcome: a support knowledge base or internal policies. Within a week, you'll see exactly how much time citation-backed search saves.
