You arrive here looking for what Claude AI is. Probably because someone mentioned it in a meeting, because you’re comparing AI vendors for your company, or because you’ve seen Anthropic being cited more and more in serious contexts. <\/p>
Claude is the language model developed by Anthropic. It has been around since 2023, but its real entry into the business world is recent: 2025-2026. At Imanta we implement it in companies that move real commercial and operational data, not in demos. <\/p>
This article covers what Claude is, what differentiates it from the rest and why it matters if your company operates in B2B.<\/p>
Claude is a large language model created by Anthropic, a company founded in 2021 by former OpenAI researchers. Its design starts from one premise: an enterprise AI has to be predictable, explainable and safe. <\/p>
On a technical level, Claude belongs to the same family as ChatGPT or Gemini. It receives text, returns text. But there is a difference that is noticeable in real use: Claude doesn’t just complete sentences, it reasons<\/strong>. <\/p> The difference matters. A sentence completion model is useful for writing an email. A reasoning model can read a 40-page contract, identify three problematic clauses, justify why, and propose alternative wordings. It’s another category of homework. <\/p> Anthropic publishes several models within the Claude family – Opus, Sonnet, Haiku – designed for different load volumes and complexity. Details on each are covered in the next article in the series. <\/p> If you want to see how we apply this to specific business operations, it is documented on our Claude page.<\/a><\/p> In consumer, an AI that makes things up is an anecdote. In B2B, it’s a problem. <\/p> Three reasons why Claude enters serious companies:<\/p> It’s not just another generative AI to make demos. It’s infrastructure on which to support decisions that matter. That’s how we deploy Claude in companies<\/a> that already have a technology stack in place. <\/p> A concrete example, without actual customer figures:<\/p> A sales manager closes the quarter with 80 open deals in the CRM. He knows that not all of them will close, and he knows that he has one week to focus on where it really counts. The question is: where? <\/p> The usual flow: export to Excel, call the team one by one, cross-reference with memory. Hours of work, decision based on intuition. <\/p> With Claude connected to the CRM, the question is straightforward: “Give me the deals with more than 60 days without activity, amount over 30,000 \u20ac, in advanced stage, and propose me a follow-up email for each one”.<\/em><\/p> Claude reads the pipeline. Cross-references date of last communication, amount, stage, email history and notes. Returns a list: 12 candidate deals to re-prioritize, a line about why each, and a draft email tailored to the context of each conversation. <\/p> The director reviews, adjusts, decides. The difference is not that Claude makes decisions for him. The difference is that the analysis that used to cost him half a day is done in five minutes, and the time he saves is spent talking to clients. <\/p> That’s what separates a productivity tool from an operational intelligence layer.<\/p> The above is the concept. The actual application depends on two questions that should be answered before starting. <\/p> First, which Claude product do you use<\/strong>? Claude.ai for individual use is not the same as the API for integrations, or Claude Code for technical teams. We break it down in the next article in the series<\/a>. <\/p> The second: what you compare it to<\/strong>. If you are evaluating ChatGPT, Gemini or Copilot, this is the Claude vs ChatGPT vs Gemini comparison for B2B<\/a>. <\/p> Claude is a reasoning language model, it is designed to fit into enterprise infrastructure, and the difference with the rest is noticeable when the data it handles really matters.<\/p>Why Claude matters in B2B<\/h2>
Claude in practice: commercial pipeline analysis<\/h2>
Before proceeding: product and comparison<\/h2>
In a nutshell<\/h2>