Anthropic is an AI safety and research company, best known for Claude, its family of AI assistants and models. Structured as a public benefit corporation, it builds AI systems designed to be safe, interpretable, and reliable for both consumer and enterprise use.

Every review is from a verified employee

Overall

4.9

17 reviews

5
15
4
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Rating by dimension

Setup and Onboarding as a user4.4
Customer support3.8
Ease of use4.6
Value for money5.0
Features & innovations4.7

AI overview

Anthropic receives high praise for its AI tools, with most reviewers highlighting significant productivity gains and enhanced operational efficiency. The tools are particularly valued for their ability to automate tasks and support complex workflows.

Rating by product

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Showing 17 reviews

Quick review
Employee at Pipecorn

Frequent User·France

5
August 2026

I'm genuinely thrilled with what Anthropic has built. Their work has reshaped how nearly every company operates today. From how teams research and write to how we make decisions and ship faster. It's rare to see a product change day-to-day business so quickly and so broadly. Anthropic has set a new standard.

Quick review
Employee at Pipecorn

Frequent User·France

4.5
August 2026

Really great tools, allow us to ship features faster than never

Classic review
Employee at Revio

Administrator·France·used it 1 to 2 years

4
August 2026

+10 projets Claude code

Claude fait gagner un temps considérable au quotidien (rédaction, code, recherche, automatisation)

Quick review
Employee at Pleo

Frequent User·France

4
July 2026

Requires a lot of time, too complex for simple requests, whatever the model

Classic review
Employee at PrettyWhale.ai

Administrator·France·used it More than 2 years

5
July 2026

Best IA tool to date

I use Cowork ann Code on a day to day basis, the tools are great to accelerate operations and open new perspective, whatever the subject.

Quick review
Employee at Innovorder

Frequent User·France

5
July 2026

New models and features very powerfull for marketing teams.

Classic review
Employee at Guimini

Frequent User·France·used it 3 to 12 months

5
July 2026

Un vrai gain de temps pour la production commerciale

J'utilise Claude tous les jours dans un contexte commercial B2B : rédaction de propositions, mails clients, scripts d'appel, contenus LinkedIn et supports de formation. La qualité rédactionnelle est excellente, le ton s'adapte vraiment à mon style (factuel et concis) et les itérations sont rapides. Un gain de temps réel sur toute la partie production de documents et de communication, sans perte de contrôle sur le résultat.

Quick review
Jeanro K.

Other·Frequent User·Hunter·USA

5
July 2026

Use it every day to code

Quick review
Arnaud G.

Sales·Expert admin·Mom3nt·France

5
July 2026

I run a 3-person B2B SaaS startup in Paris. I am the CEO, not an engineer, and Claude is the reason my sales operation runs like a team of six. We use Claude inside our product, running different Claude models depending on the task, the heavier reasoning on one, the faster generation work on another. Choosing per use case rather than defaulting to the biggest model is what made the economics work. The part I did not expect is that Claude also replaced my ops stack. I built a family of Skills in Cowork that runs two full workflows. The meeting prep routine reads my calendar, decides which meetings actually deserve a briefing, pulls the account history from my CRM, my inbox and my call recordings, checks the prospect's recent LinkedIn activity, and produces a pre-filled discovery brief delivered before the call. Thirty minutes of manual prep per meeting, down to zero. The outbound routine chains targeting criteria, pain mapping, voice guidelines and account research into a runnable prospecting workflow across my sales tools, then reads back what actually converted and returns ranked recommendations. The architectural decision that made Skills work: separating doctrine from execution. Who we sell to, what we sell against and how we write live in read-only skills that never change during a run. The orchestrators consume them. That is what stopped the system from drifting after a few weeks, and I would not have found it without Claude pushing back on my first design. What I would improve. MCP connector reliability is uneven, some servers drop mid-run and the failure is silent rather than explicit. Skills lack observability: when a chain of four skills produces a bad output, I cannot see which one degraded. Long unattended runs still need more supervision than I want to give them. Usage limits are the real constraint on running these workflows daily at the scale I want. None of that changes the rating. This is the only tool in my stack I could not replace.

Quick review
Olivier D.

Founder / CEO·Frequent User·Sparkway·France

5
July 2026

Game changer for building AI agents & code