How do you use Dust to create AI agents?

Creating a Dust agent requires no code. Choose a model, connect your data sources such as Slack or Notion, then write the instruction that frames its role before publishing it to your team. An internal training path usually helps new teams get started.

What do reviews say about Dust?

Published reviews highlight how quickly teams get started and the real gains on targeted tasks. They are more measured about the size of the gain once usage extends to a whole team over several months.

What is Dust's pricing?

Dust charges per seat per month, with a Pro plan and an Enterprise plan. The exact price list changes regularly: check it directly with the vendor before making any commitment.

How does Dust improve productivity in the enterprise?

Dust automates repetitive tasks such as sorting emails or writing meeting minutes. Time savings show up mostly on use cases industrialised across several teams.

What are Dust's features?

Dust offers custom assistants for each business function, native integrations with existing tools, agent sharing between teams and automation of recurring tasks connected to your internal data.

How does Dust stand out from other AI tools?

Dust gives access to several AI models in a single interface rather than depending on one provider. This approach avoids rebuilding your agents every time the models on the market change. Dust's founders have publicly defended this multi-model choice since the company was created.

What are Dust's use cases in the enterprise?

Customer support, human resources, legal and data are among the most common use cases. Each agent stays connected to its own team's sources so that it remains relevant.

Dust AI: what reviews miss about how it is actually used

Dust AI: overview, features and enterprise use cases

At the end of 2025, 55% of French SMEs said they used generative artificial intelligence. Only 17% use it regularly. Between the two lies a gap where most of the Dust, Copilot or ChatGPT projects launched in companies end up. This article presents Dust, a French AI agent platform for the enterprise: what the tool does, how a team can create an assistant, its features by business function and its pricing. One example per business function shows how the internal knowledge base becomes the backbone of every AI product deployed, from customer service to data. Finally, the article points out what published reviews almost always forget to measure.

What is Dust and how do you create an AI agent on the platform?

Who founded Dust and when?

Dust is a French company founded in 2022 by Stanislas Polu and Gabriel Hubert, two former Stripe engineers. The company closed a funding round in May 2026 with Sequoia and Abstract, with participation from Snowflake and Datadog. It builds a tool designed to equip a whole team rather than a single user. Its two founders publicly champion this vision of collective use.

How does the Dust platform work?

The platform is built around three building blocks: a language model, connected tools and a knowledge base specific to the company. A Dust agent combines these three elements behind a chat interface.

The stated goal is to bring an AI agent closer to the real business context rather than leave it as an isolated generic model. This agentic approach goes beyond the simple standalone chatbot that can only respond to one specific action. No developer is needed to publish a first intelligent agent. The platform runs in a standard web browser, with nothing to install.

Which data and tools can Dust connect to?

Dust connects to the tools where work already happens:

- Slack and Notion for internal knowledge and team discussions - Google Drive and Google Sheets for documents and spreadsheets - Gmail, HubSpot and GitHub for sales and technical workflows

Each connector gives access to a scope of data the agent can query. Dust also connects to Zendesk for support. Between them, Slack, Notion and Google Drive hold most of a company's documents.

What are the steps to set up an agent without coding?

Creating an agent follows a guided path, without writing a single line of code:

1. Choose an AI model (GPT, Claude, Gemini or Mistral) 2. Select the data sources to connect 3. Write the instruction that frames the agent's role 4. Test the agent on real cases, then publish it to your team

Which AI models does Dust offer?

Dust does not develop its own model. The platform gives access to several generative AI models in a single interface: Anthropic's Claude, Google's Gemini, OpenAI's models and Mistral. This model-agnostic approach makes it possible to switch models without rebuilding an agent. A client can therefore choose between several providers depending on the use case. That option remains a strong argument against a single-provider alternative. A single interface brings several models together to handle a specific need without multiplying subscriptions.