Dust raises another $16M for its enterprise AI assistants connected to internal data


French startup Dust has raised $16 million in a Series A funding round led by Sequoia Capital. With Dust, companies can create custom AI assistants and share them with their employees so they can work more efficiently.

But what’s interesting about Dust is what sets it apart from other companies working on enterprise agents or AI assistants in general. Unlike consumer-facing tools like ChatGPT, Dust assistants are connected to company data and documents. For example, when you create a new assistant in Dust, you can connect it to documents stored in Notion pages, Google Drive, Intercom conversations, or Slack.

Also, unlike most AI startups that work on enterprise agents, Dust believes that companies should have multiple AI assistants, not just one. Each assistant can be useful in performing certain tasks and solving some common problems faced by a particular team.

In more practical terms, support teams can use a Dust assistant that is aware of both the knowledge base content and previous support interactions. This way, new team members in the support team can ask the @supportExpert assistant a question and get relevant answers.

HR teams can create an AI assistant that can answer questions about corporate policies — no need to search the complex Notion database. They can also create a separate agent that can create job descriptions based on previous job descriptions. Once again, this empowers the company at scale and frees up time for the HR team.

For engineering and data teams, the use cases are pretty straightforward. For example, Dust Assistant can learn about the company’s database schema. You can ask @SQLbuddy to write SQL queries to your customer base in straight English.

One last example: Sales teams can prepare draft emails based on CRM data and the general context behind a potential client. And if you need to build your own connectors or integrate Dust Assistant into another tool, the company provides an API.

Image Credit: dust

Rather than reinventing the wheel, Dust focuses on creating a product that works for everyone. A few years after ChatGPT’s launch, most people are now familiar with the AI ​​assistant (many are even using it for work, even if it’s against company policies). They know how to start a conversation, follow up with more details and ask the AI ​​assistant to rephrase their answer.

Using Dust isn’t that different since companies are building conversational assistants with this platform. Employees can then go to Dust’s web interface or directly interact with the assistants in Slack – that way, they can be @-mentioned in the middle of a conversation. Dust basically wants to turn generative AI into an internal communication tool that everyone uses every day.

The startup now generates $1 million in annual recurring revenue, and is being heavily used by some late-stage tech companies, such as Watershed, Elon, Quanto, Pennylane, and PayFit.

Business banking startup Quanto estimates that 75% of its team of 1,600 are using the Dust assistant every month. At French health insurance unicorn Elan, 80% of the company’s people use the AI ​​assistant every week. Accounting tech unicorn Pennylane has built 86 custom assistants with Dust.

Apart from Sequoia Capital, some of the startup’s existing investors are investing once again, such as XYZ, GG1, Connect Ventures, Seedcamp, and Motier Ventures.

Taking a customer-centric approach also means that Dust is not building its own base model. When you build an assistant, you can choose the larger language model used for that assistant. Dust has integrations with OpenAI (GPT), Anthropic (cloud), Mistral, and Google for its Gemini model.

There are many startups working on enterprise platforms to build AI agents. Some names that come to mind are Bravian, Tectonic AI, Eema, Kore.ai and Glean. Even Atlassian, the enterprise software giant behind Jira and Confluence, has launched its own AI teammate, Rovo. Let’s see if Dust has found the right go-to-market method with its easy onboarding strategy.


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