French startup Dust raised $16 million in Series A funding led by Sequoia Capital. With Dust, corporations can build custom AI assistants and deploy them to employees so they’ll work more efficiently.
But what’s interesting about Dust is the way it differs from other corporations working on enterprise agents or AI assistants in general. Unlike a consumer-facing tool like ChatGPT, Dust’s assistants are connected to a company’s data and documents. For example, when you create a recent assistant in Dust, you’ll be able to link it to Notion pages, documents stored in Google Drive, Intercom conversations, or Slack.
At the same time, unlike most AI startups working on enterprise agents, Dust believes that corporations should have several AI assistants, not only one. Each assistant might be useful for performing a specific set of tasks and solving common problems faced by a specific team.
More practically, support teams can use the Dust assistant, which is aware of each the knowledge base content and previous support interactions. This way, recent team members in the support team can ask a query to the @supportExpert assistant and get an appropriate answer.
HR teams can create an AI assistant that may answer questions about corporate policies – no need to search through Notion’s convoluted database. They may also create another assistant to create job descriptions based on previous job descriptions. Once again, this strengthens the position of the entire company and frees up the time of the HR team.
For engineering and data science teams, the use cases are fairly straightforward. For example, Dust’s assistant might know the company’s database schemas. You could ask @SQLbuddy in plain English to write a SQL query for your customer database.
One final example: Sales teams can generate email drafts based on CRM data and the prospect’s overall context. And if you would like to create your personal connectors or integrate Dust assistants into another tool, the company offers an API.
Rather than reinvent the wheel, Dust is focused on building a product that works for everyone. A few years after ChatGPT launched, most individuals are familiar with AI assistants (many even use them at work, despite it being against company policy). They know how to start a conversation, provide more details, and ask the AI assistant to rephrase its response.
Using Dust isn’t all that different, as corporations build conversational assistants using the platform. Employees can then go to the Dust web interface or communicate directly with assistants in Slack—that way, they might be @-mention’d in the middle of a conversation. Dust essentially wants to turn generative AI into an internal communication tool that everybody uses every day.
The startup now generates $1 million in annual recurring revenue, and some late-stage tech corporations like Watershed, Alan, Qonto, Pennylane, and PayFit are using it extensively.
Business banking startup Qonto estimates that 75% of its 1,600-person team uses Dust assistants every month. At Alan, the French medical insurance unicorn, 80% of the company uses AI assistants on a weekly basis. Accounting tech unicorn Pennylane has created 86 custom assistants with Dust.
In addition to Sequoia Capital, some of the startup’s existing investors are investing once again, including XYZ, GG1, Connect Ventures, Seedcamp and Motier Ventures.
The client-centric approach also signifies that Dust doesn’t create its own base model. When you create an assistant, you’ll be able to select the large language model you wish to use for that assistant. Dust has integrations with OpenAI (GPT), Anthropic (Claude), Mistral, and Google for its Gemini models.
There are quite a few startups working on enterprise platforms for building AI agents or assistants. Some names that come to mind are Brevian, Tektonic AI, Ema, Kore.ai, and Glean. Even Atlassian, the enterprise software giant behind Jira and Confluence, has launched its AI teammate, Rovo. Let’s see if Dust has found the right way to go to market with its easy onboarding strategy.