Dust raises $40M to transform enterprise AI from isolated assistants into collaborative agents

Dust raises $40M to push enterprise AI past the single-player era

The Paris- and San Francisco-based platform's Series B is co-led by Abstract and Sequoia, with Snowflake and Datadog participating. Total funding is now north of $60m. Dust, the Paris- and San Francisco-based enterprise AI platform, has raised a $40m Series B co-led by Abstract and Sequoia, with participation from Snowflake and Datadog, the company said on Monday. The round takes Dust's total funding to more than $60m and follows a $16m Series A in June 2024, also led by Sequoia. Dust is selling a particular argument about what enterprise AI is missing: that the dominant product shape so far has been an assistant per person, with the context from each session disappearing back into a private chat window once it ends. The company calls that 'single-player AI', and frames its own product as the 'multiplayer' alternative: a shared workspace where agents and employees draw from the same projects, conversations, files, notifications, and to-do lists, governed centrally and connected to the systems the company already runs on. 'What will transform the way we work isn't the next best model or assistant,' said Gabriel Hubert, Dust's co-founder and chief executive, in a statement. 'It's going to be a completely new type of system that gives humans and agents shared, governed access to the same information and capabilities so they become true collaborators.' The framing is positioning as much as product: an attempt to draw a category line between Dust and the wave of single-user copilots from foundation-model labs and software incumbents. On the company's own numbers, Dust is now used by more than 3,000 organisations, reached 41,000 monthly active users in April, and has more than 300,000 agents deployed across its platform. The company reports 70% weekly active usage across its customer base and zero customer churn in 2025. The platform connects to over 100 data sources, layers in memory and agent analytics, and ships SOC 2 Type II certification and GDPR compliance with EU and US data residency, with a contractual commitment from major providers not to train on customer data. Customer datapoints sit underneath the marketing line. At Vanta, a 46-person revenue team estimates 400-plus hours saved a week, per CRO Stevie Case. Watershed cut a recurring data-mapping workflow from two to three hours to a few minutes at a 78% success rate. In Europe, Qonto's case study with Dust puts savings at around 50,000 hours a year across 50-plus specialised agents and 1,000-plus daily users. The category is getting crowded. Anthropic shipped ten financial-services agent templates inside Claude earlier this month, and Google, Microsoft, and OpenAI have all been pushing variants of agentic enterprise tooling. Sequoia's Konstantine Buhler framed Dust's bet as orthogonal: 'Most enterprise AI today is single-player: one person, one prompt, no compounding.' Abstract's Ramtin Naimi described AI Operators inside customers like Datadog and 1Password as already 'rewiring how the entire company works'. There is also a labour-market subtext. Where companies like Klarna have leaned into AI as a hiring substitute, Dust is selling employers a tool that explicitly assumes the workforce stays in place and gets leverage from agents rather than being displaced by them. The 'AI Operator' role, which the company describes as an internal builder inside Ops, Support, Marketing or Sales who configures and runs agent fleets, is the staffing model implied by the product. Dust was founded in February 2023 by Hubert and Stanislas Polu, who met at Stanford in 2007 and previously sold data analytics company TOTEMS to Stripe in 2014. Polu went on to a stint as a research engineer at OpenAI, working on mathematical reasoning under Ilya Sutskever; Hubert was chief product officer at French health-tech Alan. The Paris-incorporated company has more recently scaled its US operations out of San Francisco (per Sequoia's portfolio page). The Series B will push three things at once, on Dust's own brief: agents that improve as they are used, collaboration primitives that make humans and agents bidirectional co-contributors, and the orchestration and governance plumbing for enterprise scale. Run-rate revenue was not disclosed.

Multiplayer AI startup Dust raises $40M to help enterprises move beyond isolated AI assistants - SiliconANGLE

Multiplayer AI startup Dust raises $40M to help enterprises move beyond isolated AI assistants Dust, an agentic artificial intelligence startup that's trying to push enterprise workers away from isolated chatbots into a more collaborative, multiplayer ecosystem, said today it has raised $40 million in a Series B round of funding. Today's round was led by Abstract and Sequoia Capital and saw participation from two of the technology industry's biggest data powerhouses - Snowflake Inc. and Datadog Inc. It brings the company's total amount raised so far to more than $60 million. Dust, officially known as Permutation Labs SAS, believes that most enterprises today are stuck playing a "single-player" game, with each employee using various different chatbots, copilots and AI assistants that operate in siloes. There's very little collaboration going on in terms of the actual AI agents being deployed across organizations. So, when an employee prompts a chatbot for insights on a particular customer, for example, whatever it digs up will likely stay within that individual's private chat window. The context is never shared, creating a fragmented environment that leads to work being duplicated and a failure of organizational knowledge to compound. Though these isolated AI assistants do deliver some productivity gains on an individual level, Dust says they're not going to bring about the transformational change that enterprise AI has promised. One of the main problems is that existing AI tools have a tendency to reinforce this pattern of isolated effort. A salesperson, for instance, might spend an hour or so using AI to research a specific account, and then the next day a solutions engineer will go through the same process of research, using their own AI agent. This is a symptom, or a hangover, of the fact that traditional enterprise tools are designed primarily for one-to-one interactions. Because of this, most of the AI systems being used by organizations today don't create a shared memory of the work they've performed, and this is the challenge that Dust aims to solve. Its platform transforms AI agents from personal assistants into team players that can work and share their knowledge across the entire organization, with full governance built in. Paris-based Dust's technology is built on a shared collaboration surface or workspace that allows for humans and agents to coexist within the same projects, so others can access their conversations, to-do lists and artifacts. It's woven together by an intelligence layer that connects to more than 100 enterprise data platforms, including Slack, Notion and Salesforce, and even some specialized internal databases, so that agents can access the full organizational context. To go alongside this, Dust has developed a series of AI operators, which are nontechnical agentic employees that can perform roles in marketing, sales and support and build and deploy highly specialized agents without any assistance from the engineering department. The company also offers a cloud-hosted compute environment for processing files and generating documents, together with integrated memory loops. This allows the specialized agents to learn over time from human preferences and proactively make suggestions to the way they function. Dust co-founder and Chief Executive Gabriel Hubert said it won't be individual models or AI assistants that transform the way work gets done across enterprises. "It's going to be a completely new type of system that gives humans and agents shared, governed access to the same information and capabilities so that they become true collaborators, working with the same context, notifications, artifacts and goals to compound organizational impact," he insisted. "This is what we call multiplayer AI, and this is what we're building at Dust." This is one of the reasons why Dust is model-agnostic. The company doesn't build the underlying AI models itself, but lets customers choose which model they'd like to power the individual enterprise agents they rely on. It provides access to a broad range of frontier models, which are then integrated into the platform's governance layer to ensure they remain fully under the organization's control. It has been so far, so good for Dust, for the startup has enjoyed rapid traction, with more than 3,000 organizations globally relying on it for their multiplayer AI operations. Those customers have successfully deployed more than 300,000 agents so far, and the startup said it had seen zero customer churn in 2025, along with a 70% weekly active user rate. These numbers suggest its platform is becoming a staple for enterprise workloads, as opposed to being something experimental. Abstract General Partner Ramtin Naimi said he's backing Dust because there's only so much you can do with single-player AI. "Dust is multiplayer, so AI operators inside companies like Datadog and 1Password don't just use Dust, they build agents that collaborate across teams, learn from every interaction and rewire how the entire company works," he said. "It's a new operating model and category." Dust's impressive progress so far is matched by the pedigree of its founders. Hubert previously helped to scale AI adoption at the payments company Stripe Inc., while co-founder Stanislas Polu once served as a research engineer at OpenAI Group PBC, where he co-authored a number of groundbreaking papers on AI reasoning. The co-founders say they'll use the funding from today's round to accelerate the development of specialized AI agents that learn as they work and enhance the collaboration primitives that enable them to serve as the equals of the human workers they serve.