Startup Spotlight: Dust’s Multi-Model AI Platform

Startup Spotlight: Dust’s Multi-Model AI Platform

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Oct 21, 2026 / 8 min read Partner & Customer Value Copy post link Open in Claude Open in ChatGPT

Snowflake Startup Spotlight: Dust

Snowflake

Welcome to Snowflake's Startup Spotlight, where we ask startup founders about the problems they're solving, the products they're building and the lessons they've learned during their startup journey. In this edition, meet Dust's Co-Founder and CTO, Stanislas Polu, and learn how the company is building a collaborative AI platform to reduce the distance between your data and the decisions your teams are making.

What inspires you as a founder?

I'm inspired by technical shifts that force us to question old assumptions. Large language models have made intelligence abundant, but abundance creates a new problem: How does a company use changing sources of intelligence without giving up control of what makes it unique? As AI spreads across individual tools and vendors, the company's context, agents and ways of working can become fragmented. I want to build the layer that lets organizations absorb constant progress while keeping those assets and choices their own.

Explain Dust in a single sentence.

Dust is the collaborative AI platform that rewires the way companies work and enables them to control the intelligence they use and own the data they generate.

What problem does Dust solve? How did you identify that issue?

Companies are gaining access to more intelligence, but they risk losing control over it. Much of workplace AI is still adopted by one person, assistant or model at a time. That creates three forms of dependency: reliance on a single model provider, loss of control over the data and traces generated by agents, and limited visibility into costs and behavior. The traces matter. They capture how work was completed: the instructions, context, model and tool choices, outputs and human feedback. Over time, that record may become one of the company's most valuable AI assets, helping it evaluate workflows and develop systems tailored to how it actually operates. Companies shouldn't give that asset away by default. My co-founder, Gabriel Hubert, and I had seen the growing pains of coordination while working at Stripe, as it grew from roughly 150 people into a much larger organization. Information gets lost, decisions become harder to trace and teams stop sharing the same context. When LLMs became capable enough for serious work, our hypothesis was that companies didn't need another model. They needed a shared layer between humans, company data, existing systems and the available models. Dust is that layer. It lets teams work collaboratively with AI, switch among supported frontier and open source models, and govern the context, agents and traces their work generates. What one person builds can become a reusable company capability without becoming dependent on a single source of intelligence.

What’s the coolest thing your company is doing with data?

We're helping companies turn agentic traces into an asset they can understand, improve and retain. The data required to do a job rarely lives in one place. A single question can require warehouse data from Snowflake, account context from a CRM and qualitative information from Slack, Notion or call notes. But the result is only part of the value. The trace around it shows which context, instructions, models, tools and human decisions produced the work. Dust brings those elements into a shared company system. An AI operator can study what works to improve an agent or skill and make it reusable by colleagues. As the company changes models or providers, it keeps the accumulated context and learning around its workflows. In the future, that corpus could also help companies evaluate or train models for their specific work. The model may change, but the intelligence the organization builds around it should remain its own.

How does Snowflake fit into the Dust stack? What role does it play?

Snowflake is a connected source of structured business data that Dust agents can work with. Organizations can connect Snowflake to Dust so teams can query relevant warehouse data in natural language and combine it with context from other company systems. That matters because useful business questions often cross system boundaries. Snowflake can remain the structured data foundation and source of truth, while Dust helps humans and agents use that data as part of broader work. This is an important part of company-owned AI. A company should be able to keep Snowflake as its source of truth, apply the relevant permissions and control how that context is used by its agents, rather than rebuilding its data strategy around an AI vendor.

How does Snowflake enable you to push the envelope in your line of business?

Snowflake makes structured data usable beyond the data team. Paired with Dust, a business user can ask a question in natural language, use relevant warehouse data alongside other company context and turn the result into a repeatable workflow rather than a one-off analysis. This is the direction we care about: reducing the distance between a company's data and the decisions or actions it should inform, without weakening the company's control over that data. Access and control are not opposites. Data teams can encode trusted access and business logic, while more colleagues gain a practical way to use them.

As a founder and innovator, what is your take on the rapidly changing AI landscape, and how will it change what Dust is able to do?

The mistake is to treat AI as a race with one permanent winner. Models will keep changing in capability, cost, latency and specialization. Companies should not have to predict the winner or rebuild their operating layer whenever the frontier moves. Dust is multimodel by design. Organizations can choose among supported frontier and open source models and update those choices as the landscape changes, without rebuilding their company context and workflows. This is what sovereignty means to us in practice: not doing everything locally or rejecting external providers, but staying in control when a provider, price, policy or model changes. Companies will rent intelligence from many sources. The durable asset is what they build around it: how the organization works, what it knows, what it has decided and where human judgment matters. That layer...