For operations and knowledge teams building internal AI tools, Dust is a no-code platform that lets you assemble custom AI assistants wired into your company's internal data and workflows, without writing code.
Presence & Market Position
#6 of 22
Orchestrators & Agentic Workflow
▲ 66 60-day move
Measures earned, engagement-weighted share of voice across the GTM voices panel. Methodology →
Dust is positioning itself as a collaborative workspace where operations and knowledge teams build internal agents around company data and recurring workflows. Its market presence is rising among GTM builders who treat agent creation as an operating discipline rather than a developer-only task. Practitioner use cases center on agents that qualify requests, surface customer context, answer security questions, and turn internal knowledge into specific work outputs. The company’s recent partner integrations reinforce the multiplayer model: people and agents sharing a common context across teams instead of keeping each assistant isolated.
Relevance AI
Lindy
CrewAI
Vellum
InngestClay, Doctolib, Alan, Qonto, PayFit, Pennylane, Watershed, Malt
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Business Profile
Dust launched in 2023 out of Paris, founded by Gabriel Hubert and Stanislas Polu, and has grown to roughly 143 people within two years, a fast headcount curve behind one core bet: AI at work compounds across a team only when agents share context in a common workspace instead of living inside individual chat windows. The company reports $7.3M in ARR and a $21.8M valuation as of 2025, with roughly $21.5M raised, and lists customers including Clay, Doctolib, Alan, Qonto, PayFit, and Watershed. Public reporting puts its footprint at several thousand customer organizations and hundreds of thousands of deployed agents, with a steady cadence of product updates through 2026.
Agent Readiness
Measures how easily your agents can build on it — API, MCP, CLI, SDK, docs depth. Methodology →
Dust ships a developer platform that pairs a RESTful API with an official TypeScript/JavaScript SDK for creating, versioning, and running agents programmatically, plus llms.txt-formatted docs built for agents themselves to read. Its own agent builder is a growing MCP consumer, adding new MCP-connected tools (Contentsquare, Monday.com, Snowflake, and others) through 2026 alongside frequent model upgrades. The build-out points toward agents as first-class platform citizens, not just a chat feature bolted onto the product.
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