Dust is a no-code platform that lets anyone in your company build AI agents connected to your tools and data – achieving 70% weekly adoption rates at companies like Doctolib, Qonto, and Clay.
Why Dust Matters Right Now
If you’re running a B2B SaaS company in 2025 and haven’t figured out your AI agent strategy yet, you’re already behind. Not “a little behind” – like seriously behind. Because while everyone’s been debating whether AI agents are real or hype, companies like Clay, Qonto, Doctolib, and Alan have been quietly using Dust to achieve 70%+ weekly AI adoption rates across thousands of employees.
That’s not a typo. 70% weekly usage across 3,000+ person organizations.
Let me tell you about the most interesting AI infrastructure play you might not know about yet.
The Dust Story: OpenAI Researcher Meets Stripe PM
Dust was founded in February 2023 by two second-time founders who knew each other from their first startup (TOTEMS, a data analytics company acquired by Stripe in 2015). The founding duo is fascinating:
Stanislas Polu spent three years at OpenAI’s research team studying mathematical reasoning in language models. Before that, he was at Stripe as a software engineer. He’s École Polytechnique + Stanford pedigreed, and he literally watched GPT-2 write its first coherent paragraph at OpenAI. That “oh shit” moment convinced him that LLMs would fundamentally reshape enterprise software.
Gabriel Hubert went from Stripe (where he drove geographical expansion and product) to Chief Product Officer at Alan, one of Europe’s fastest-growing insurtech companies. He’s also École Polytechnique + Stanford, and he’s the product thinker to Stan’s AI researcher brain.
They raised €5M in seed from Sequoia Capital in 2023, then came back in June 2024 with another $16M Series A – also led by Sequoia – at what sources suggest was around a $100M+ valuation. Total raised: $21.5M.
The team is only 66 people and they hit $7.3M ARR as of mid-2025. That’s over $110K ARR per employee – which is exceptional for this early stage. They’re Paris-based but increasingly global.
What Dust Actually Does (And Why It’s Different)
Here’s what makes Dust different from the 10,000 other “AI assistant” companies flooding your LinkedIn:
1. Multi-Agent, Not Single-Assistant Philosophy
Most AI tools give you one big dumb assistant that tries to do everything poorly. Dust’s core insight? A team of specialized agents beats one generalist assistant every single time.
You can spin up agents for:
- Customer support (reads your Zendesk tickets, help docs, past conversations)
- Sales enablement (connected to your CRM, competitive intel, call transcripts)
- Engineering documentation (pulls from GitHub, Notion, Linear, past PRs)
- Data analysis (queries your databases, generates visualizations)
- Content creation (understands your brand voice, past campaigns, style guides)
Each agent is narrow, specific, and actually good at its job. Because it’s pulling from the exact data sources it needs and nothing else.
2. Model Agnostic (This Is Huge)
Dust doesn’t bet on one AI model winning. They integrate:
- GPT-4 and o1 from OpenAI
- Claude 3.5 Sonnet and Opus from Anthropic
- Gemini from Google
- Mistral models
You pick the right model for each task. Some queries need Claude’s reasoning. Some need GPT-4’s speed. Some need Gemini’s multimodal capabilities. Dust handles the orchestration.
This is strategically brilliant. When OpenAI has a latency spike at 9am PST (which you can literally time by the API slowdowns), your team isn’t dead in the water. When Anthropic releases a better model, you can switch instantly.
3. Actual Enterprise-Grade Data Connectivity
Dust connects to everything your company actually uses:
- Google Drive, Notion, Confluence (knowledge bases)
- Slack, Microsoft Teams (conversations)
- GitHub, Linear, Jira (dev tools)
- Zendesk, Intercom (support)
- Salesforce (CRM)
- And they just shipped Model Context Protocol (MCP) support for custom integrations
But here’s the critical part: granular permissions. Your engineering docs don’t leak to sales. Your financial models don’t leak to marketing. Dust respects your existing access controls and enforces them in the AI layer.
SOC 2 Type II certified. GDPR compliant. HIPAA-compatible. Zero data retention option. US/EU data residency. SSO and SCIM for enterprise.
This isn’t “AI washed” security theater. This is actual enterprise-grade infrastructure.
4. No-Code Agent Builder (The “Vibe Coding” of AI)
Here’s where it gets really interesting. Anyone on your team can build an agent in literally minutes:
- Name your agent and describe what it should do
- Connect the data sources it needs access to
- Give it specific instructions and constraints
- Deploy it to your workspace or specific teams
- Done
I’m talking 5 minutes from idea to deployed agent. No coding. No prompt engineering PhD required. No waiting for IT.
Want an agent that reads all your customer feedback from Zendesk, Gong calls, and Slack, then generates a weekly synthesis? Build it.
Want an agent that monitors your competitors’ documentation and blogs for changes? Build it.
Want an agent that helps new hires understand your codebase by answering questions from your GitHub repos and internal docs? Build it.
The “democratization” of AI agent creation is real here, and it’s powerful.
The Metrics That Actually Matter
Let’s talk cold hard numbers, because that’s what we care about at SaaStr:
Adoption Metrics (These Are Insane)
- Doctolib: 70% weekly usage across 3,000 employees
- Qonto: 75% monthly usage (targeting 100% by year-end)
- Clay: 100% adoption rate while scaling 4x
- Average customer retention: stickiness comparable to Slack (per Sequoia)
Think about that. Slack is the gold standard for “everyone in the company uses it daily.” Dust is achieving similar stickiness for AI agents. That’s the signal.
Customer Impact (Revenue Per Employee Stuff)
- Malt: Cut support ticket closing time by 50%
- Qonto: Saving 50,000 hours annually (that’s 24 FTEs at 2080 hours/year)
- Alan: Engineers reporting 20% faster project completion
- Pennylane: Agent evolved from support tool to operational backbone
These aren’t “we saved 10 minutes per day” vanity metrics. These are “we’re running a materially different business because of this tool” transformations.
Business Model & Pricing
Dust runs a clean two-tier SaaS model:
Pro: €29/user/month (~$32 USD)
- Minimum 1 user
- All advanced models (GPT-4, Claude, etc.)
- Unlimited messages (fair use policy)
- All integrations
- 1GB storage per user
- One workspace
Enterprise: Custom pricing (100+ users)
- Everything in Pro
- SSO (Okta, Entra, JumpCloud)
- SCIM provisioning
- Custom data limits
- Priority support
- US/EU data hosting
- Multiple workspaces
They also offer programmatic usage pricing (API, Google Sheets, Zapier) for automation use cases.
At $32/month per seat, if you get 70% of your company actually using it weekly, the ROI is obvious. You’re replacing or dramatically reducing spend on:
- Point solution AI tools ($20-40/user/month each)
- Consultant time building custom agents
- Engineering resources maintaining homegrown solutions
- The opportunity cost of slow AI adoption
Why This Matters for Your B2B Business
I spend a lot of time thinking about AI deployment in B2B. Here’s what Dust gets right that most companies miss:
1. They Solved the “Which Model?” Problem
Every SaaS founder I talk to asks: “Should we build on OpenAI? Anthropic? Host our own?”
Dust’s answer: “All of them, and let the use case decide.”
This is so smart. You’re not betting your product roadmap on one vendor. You’re not rewriting everything when a better model drops. You’re infrastructure-agnostic from day one.
2. They Made AI Agents Actually Useful (Not Just Chatbots)
Most “AI assistants” are just chat interfaces that hallucinate answers. Dust agents can:
- Search across your actual company data (not just chat history)
- Execute actions in other tools (create tickets, update CRM, post to Slack)
- Chain multiple operations together (research → analyze → summarize → post)
- Run on schedules (daily reports, weekly summaries, monthly reviews)
This is the difference between a chatbot and actual software. Dust agents are software.
3. They Nailed the Bottleneck
The bottleneck for AI adoption in enterprises isn’t AI capability. It’s:
- Security/compliance concerns (solved: SOC 2, GDPR, access controls)
- Integration complexity (solved: native connectors, MCP)
- User adoption (solved: anyone can build agents, no-code)
- ROI measurement (solved: clear productivity gains, measurable outcomes)
Dust systematically attacked each of these. That’s why they’re seeing 70%+ adoption while most enterprise AI tools sit at 10-20%.
4. The Platform Play Is Real
Dust isn’t selling you 10 different AI products. They’re selling you one platform that replaces 10 products.
Your copilot for coding? Dust agent connected to GitHub. Your sales assistant? Dust agent connected to Salesforce and Gong. Your customer support AI? Dust agent connected to Zendesk and your help docs.
One vendor. One security review. One contract. One invoice. One admin panel.
CFOs and CIOs love this. Probably more than they should admit.
The Competitive Landscape
Let’s be real about who Dust competes with:
Direct Competitors:
- Glean (search-first, not agent-first)
- Cohere (more AI infrastructure, less end-user product)
- Various verticalized solutions (single-use case)
Adjacent Threats:
- Microsoft Copilot (bundled with M365, but limited extensibility)
- OpenAI’s enterprise product (single-model lock-in)
- Vertical-specific AI tools (point solutions)
Dust’s moat is the combination of:
- Multi-model flexibility (vendor independence)
- No-code agent builder (democratized creation)
- Enterprise-grade security (actual compliance, not theater)
- Proven adoption metrics (70% weekly usage vs industry 10-20%)
That’s a real moat. Not a huge moat yet – they’re only 2 years old – but a real one.
Customer Profile: Who Should Buy Dust?
Dust works best for:
Company Size: 50-5,000 employees (sweet spot is 200-1,000)
- Below 50: probably just use ChatGPT Team
- Above 5,000: need even more custom enterprise features
Industry: Knowledge work businesses
- SaaS companies (obviously)
- Financial services
- Healthcare/life sciences
- Professional services
- Tech companies
Key Indicators You Need Dust:
- You have tribal knowledge scattered across 5+ tools
- Your team already uses AI tools, but inconsistently
- You care about data security (regulated industry)
- You want to build custom AI workflows, not buy 10 point solutions
- You have a distributed team that needs async knowledge access
Red Flags (Not a Fit):
- You’re pre-product-market fit (too early)
- You don’t have meaningful data in connected tools yet
- You want an out-of-the-box solution with zero customization
- Your team is extremely change-resistant
The Bottom Line: Should You Care About Dust?
If you’re a B2B founder/executive: Yes, you should 100% try Dust. Even if you don’t buy it, you need to understand what best-in-class AI deployment looks like. The companies using it are achieving 4x productivity gains on specific workflows. That’s not hype, that’s measurable ROI.
If you’re a VC: This is a real business with real metrics. $7.3M ARR on $21.5M raised with 66 people is capital efficient. 70% weekly adoption rates suggest genuine product-market fit, not just pilot purgatory. Sequoia clearly sees it (they led both rounds).
If you’re building an AI product: Study what Dust did right:
- Model agnostic from day one
- Enterprise security wasn’t an afterthought
- They made agent building accessible, not just usable
- They picked a wedge (knowledge workers in 100-1000 person companies) and dominated it
If you’re a customer: Try it. The ROI case is pretty simple:
- $32/user/month
- If each person saves 2 hours/week (conservative)
- That’s 104 hours/year
- At $50/hour loaded cost = $5,200 in value
- ROI: ~13x
And that’s assuming only 2 hours saved per week, which based on customer metrics is way conservative.
What’s Next for Dust
Based on their trajectory and the market, here’s where I think they’re headed:
Near-term (2025):
- Scale from 1,000 to 5,000+ customers
- Push from $7M to $20M+ ARR
- Build more pre-built agent templates (reduce setup friction)
- Ship more powerful workflow automation (multi-step agents)
- Enterprise expansion motion (land with 50 seats, expand to 500+)
Medium-term (2026-2027):
- $50-100M ARR
- Expand beyond knowledge work into ops workflows
- M&A interest from Microsoft, Salesforce, ServiceNow, or a platform player
- Potential IPO path if they hit $100M ARR with strong unit economics
The exit path is pretty clear: strategic acquisition by a platform player who wants an AI agent layer, or continue building toward IPO if they can sustain 3x growth for 3-4 more years.
Technical Deep Dive: How Dust Actually Works
For the technical folks, here’s what’s under the hood:
Architecture: Dust is essentially an orchestration layer that sits between users and multiple LLM providers. They handle:
- API management across providers
- Context management (what data to inject into prompts)
- Tool calling/function execution
- Response synthesis and formatting
- Access control and security
Data Pipeline:
- Connectors ingest data from your tools (Google Drive, Slack, etc.)
- Data is chunked and embedded (vector representations)
- Embeddings are stored with access control metadata
- When a query comes in, relevant chunks are retrieved
- Context + query goes to appropriate LLM
- Response is formatted and returned
Key Technical Choices:
- Multi-model support (not locked into one vendor)
- Vector search for retrieval (not just keyword search)
- Stateful conversations (context persists across messages)
- Action execution (can call APIs, not just chat)
- Scheduled jobs (agents run on timers, not just on-demand)
This is all stuff you could build yourself… but it’s 12-18 months of engineering work and ongoing maintenance. Or you pay $32/user/month and it just works.
Meet Dust in Person
Want to see Dust in action? They’ll be at SaaStr AI London on December 1-2, where you can meet the team and get a hands-on demo.
This is your chance to:
- See real customer workflows, not demo-ware
- Ask tough questions about security and compliance
- Understand how agents actually get built and deployed
- Meet Stan and Gabe (the founders are actually technical and can go deep)
- Talk to other companies using Dust about their experience
If you’re serious about AI deployment in your company, this is worth the trip.
Register for SaaStr AI London here
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Final Thoughts
There are a million AI tools launching every week. Most are just ChatGPT wrappers with a fancy UI. Many will be dead in 18 months.
Dust is different because:
- Real adoption metrics – 70% weekly usage isn’t a fluke
- Thoughtful technical architecture – model agnostic, security-first
- Strong founding team – OpenAI researcher + Stripe/Alan product leader
- Actual customer ROI – 50% support time reduction, 20% faster projects
- Capital efficient growth – $7.3M ARR on $21.5M raised
This is a real company building real infrastructure for how AI gets deployed in enterprises. Not everyone needs it. But if you’re a 100-1000 person knowledge work business trying to figure out your AI strategy, Dust should absolutely be on your shortlist.
The companies using it are moving faster than their competitors. And in B2B, velocity compounds.
Want to go deeper on AI deployment strategies? Come to SaaStr AI London on Dec 1-2 and let’s talk. And swing by the Dust booth while you’re there.
SaaStr AI App of the Week is a weekly series highlighting the most interesting AI tools actually being used in production by B2B companies. Not just demos. Not just pilots. Actually deployed, actually working, actually generating ROI.
