Dust
Open-source AI agent deployment platform that connects LLMs to company data in Notion, Slack, and Google Drive to automate knowledge work.
Dust is an open-source platform for building and deploying AI agents that connect large language models to company data sources - Notion, Google Drive, GitHub, Slack, and Salesforce - to automate repetitive knowledge work without exposing proprietary data to third-party LLMs. Unlike general no-code automation tools, Dust is designed for company-wide AI agent rollouts using retrieval-augmented generation (RAG). Founded in 2022 by Stanislas Polu and Gabriel Hubert (former OpenAI and Stripe engineers), Dust has raised $16 million in funding. The platform supports GPT-4o, Claude, Gemini, and open-source models as the underlying AI. As of 2025, Dust is deployed at companies including Qonto and Pennylane for internal knowledge management and support automation.
Key Features
- RAG-based knowledge retrieval connecting agents to Notion, Google Drive, GitHub, Confluence, and Slack
- Multi-LLM support - deploy agents on GPT-4o, Claude, Gemini, or Mistral as the underlying AI model
- No-code agent builder with workflow editor for chaining data retrieval and generation steps
- Slack-native deployment - agents respond to queries directly in existing Slack channels and DMs
- Role-based access control defining which employees can access which data through each agent
- Full conversation logs and analytics to audit agent responses and improve accuracy over time
- Open-source core on GitHub - self-hostable for organizations with strict data residency requirements
Use Cases
- Customer support teams deploying an AI agent trained on internal documentation to answer Tier 1 tickets
- Engineering teams giving employees a Slack bot that answers questions about the codebase and architecture
- HR teams creating an agent that answers policy and benefits questions directly from the employee handbook
- Sales teams deploying a competitive intelligence agent connected to CRM data and recent prospect research
Pros
- Open-source core means full auditability and a self-hosting option for data-sensitive organizations
- Multi-LLM support avoids lock-in to any single AI provider as models continue to improve
- Slack-native deployment means employees adopt the agent without any new tool or workflow change required
Cons
- Initial data source setup and permission configuration requires significant IT involvement for enterprise deployments
- RAG retrieval quality depends heavily on how well internal documentation is structured and maintained
- Smaller community and fewer prebuilt integrations compared to established platforms like n8n or Make
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