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Shared intelligence for innovation.

Build custom AI assistants that leverage your team's internal knowledge in Slack, Notion, and Drive.

Dust is a sophisticated AI orchestration platform designed to break down information silos within modern enterprises. Unlike generic chatbots, Dust focuses on 'Programmable LLMs'—allowing teams to build custom assistants that are deeply integrated with their internal data stacks (Notion, Slack, GitHub, Google Drive). Its technical architecture is built on a proprietary Domain Specific Language (DSL) that allows developers to define complex chains of LLM calls, data retrieval, and logic. By 2026, Dust has positioned itself as the 'operating system' for team intelligence, moving beyond simple RAG (Retrieval-Augmented Generation) to provide agentic workflows that can perform actions across connected apps. The platform is model-agnostic, supporting state-of-the-art models from OpenAI, Anthropic, and Mistral, ensuring that enterprises can swap underlying models as the LLM landscape evolves. Its core value proposition lies in its ability to provide context-aware answers while maintaining strict data privacy and permissioning protocols, making it a favorite for engineering, sales, and operations teams that require high-fidelity information retrieval.
Dust is a sophisticated AI orchestration platform designed to break down information silos within modern enterprises.
Explore all tools that specialize in semantic search. This domain focus ensures Dust delivers optimized results for this specific requirement.
A declarative language to define complex prompt chains and data extraction logic.
Dynamically route different tasks to different models (GPT, Claude, Mistral) within a single workflow.
Vector-based indexing of all connected documents with automatic incremental updates.
Inherits permissions from source systems (like Notion) to ensure users only see data they can access.
Two-way integration that allows assistants to trigger external actions based on chat commands.
Dashboards tracking assistant usage, query accuracy, and data source health.
Pre-built logic structures for common roles like 'SDR Assistant' or 'Onboarding Bot'.
Create a Dust workspace and authenticate via Google or GitHub SSO.
Navigate to the 'Connections' tab and link organizational data sources (e.g., Notion, Slack, Google Drive).
Wait for the initial indexing process to complete; monitor progress in the 'Data Sources' dashboard.
Create a new 'Assistant' and define its specific scope using the Dust builder UI.
Select the underlying model (e.g., GPT-4o, Claude 3.5 Sonnet) based on task complexity.
Define instructions and retrieval strategies (e.g., semantic search vs. exact match).
Test the assistant in the Dust playground to verify accuracy and grounding.
Invite team members to the workspace and set granular access permissions per data source.
Deploy the assistant to Slack for real-time team interaction via the @Dust command.
Monitor usage logs and refine prompt templates based on user feedback.
All Set
Ready to go
Verified feedback from other users.
"Users praise the platform for its deep integration with Slack and the ability to cite sources directly, though some find the DSL has a learning curve."
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