Dust

What it is

Dust gives every team member a custom assistant grounded in your Notion, Slack, Drive, and ticket data. Strong fit for mid-market teams that want to deploy AI broadly without writing custom RAG.

Notes from using it

Dust's bet is that the future of work AI looks like "every team member has a custom assistant grounded in our company data," not "automated workflows running in the background." That framing fits some teams perfectly — knowledge-heavy organizations where the bottleneck is finding and synthesizing information, not automating tasks.

The integration depth across Notion, Slack, Drive, and ticket systems is the moat. Setting up RAG-style team assistants without Dust requires a real engineering project; with Dust it's an afternoon. For teams that don't have ML engineering capacity but want grounded AI across their docs, the value is immediate.

Where Dust lags: pure-automation workflows (Lindy, Zapier, Relevance all do this better) and very deep customization of agent behavior (LangGraph or CrewAI win for engineers). For knowledge-work teams that don't want to build, Dust is the cleanest fit. For teams that want autonomous workflows, look elsewhere.

Where it shines

• Deep integrations with the tools knowledge workers use.
• Strong audit trails for enterprise.

Where it falls down

• Per-seat pricing is steep at scale.
• Less powerful for fully autonomous agents vs. Lindy/Relevance.

Best fit for

If you're trying to put AI behind any of these functions, Dust is worth a look:

• AI for Customer Support — Tier-1 chat, email triage, knowledge-base answers, ticket routing.
• AI for Content Writing — Blog posts, landing pages, social copy, newsletters.
• AI for Lead Enrichment — Adding firmographic, technographic, and intent data to inbound and outbound leads.