Back office ops · Production
Dust: Horizontal AI agent infrastructure platform for enterprise deployment
The problem
Enterprises lacked a way to deploy AI broadly across all teams; developer tools required technical expertise, while vertical solutions could only penetrate a single department rather than the whole company.
Workflow diagram · grounded in source
1
Non-technical user creates agent
trigger
“allowing non-technical users to create agents”
2
Platform integrates company data
integration
“maintaining numerous integrations and handling diverse data types appropriately – from structured Salesforce data to unstructured Notion pages”
3
Agents execute via dependency graphs
ai_action
“Building dependency graphs of agents”
4
Broad organizational adoption
output
“88% daily active users in some deployments”
Reported outcome
Dust achieves 88% daily active users in some deployments and 60-70% weekly active users across entire companies.
Reported metrics
Daily active users88%
Weekly active users across companies60-70%
Reported stack
DustSalesforceNotion
Frequently asked questions
What did this team achieve with this AI workflow?
Dust achieves 88% daily active users in some deployments and 60-70% weekly active users across entire companies.
What tools did this team use?
Dust, Salesforce, Notion.
What results were reported?
Daily active users: 88%; Weekly active users across companies: 60-70% (source-reported, not independently verified).
How is this back office ops AI workflow structured?
Non-technical user creates agent → Platform integrates company data → Agents execute via dependency graphs → Broad organizational adoption.