Education
Teach AI as a workflow, not a stack of tools
When a class jumps between Midjourney, Runway, Krea, and Nano Banana, students learn each tool in isolation — not how to combine them. Teachers lose track of who did what, licenses pile up, and the actual learning gets buried.
What you can do
Run group projects with per-student budgets
Each student gets their own credit budget so nobody does the work for the rest of the team.
Track who did what, which model, when
Teachers see every generation in the dashboard, so individual evaluation lives inside group work.
Hand out access for the duration of the course
Codes activate at course start and deactivate at the end — no license sprawl between cohorts.
Build the brief on a shared board
Split participants into teams and assign practical work with capped credits per user.
Recommended
Dual is the default for teaching AI as a workflow. The shared canvas, per-user credit budgets, and teacher dashboard mean evaluation works without leaving the platform.
Customer stories
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Guides
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The problem with learning AI through ten different tools
When a team adopts AI by jumping between Midjourney, Runway, Krea, and Nano Banana, they learn very little about each tool in isolation. Real learning is something else: knowing how to combine them inside a real workflow, accounting for cost in an integrated way, and learning how to split tasks across a team.
Your AI stack will be irrelevant in 18 months
Nokia dominated smartphones. BlackBerry owned enterprise. Then the iPhone arrived. The same consolidation is happening with AI tools right now.
Built for education
Book a demo and we'll walk through education use cases with your stack in mind.