Disconnected experiments
Prompts and model outputs still need someone to move data between systems.
AI workflow automation
Connect data, AI analysis, decisions, human approval and delivery in one observable workflow.
The operational gap
Teams need AI to act inside real processes without losing control.
Prompts and model outputs still need someone to move data between systems.
Important actions need review, context and a clear owner before execution.
One-off chats do not create a scheduled, reusable or auditable operating process.
Core capabilities
Combine AI, code, data transformations and reusable modules on one canvas.
Start manually, on a schedule, through a webhook or from another workflow.
Route outcomes by conditions and pause critical changes for a human decision.
Create artifacts and send results through configured delivery modules.
How it runs
Every step has inputs, outputs and a visible execution state.
Product proof
The visual editor keeps triggers, AI steps, conditions and approval gates in one reviewable definition.
A scheduled workflow reads operating data, identifies metric changes, drafts a Japanese summary and routes the result to an approval step before delivery.
Before: analysts repeat collection, comparison and summary work.
After: the workflow prepares the review package; a person remains responsible for release.
FAQ
AI workflow automation
Start with sample data, inspect every step and keep a person at the decision point.