Process first
Automation begins with a real workflow and a measurable bottleneck.
AI process automation
Secure business process automation and AI agents using n8n, Make, and Claude with controlled data flow, testing, and maintenance.
Controlled AI
Models support classification and extraction where rules alone are insufficient.
Operations
Monitoring and testable flows reduce the risk of silent production failures.
Automation designed as a product
The goal is not a collection of disconnected automations. It is a controlled workflow across CRM, email, documents, and operational tools with AI used where it creates real leverage.
AI automation capabilities
01
01 - Process audit
Select automations with meaningful business impact.
- Workflow mapping
- Integration inventory
- Impact and risk assessment
02
02 - Implementation
Build controlled flows in the right tool for the task.
- n8n self-hosted
- Make integrations
- Claude where interpretation is needed
03
03 - Stability
Prepare automations for production ownership.
- Critical flow tests
- Monitoring
- Iteration backlog
How AI automation is implemented
Discover the process
Manual handoffs and repeatable work are made visible.
- Interviews
- System map
- Automation candidates
Rules, integrations, and AI responsibility are separated clearly.
- Tool selection
- Control points
- Data handling
Design the workflow
Operate safely
Production automations remain observable and maintainable.
- Testing
- Monitoring
- Ongoing changes
Where AI automation makes sense
Best where people repeatedly move or interpret data between systems and delays or errors are costly.
Best for
- B2B operations teams
- Service businesses
- SaaS teams scaling workflows
Typical outcomes
- Less manual processing
- Controlled AI assistance
- Stable automation platform
FAQ - Questions about AI automation
Tools, security, and production stability.
Why use n8n instead of only Make or Zapier?
n8n is useful for controlled and more demanding workflows, including self-hosting; Make remains suitable for quick SaaS integrations.
Can data remain under our control?
Yes. Architecture can use self-hosted n8n and limited model calls appropriate to the data policy.
What happens after launch?
The workflow can be handed over or developed through continuous operations, monitoring, and new automation iterations.
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