This project started in the cloud, moved to a VPS, and eventually became one of the reasons behind Thinkputer Local Private AI Server.

Why We Moved

The original system relied heavily on cloud services. It worked, but as the workflow grew, so did recurring costs, authentication requirements, service dependencies, privacy concerns and the number of external systems involved. Moving to a VPS gave us more control, but important workloads and data were still running outside our physical environment.

The final step was moving the solution to a Local Private AI Server (LPS). This brought AI processing, automation, documents and business data much closer together while giving us direct control over the infrastructure.

What Did We Gain?

• Greater control over AI, applications and business data
• Supported sensitive-data processing stays local
• Faster local processing for suitable workloads
• Less dependency on multiple cloud services
• No Thinkputer per-token charges for supported local AI
• Supported local workflows can continue without internet access
• One environment for AI, documents, data and automation

How Long Did the Migration Take?

The complete journey from cloud services to VPS and finally to a production-ready Local Private AI environment took months, including migration, redesign, testing, optimization and verification of the workflows.

What Can It Do?

The completed system can receive business documents, read and understand their information, extract and organize data, apply AI and business rules, store information, search documents and automatically perform multi-step workflows. It combines Document AI, Generative AI and Agentic AI with business automation.

Who Can Benefit Most?

This approach can be particularly valuable for accounting firms, law firms, healthcare organizations, engineering companies, financial and professional services, manufacturers and other organizations that process large volumes of documents or work with sensitive business information.

From a Project to Thinkputer

What began as an effort to solve our own cloud limitations demonstrated something much bigger: many AI workloads do not have to live entirely in the cloud. The experience became a foundation for Thinkputer—bringing Private AI, Local AI and AI Workflow Automation together on infrastructure the business controls.