Enterprise Enablement for Tidhar
Turning Rapid AI and Low-Code Solutions into Enterprise Applications
Industry
Real Estate / Construction
Services
Technology
Claude Code, AWS, Azure, Workato, Power Automate, Copilot Studio
TL;DR
- 1.Created a two-track qualification model for smaller solutions that need enterprise enablement and larger solutions that require direct involvement in design and implementation.
- 2.Defined a governance panel that connects CISO and budget approval with architecture, permissions, performance, backup, monitoring, cost, and relevant data requirements.
- 3.Established a standard DevOps path for approved solutions, including ownership, documentation, monitoring, and a mechanism for returning non-compliant solutions with a remediation list.
- 4.Introduced FinOps, token economics, ROI, and reusable components as decision criteria so investments can be evaluated on business value and operating cost — not only POC success.
The Challenge
At Tidhar, different users created small solutions with Claude Code and Low-Code tools, while broader initiatives were developed with external suppliers. Several solutions created value, but the working environment was fragmented across AWS, Azure, Workato, Power Automate, and Copilot Studio.
Tidhar needed a consistent route for approving solutions, assigning durable technical ownership, measuring ROI, controlling operating costs, and reusing components across initiatives rather than leaving successful experiments as isolated applications.
- 1
Fragmented Technology Landscape
Solutions were spread across multiple cloud, automation, and AI environments, making architecture, ownership, and operational standards difficult to apply consistently.
- 2
From Personal App to Enterprise Service
Fast solutions created by different users needed a repeatable path for security, permissions, performance, backup, monitoring, maintenance, and long-term responsibility.
- 3
Cost, Duplication & Reuse
The organization needed visibility into cloud and token economics, less duplication between suppliers, and a way to turn recurring components into shared internal infrastructure.
The Solution
Two Qualification Tracks
Dofinity.AI divided the portfolio into two routes: smaller solutions requiring enterprise qualification, and broader solutions requiring direct involvement in planning and implementation. This lets governance depth match the scale and risk of the initiative.
Enterprise Qualification Panel
A governance framework was defined to specify what every solution must satisfy before organizational approval, including consideration of Agent Center. The panel connects CISO and budget approval with architecture, permissions, performance, backup, monitoring, and cost. For AI solutions, it also covers FinOps, token economics, data retention, data repositories, and requirements relevant to the real-estate and construction environment.
Standard DevOps & Reusable Building Blocks
The DevOps team implements the requirements in the existing infrastructure, while AI developers reshape solutions into a form that can be operated, maintained, and controlled. A solution that does not meet requirements returns with a gap and remediation list; an approved solution moves into a standard DevOps path with ownership, documentation, and monitoring. Recurring components can then become shared infrastructure for future solutions.
Success
The process gives Tidhar a practical way to preserve the speed of AI and Low-Code experimentation without leaving valuable solutions as personal applications that cannot be maintained at enterprise scale.
It also supports lower duplication across suppliers, stronger internal knowledge, and investment decisions based on business value and operating cost — not merely whether a proof of concept worked.
Ready to govern AI at enterprise scale?
Let's turn fast AI experiments into controlled, auditable enterprise services.