#AI Migration #Legacy Systems #DEN-NO Solutions

Streamlining Legacy System Migration with AI

In a groundbreaking project led by DEN-NO Solutions, a Tokyo-based tech firm, collaboration with withone has demonstrated the power of artificial intelligence (AI) in significantly reducing the time and costs associated with migrating legacy systems. This recent achievement showcased a remarkable 60% decrease in both expenses and duration when compared to traditional migration methods.

The Legacy System Migration Challenge

Many enterprises are under pressure to upgrade their outdated legacy systems due to mounting risks and inefficiencies. However, they often find themselves at a standstill when presented with exorbitant estimates for migration. The primary obstacle to successful system migration lies in the traditional methods that heavily rely on manual processes for code analysis, coding, and testing.

This reliance on human labor limits the potential for cost and time savings. Recognizing this structural challenge, DEN-NO Solutions harnessed the capabilities of a proprietary AI agent working in tandem with human experts to tackle the issue head-on. The findings from the actual migration project have validated this innovative approach’s efficacy.

Project Overview

The migration project focused on updating a central service management system built on an outdated framework to a modern language and framework.

Key Details of the Project:

• - Target System: Core service management system with a scale of hundreds of thousands of lines of code.
• - Migration Details: Upgrading from FuelPHP to the latest PHP and Laravel 12.
• - Collaboration: Joint execution by DEN-NO and withone.

This project emerged from the necessity to mitigate security and business continuity risks that arose from the end of support for the legacy framework. Previously, multiple vendors were consulted, but the costs and timelines did not align, leading the company to postpone vital upgrades.

Results Achieved

The results from the implemented AI-driven migration exceeded expectations:

• - Cost Savings: Approximately one-third of the original estimate (over 60% reduction).
• - Timeline Improvement: Also achieved a duration reduction to approximately one-third of the initial expectations.
• - Quality Assurance: The system launched successfully in a production environment and maintained quality standards equivalent to those achieved by traditional migration methods.

(Note: Savings may vary depending on the target system and migration requirements.)

Mechanisms Behind the Significant Savings

The reduction in time and costs can be attributed to two innovative mechanisms: the implementation of a dedicated AI migration agent and the assurance of quality through human oversight.

• - The AI migration agent, specifically developed by DEN-NO, automates the entire migration process — from analyzing existing code to conversion and testing — allowing for substantial manual labor reduction.
• - Every output generated by the AI undergoes a thorough human review to ensure quality. This rigorous validation process entails the involvement of skilled technicians for essential tasks, including user acceptance testing and decisions regarding the migration to production environments. By leveraging AI for speed and cost efficiency while human experts safeguard quality, this division of responsibilities has enabled DEN-NO to achieve both reductions in expenses and timelines without sacrificing quality.

Consulting Services Available

DEN-NO Solutions extends its service to companies struggling to migrate due to prohibitive estimates for costs and timelines, regardless of the system's size or coding language.

Free Diagnosis Overview:

• - What to Provide: An overview of the current system and a portion of source code (NDA required).
• - Deliverables: A diagnostic report summarizing current risks and migration feasibility.
• - Cost: Free of charge (post-evaluation, companies may choose whether to proceed with a paid migration).

This methodology is provided under the service name