AI-powered app Modernization Services ––

Turn Legacy Applications Into Modern Engineering Assets.

InfoMagnus helps enterprises modernize legacy applications using AI-assisted engineering, structured workflows, GitHub-native delivery practices, and SONYX, our innovative modernization IP, to reduce risk and accelerate delivery.
Building Value ––

Modernization Needs More Than AI.

AI can accelerate modernization, but it cannot replace the system around it. InfoMagnus brings the structure, testing, documentation, governance, and engineering expertise needed to modernize legacy applications with speed and control.
AI Needs Direction.
AI can generate code, but it needs clear goals, defined tasks, and expert review. InfoMagnus gives modernization work the structure needed to produce usable, reliable results.
Stability Comes First.
Modernization cannot put the business at risk. We strengthen testing, documentation, and validation so teams can improve legacy systems without breaking what already works.
Speed Needs Control.
Fast delivery only matters when quality holds. InfoMagnus combines AI-assisted engineering, SONYX, and GitHub-native workflows to help teams move faster while keeping governance, security, and delivery discipline intact.
Our Core Motions ––

Modernize Applications With Governance Built In.

We modernize legacy applications through a controlled delivery model that connects assessment, AI-assisted engineering, testing, validation, and GitHub-native workflows from the start. This creates a faster path to modernization without losing governance, stability, or delivery confidence.
Legacy Application Assessment
Understand the current system before changing it.

At InfoMagnus, we analyze application architecture, dependencies, documentation gaps, vulnerabilities, unsupported frameworks, and operational risks before modernization begins.
AI-Assisted Modernization
Accelerate modernization using AI-guided engineering workflows and specialized modernization agents.

Applications are broken into structured tasks that improve delivery speed while reducing operational risk across the modernization lifecycle.
Testing & Functional Validation
Modernization fails when organizations lose confidence in existing functionality.

We help strengthen automated testing, validates application behavior, and maintains deployment integrity throughout delivery to reduce regression risk.
GitHub-Native Delivery
Modernization is tied directly into modern engineering workflows.

GitHub Copilot, GitHub Actions, deployment automation, testing workflows, and governance controls are integrated throughout the modernization process.
AI-Powered App Modernization —
How We Cut Application Modernization Time by 55% Using GitHub Copilot.
Explore Case Study
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Silhouette of a person standing in a large space with light coming through an open doorway, symbolizing opportunity or direction.
Silhouette of a person standing in a large space with light coming through an open doorway, symbolizing opportunity or direction.
Innovation In action ––

We Built A Modernization System Around AI with SONYX.

A multi-agent modernization platform designed to accelerate product engineering and SDLC execution inside GitHub-native workflows. SONYX breaks modernization into structured, consumable tasks that specialized AI agents can execute with greater consistency, governance, and operational control.
Built Around GitHub: The 7-Phase InfoMagnus Modernization Framework.
Phase 1: LEARN
We don't start with code generation. We start with understanding.

Every phase in the framework exists because skipping it creates downstream rework, and we've seen what happens when teams skip phases.
Phase 2: TEST
Establish a behavioral baseline before modernization begins.

We build test coverage against current system behavior, targeting at least 70% coverage to reduce regression risk.
Phase 3: STABILIZE
Address only critical vulnerabilities and operational bugs before modernization begins.

For TBS-TimeWarp, InfoMagnus prioritized 70 CVEs, including 23 critical and 47 high-risk issues.
Phase 4: PLAN
Translate the architecture understanding into a tactical migration roadmap.

Define success criteria per phase, identify risk controls, and break large work into focused, measurable chunks.
Phase 5: SCAFFOLDING
Build the foundation for incremental migration using the Strangler Fig pattern, with adapters and facades that let old and new systems coexist while features move gradually.
Phase 6: DEVOPS
Implement CI/CD pipelines, automated testing at every commit, and incremental deployments.

Automated quality gates mean verification is built into the process, not bolted on at the end.
Phase 7: MAINTAIN
Documentation is not a deliverable you produce at the end of a project. It's a living artifact you maintain throughout.

This phase establishes the continuous improvement loop that keeps the modernized system from accumulating new debt.
Explore More About SONYX.
Explore Sonyx
Modernize Applications Without Losing Control.
Application modernization does not need to become a multi-year rewrite project. With the right AI-native engineering model, organizations can modernize legacy systems faster while improving governance, testing, security, and operational reliability.

Explore how InfoMagnus helps enterprises modernize critical applications with AI-assisted delivery and GitHub-native engineering.
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