Agentic AI for Legacy Modernization

Legacy apps are dead weight.
CHAI™ makes them agentic.

Transform 18-month legacy migrations into 7-day agentic implementations — no source code required.

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One Workspace · Every Frontier Model

One conversation. Every model.
Modernize legacy on your terms.

CHAI Universe brings OpenAI, Anthropic, Google, Azure, Bedrock and more into a single agentic workspace.

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End-to-End Modernization · Discovery to Deployment

Migrate Anything. Modernize Everything. Break Nothing.

The most advanced Context Rich Harness with Reinforcement Learning.

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The Enterprise Modernization Challenge

80%
Enterprise apps remain trapped in legacy environments
$1.8T
Spent annually on maintaining legacy systems
67%
Digital transformations fail due to technical debt
45%
Migrations fail without deep assessment

AI-Blind Applications

Legacy apps can't see, learn, or act. Useless in an agentic AI future.

Expensive to Maintain

Legacy applications consume 60-80% of IT budgets while delivering minimal business value.

Difficult to Secure

Outdated systems create vulnerability gaps and compliance risks.

Resistant to Change

Monolithic architectures prevent rapid innovation and AI adoption.

MEET CHAI™

One Harness. Four Products. Zero Guesswork.

Assess. Transform. Validate. Govern. The complete Context Rich Harness powered by Agentic AI and Reinforcement Learning.

CHAI DART™

The Most Accurate App Intelligence

Tri-Vector analysis across runtime, workload, and code. 45% of migrations fail without deep assessment. DART solves this.

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CHAI Flow™

Agentic Orchestration at Scale

Multi-agent collaboration: Discovery, Assessment, Deploy, Transform, Observability & DevOps agents working in concert.

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CHAI Universe™

The Context Rich Harness

One harness unifying assessment, intelligence, and execution, with Sovereign AI and Reinforcement Learning built in.

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NEW

CHAI Compass™

Business Value Framework

Model ROI, score compliance, and define modernization goals. Know the value of every decision before you commit.

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See It In Action

CHAI Universe at Work

From legacy source code to a complete design document — generated in seconds, not weeks.

ATRS (Airline Ticket Reservation System) — Design Document
Table of Contents
01Executive Summary 02System Overview 03Layered Architecture 04Domain Model 05Service Layer & Fare Pipeline 06REST API 07Security Filter Chain 08Async Processing (JMS) 09Architecture Assessment 10Modernization Path
1. Executive Summary

ATRS is a multi-module Java/Spring web application for airline ticket reservation. Built on the TERASOLUNA framework (v5.10.0), it demonstrates enterprise-grade patterns for a transactional booking system.

Version1.11.0.RELEASE Java17 FrameworkSpring MVC 6.x, Spring Security 6.x ORMMyBatis DatabasePostgreSQL ViewJSP MessagingActiveMQ Artemis (JMS)
2. System Context
ACTORS Web User API Client HTTP / HTML REST / JSON ATRS APPLICATION Web Layer (JSP) REST API Layer Service Layer PostgreSQL ActiveMQ Artemis

Fig 2.1 — Actors interact via HTTP and REST; both funnel through the Service Layer into PostgreSQL and the JMS broker.

2.1 Core Features
A0–A2Authentication — login, logout, session mgmt B0–B2Ticket Operations — flight search, fare calc, reservation C0–C2Member Management — registration, profile update D1Reporting — async reservation history report
3. Layered Architecture
01 · PRESENTATION LAYER atrs-web JSP Views Forms & Validators Web Controllers REST Controllers 02 · APPLICATION LAYER atrs-domain Services DTOs 03 · DOMAIN LAYER atrs-domain Entities / Models Repositories 04 · INFRASTRUCTURE LAYER MyBatis Mappers JMS Templates Configuration EXTERNAL PostgreSQL ActiveMQ

Fig 3.1 — Five-layer stack with strict downward dependencies — presentation through infrastructure, terminating at external data & messaging.

4. Domain Model — Core Entities
MembercustomerNo, name, mail, creditNo FlightdepartureDate, flightName, vacantNum RouterouteNo, basicFare, airports ReservationreserveNo, totalFare, representative Passengername, age, gender FareTypediscountRate, reservationPeriod
5. Service Layer — 10 services across 4 domains
b0TicketSharedService · calculateFare, validate b1TicketSearchService · searchFlight(criteria) b2TicketReserveService · register, calcTotal a1 · a2AuthLogin / Logout · updateLoginStatus d1ReservationHistoryReport · async via JMS
5.3 Fare Calculation Pipeline
STEP 1 Basic Fare from Route route.basicFare STEP 2 + Boarding Class extra charge N/S class STEP 3 × Fare Type discount rate 0.3 – 1.0 STEP 4 × Peak Time ratio multiplier 1.0 – 1.5 OUTPUT Total Fare ¥ ( basicFare + extraCharge ) × discountRate × peakRatio = totalFare

Fig 6.3 — Four-step deterministic pricing pipeline composed by TicketSharedService.calculateFare().

6. REST API Layer — /api/v1
GET/api/v1/flight → FlightResource[] (200) POST/api/v1/ticket → TicketReserveResource (201) GET/api/v1/ticket/check → TicketReserveResource (200)
7. Security Filter Chain
HTTP Request Security Filter Chain WEB APP CHAIN · ORDER 2 Path: /** Form Login CSRF Enabled Session-based PBKDF2 / BCrypt /auth/dologin REST API CHAIN · ORDER 1 /api/v1/** HTTP Basic Stateless CSRF Disabled JSON error envelope ApiGlobalExceptionHandler

Fig 9.1 — Two independent filter chains: stateful Web (form-login, CSRF) and stateless REST (HTTP Basic, no CSRF).

8. Async Processing (JMS) — Report Generation
User Controller Service JMS Queue JMS Listener Reports Request report sendRequest(criteria) enqueue message Request accepted (ID) Async processing onMessage() createReport() PDF / CSV →

Fig 12.5 — User-facing request returns immediately with an ID; heavy report generation runs off-thread via JMS.

9. Architecture Assessment
Modularity4 / 5 — well-structured multi-module Maven project Maintainability4 / 5 — clean separation of concerns Security4 / 5 — comprehensive Spring Security Scalability3 / 5 — stateful design limits horizontal scaling Documentation2 / 5 — limited inline/external docs Overall3.5 / 5 — solid enterprise architecture with room for modernization
9.3 Technology Maturity vs Maintenance
IDEAL CONSIDER UPGRADE EVALUATE TECHNICAL DEBT Modern ↑ Legacy Low Maintenance High Maintenance → Spring 6 Java 17 PostgreSQL MyBatis ActiveMQ TERASOLUNA JSP

Fig 13.3 — Modern core (Spring 6, Java 17, PostgreSQL) vs. legacy UI/framework (JSP, TERASOLUNA) that warrants modernization.

10. Recommended Modernization Path
ContainersDockerize · deploy on Kubernetes (AKS/EKS/GKE) API DocsIntroduce OpenAPI / Swagger CachingAdd caching for airports, routes, fare types MonitoringSpring Boot Actuator + Micrometer + Prometheus CI/CDGitHub Actions pipeline — automated build & deploy
Prompt
Generate a source code design document for the ATRS application. Analyze the full codebase — modules, services, entities, architecture layers — and produce a comprehensive technical document.
Source
atrs-1.11.0.RELEASE (Java/Spring)
Generated By
CHAI Universe™ — Context Engine

From legacy codebases to full design documentation. Powered by CHAI Universe.

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Why Enterprises Choose CHAI™?

Sovereign AI Security

Train private AI models for internal use. Air-gapped, fortress-grade deployment ensures data sovereignty with zero leakage into public models. Your competitive advantage stays within your walls.

Continuous Transformation Observability

Real-time monitoring of applications, workloads, and infrastructure. Comprehensive tracking of costs, technical debt, risks, and sustainability metrics across your entire estate.

Conversational Application Intelligence

Ask natural language questions like "Which apps are ready to modernize?" or "Where are my modernization risks?" and get clear, contextual answers powered by Agentic AI.

Works With or Without Source Code

Ideal for brownfield and black-box environments. Scans source code, runtime behaviors, or deployment artifacts, all with the same depth of insight.

Actionable Modernization Recommendations

Optimal modernization paths from replatforming to refactoring and containerization. Maps dependencies and risks with unmatched precision.

END-TO-END PIPELINE

The Only Pipeline Built to Finish What Others Start.

Powered by CHAI Recipes for speed and Self-Healing Workflows for accuracy.

STEP 1
1
Assess
Full estate discovery: every app, dependency, and risk mapped across code, runtime, and workload
STEP 2
2
Plan
AI-generated modernization paths, prioritized by risk and business value, wave-ready from day one
Human in control
STEP 3
3
Execute
CHAI Recipes power agentic execution: battle-tested blueprints, 6X faster, zero manual guesswork
STEP 4
4
Verify
Self-healing test loops catch every gap, with audit-ready output for CMMC, IL6, and HIPAA
Human in control
STEP 5
5
Optimize
Reinforcement learning compounds every run. Each transformation makes the next faster and cheaper
WHY CHAI

Built Different. By Design.

Six reasons enterprises choose CHAI™ over everything else.

Tri-Vector Assessment

Three Dimensions. One Truth.

Source code + runtime + workload converge into unmatched precision. Builds dynamic context. Makes transformation predictable.

Sovereign AI

Your intelligence. Your walls.

Air-gapped deployment with zero data exfiltration. Intelligence stays within your organization. Your IP never leaves.

Unified Intelligence

One brain. Infinite reach.

Cohesive AI spanning your entire SDLC. Eliminates fragmented LLM usage. Optimizes LLM cost.

CHAI Recipes

Enterprise-approved blueprints.

Pre-orchestrated, governed modernization playbooks. Consistent, reusable, composable blueprints.

Reinforcement Learning

Gets smarter with every move.

Progressively enhances, refines and optimizes. Reduces LLM usage and cost. Compounds institutional knowledge.

Self-Healing Modernization

Verify. Correct. Repeat.

Auto tests on source and end states. Corrects course until verification passes. Production-grade accuracy.

900+
Apps Modernized on EKS
60%
Human Hours Saved
600%
Faster Modernization
4X
Migration Velocity
40%
Cost Reduction

Trusted by Leading Enterprises Worldwide

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Testimonials

"Leidos collaborated with CloudHedge to modernize NASA's applications. The user-friendly platform required minimal training and provided intelligent recommendations, enabling efficient application discovery and modernization."
Srini Iyer
CTO, Leidos
"Mantech has successfully used CloudHedge's Modernization Platform to migrate a 20+ year old complex logistics application in a matter of weeks: from Planning, to Design, to Implementation."
Renu Shankar
Director - Software Engineering, Mantech

From legacy mess to agentic dominance. Here's how

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Connect & Assess

Connect your applications, workloads, and infrastructure to CHAI DART™ for comprehensive discovery and analysis.

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App 1
DB
App 2
API

VM 1
App 3
Cache
MQ

App 4
Auth
VM 2
App 5

Intelligent Insights

CHAI performs non-intrusive scanning, reviewing runtime behaviors, source code, and infrastructure dependencies to identify transformation opportunities.

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RuntimeSourceDepsRiskCost

Personalized Roadmaps

Get AI-powered modernization recommendations aligned with your enterprise objectives, risk tolerance, and business goals.

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Legacy Monolith
→
Tri-Vector Scan
→
Risk Score: Low
Wave 1 — 12 Apps
→
Containerize
→
K8s Deploy
Wave 2 — 8 Apps
→
Refactor
→
Microservices
Wave 3 — 5 Apps
→
Re-architect
→
Cloud Native

Execute & Deploy

Export insights into CHAI Flow™ to automate code modernization and deploy optimized, modernized workloads at scale.

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⬡Source Code TransformationDONE
⬡Dockerfile GenerationDONE
⬡K8s Manifests & Helm ChartsDONE
◎CI/CD Pipeline ConfigRUNNING
○Production DeploymentPENDING

Kill the modernization bottleneck

CHAI transforms your legacy applications into agentic, AI-native systems. This isn't a vision. It's reality. Days to AI-native.

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Ready to Transform?

From data center exits to transformation roadmaps. From legacy Java to agentic AI.

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