DMAP AI: Powering Enterprise App Modernization at Scale

Enterprises are modernizing their applications for cloud-native architecture, data center exits, VMware exits and end-of-life software and hardware. But legacy code, limited access to subject matter experts, and a lack of tests and validation baselines, combined with manual processes, make modernization slow and risky.

Manual execution stalls migration. DMAP AI changes that by turning application modernization into a repeatable, automated and validated process.

What is DMAP AI?

DMAP AI is Newt Global’s platform for database and application modernization, built on an agentic AI architecture. It wraps discovery, remediation and Migration Acceptance Testing (MAT) with AI-assisted automation, governance and evidence.

The DMAP AI portfolio covers:

  • DB Modernization — Oracle to Oracle migration, Oracle to PostgreSQL migration, SQL to PostgreSQL migration, and app modernization for embedded SQL remediation for PostgreSQL targets
  • App Modernization — OS upgrades, tech upgrades, code vulnerability correction and code comprehension
  • Test Automation — migration validation, unit test automation and functional test automation
  • DevOps Automation — ADO to ADOS/GitHub Enterprise, GitHub Enterprise to GitHub Enterprise (Azure managed), and TFS migration to ADO/GitHub
  • PostgreSQL Day 2 — PostgreSQL support and server performance improvement

DMAP AI is built on four principles:

  • Automation — automation at scale for assessment, migration, app modernization and validation helps reduce cost and time
  • Analytics — rich analytics with extensive reporting provides deep insights, enabling faster decisions at every stage
  • Assurance — full testing coverage ensures high quality for app and DB migration through automated validation
  • Adaptive — a modular architecture makes it easy to extend the platform for new use cases and features

Why application modernization stalls

Business imperatives driving modernization:

  • Migration and modernization for cloud-native architecture
  • Data center exit
  • VMware exit
  • End-of-life software and hardware

Execution blockers that hold programs back:

  • Application knowledge gaps — legacy Java/.NET apps hide dependencies across runtimes, frameworks, app servers, libraries, configurations, security, DB calls and workflows.
  • Slow manual discovery — SME walkthroughs, documentation review, dependency mapping and test planning are inconsistent across large portfolios.
  • Code comprehension bottleneck — a lack of understanding of business logic and existing code creates high modernization risk.
  • Testing delays and escaped defects — coverage gaps and limited automation increase defects after release.
  • Release slippage risk — code changes outpace manual test updates and delay migration acceptance and cutover.
  • Lack of baseline — migration certification needs baseline behavior and data.

Coding assistants are not automation

Neither legacy methods nor AI coding assistants are sufficient for repeatable execution, release readiness and cutover confidence.

App modernization with DMAP AI: five stages

  1. Discover — rapidly builds a clear view of the application, its business functions, technology stack and modernization complexity. Identifies risks, blockers and upgrade priorities early.
  2. Migration Scope — converts discovery insights into a practical modernization plan. Prioritizes what to upgrade, what to retain, and where risk needs management.
  3. Baseline — captures how the current application behaves before any change. Creates a trusted reference point for testing and business validation.
  4. Modernize — uses AI-assisted automation to accelerate code, framework, dependency and runtime upgrades. Reduces manual remediation effort while preserving stable components where appropriate.
  5. MAT (Migration Acceptance Testing) — validates that the upgraded application works the same as the source. Compares pre- and post-modernization behavior, isolates gaps and produces audit-ready acceptance evidence.

DMAP AI capabilities

  • Automates discovery, dependency analysis, scope and remediation planning
  • Supports Java/.NET, runtimes, app servers, frameworks and container readiness
  • Captures baseline behavior and enables trace-driven test automation
  • Creates MAT evidence, defect isolation and release confidence
  • Enables parallel and repeatable modernization factory execution with end-to-end observability

DMAP AI helps convert modernization from a manual, high-risk program into a repeatable assessment, remediation, testing and certification factory, with 80% savings.

Modernization use cases

  • OS, app and web server upgrades
  • Containerization and cloud platform readiness
  • Monolith to microservices
  • DB modernization to 3-tier architecture

Case study: Java app modernization with DMAP AI

Demo-App is a 30-year-old legacy application with features for Reserved Centrex Management, DID (Direct Inward Dialing) Management, NCRI (Network Channel Resource Inventory) and cross-audit functionality for compliance and verification, among others. It has 30+ interfaces, including mainframe interfaces.

Current technology stack Modernized and migrated
RHEL 5.11 RHEL 9.7
JDK 1.7 JDK 17 (LTS)
Oracle 11g Oracle 19c (19.30)
Tomcat 7.x Tomcat 10.x
Connect Direct Connect Direct Secure +
FTP SFTP
HTTP HTTPS
JSP Taglib (2.0) 2.1
Servlet (2.4) 4.0

Results

  • ~80% reduction in manual effort
  • Built-in verification and validation
  • Trace-driven test automation
  • Accelerated migration timelines
  • Scalable, repeatable modernization framework

The DMAP AI advantage

  • 80% effort reduction
  • Error-free modernization with built-in test automation

To learn more about DMAP AI app modernization – Book Demo

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