Application Modernization
Modernize the core without betting the business.
Incremental, test-first modernization of mainframe, COBOL, .NET and Java applications, accelerated by AI-assisted code understanding and designed to deliver value every quarter.
Why it matters
Big-bang rewrites fail. Incremental modernization works.
Core systems encode decades of business rules, and their original authors have often retired. Rewrites that try to replace everything at once routinely overrun and get cancelled. Doing nothing carries its own risk: scarce skills, rising costs and an inability to integrate with modern data and AI.
We modernize incrementally. First we understand the system (now dramatically faster with AI-assisted code analysis), then we choose the right strategy per component and carve out capabilities behind stable interfaces, with automated tests proving behavior is preserved.
Strategies we apply
- Encapsulate: expose legacy capabilities via APIs to unlock data and AI now.
- Rehost / replatform: move to modern infrastructure with minimal code change.
- Refactor: restructure into modular services incrementally (the strangler pattern).
- Rewrite or replace: where the business case is clear.
- Retire: remove what no longer earns its keep.
Solutions in this practice
Start with the problem you have
Each solution can run on its own or as part of a broader program.
What we deliver
Capabilities
Delivered by senior architects and specialist engineers, with security and governance designed in.
AI-assisted code analysis
Documentation, business-rule extraction and dependency mapping using LLMs with human review.
Incremental refactoring
Strangler-pattern decomposition into services behind stable APIs.
Test automation
Characterization tests that lock in behavior before code changes.
Cloud-native replatforming
Containers, managed services and CI/CD for modernized apps.
Data modernization
Moving legacy data stores to modern platforms with reconciliation.
Risk-managed cutover
Parallel runs, feature flags and rollback plans.
Approach
How the engagement runs
Clear phases, clear exit criteria, and a working result at every stage.
Understand
AI-assisted code and data analysis, documentation and complexity scoring.
Strategize
Per-component strategy, roadmap and business case.
Modernize
Incremental delivery with automated tests and parallel runs.
Retire
Decommission legacy components and realize savings.
Typical starting engagement
Modernization Discovery
Fixed scope, fixed fee, and a clear decision at the end. Final pricing is confirmed after a short scoping call.
Scope this engagement →Starting at
- AI-assisted code and dependency analysis
- Business-rule documentation
- Per-component modernization strategy
- Roadmap, costs and risk register
- Pilot slice recommendation
FAQ
Frequently asked questions
Straight answers to the questions CIOs, CTOs and procurement ask us first.
What is legacy application modernization?
Updating or replacing older software systems to improve maintainability, cost, scalability, security and integration with modern data and AI, using strategies such as rehosting, refactoring, rewriting or replacing.
How does AI help modernization?
LLMs can accelerate code comprehension, documentation, business-rule extraction, test generation and translation, all with expert review. It shortens discovery and reduces risk, but it doesn't eliminate the need for engineering judgment and testing.
How do you avoid a failed rewrite?
By modernizing incrementally behind stable interfaces, proving behavior with automated tests and parallel runs, and delivering value every quarter rather than at the end.
Start here
Get a senior architect's view of your fastest path to value.
A 45-minute working session, not a sales pitch. Bring your toughest AI, data or cloud problem; leave with a point of view, a rough plan and a realistic budget range.
- Senior architects onlyNo account managers in the first meeting.
- Fixed-fee optionsClear scope, price and outcomes up front.
- Mutual NDA on requestBefore any detailed discussion.