Move aging applications toward a more secure, maintainable and scalable architecture through assessment-led refactoring, replatforming, cloud migration, API enablement, data modernization and phased cutover.
Initial assessment and modernization roadmap: typically 5–7 working days for suitable scopes. Implementation timelines are confirmed after assessment.
Entry pricing is structured around a meaningful technical assessment rather than an unrealistic full-system teaser price. Broader implementation scope is confirmed after the current application, dependencies and migration risks are understood.
For organizations that need a defensible modernization path before committing engineering budget.
For a prioritized module, runtime, database, integration layer or technical-debt hotspot.
For multi-phase modernization requiring coordinated architecture, code, data, infrastructure and cutover work.
Pricing note: software modernization varies substantially by codebase size, technology age, data volume, integrations, compliance needs and the selected modernization strategy. The figures above are starting points, not fixed quotes for every system.
The process is designed to surface dependencies early, protect business continuity and avoid treating a complex legacy system like a greenfield rebuild.
Map applications, runtimes, modules, integrations, databases, users and operational constraints.
Review code health, support risk, security exposure, data complexity, testability and release process.
Choose what to retain, retire, replace, rehost, replatform, refactor or rearchitect first.
Validate the target approach on a bounded module, integration, runtime or platform component.
Deliver in increments with tests, observability, documentation and staged deployment controls.
Reconcile data, verify workflows, performance-test critical paths and prepare rollback.
Transition approved workloads, monitor behavior, hand over documentation and retire legacy components safely.
Legacy modernization can span application code, architecture, data, integrations, infrastructure and engineering practices. Scope is selected according to risk and business value.
Upgrade obsolete runtimes, libraries and frameworks while reducing fragile dependencies and hard-to-change code.
Move from tightly coupled designs toward clearer service boundaries and maintainable deployment units.
Modernize schemas, data access, migration workflows and storage technology with validation built into the plan.
Untangle point-to-point integrations and expose stable interfaces for modern apps, partners and automation.
Prepare applications for supported infrastructure, cloud services, containers and more repeatable environments.
Add confidence around change with automated validation, secure delivery controls and better production visibility.
Not every legacy component should be rebuilt. A portfolio may use several modernization strategies at once: stabilize one module, replatform another, replace a commodity capability and rearchitect only the areas where it creates business value.
Review Your Legacy EstateKeep a stable component when modernization cost or risk outweighs current business value.
Decommission unused or duplicated capabilities and archive required data safely.
Move commodity functions to a suitable product or managed platform instead of rebuilding them.
Move the application to a new infrastructure environment with minimal code change.
Upgrade infrastructure, databases, runtimes or managed services while preserving core behavior.
Restructure selected code and dependencies to improve maintainability, performance or deployment speed.
Redesign major system boundaries or rebuild when the current architecture blocks critical future needs.
Use multiple strategies across modules so modernization follows business priority instead of one blanket method.
Undocumented dependencies, data inconsistency, production-only behavior and fragile releases can turn modernization into a business-risk event. The delivery plan should explicitly manage them.
| Legacy risk | How we address it | Evidence before cutover |
|---|---|---|
| Hidden business rules | Workflow interviews, code tracing and characterization tests. | Acceptance scenarios and regression coverage. |
| Undocumented integrations | Dependency inventory, traffic review and contract mapping. | Integration tests and endpoint inventory. |
| Data migration errors | Mapping, repeatable scripts, validation queries and reconciliation. | Migration reports and rollback checkpoints. |
| Release disruption | Incremental releases, parallel-run planning and staged cutover. | Cutover checklist, rollback plan and monitoring. |
| Performance regression | Baseline critical workloads before architectural change. | Targeted load, latency and resource checks. |
| Knowledge concentration | Architecture records, runbooks, code documentation and handover. | Maintainer documentation and knowledge-transfer sessions. |
The exact target stack depends on your product, team and operating environment. These examples illustrate the kinds of technology layers a modernization program can address.
Older or tightly coupled technology that has become expensive, risky or slow to change.
Supported technology chosen around maintainability, deployment needs, integrations and operating model.
Legacy work is difficult because the application already carries years of business rules, integrations and operational assumptions. The delivery approach is designed around understanding that context before making irreversible changes.
Answers to common questions about strategy, timing, data, migration risk, technology and implementation.
Share enough context for us to understand the application and the reason for modernization. Avoid sending credentials, production data or other highly sensitive material in the first enquiry.
