01
Application & dependency assessment
Map runtimes, libraries, data flows, scheduled jobs and integrations. Identify business-critical behavior, constraints and the areas that need closer investigation.
Loading page…
Move established applications forward without losing the business knowledge inside them. Upgrade technology, simplify integrations and improve delivery through scoped changes, tested releases and a clear recovery plan.
Record critical behavior, dependencies and a performance baseline before choosing the migration boundary.
Implement a scoped upgrade with stable interfaces, regression checks and a plan for data or component coexistence.
Reconcile results, rehearse deployment and agree the feasible recovery path before extending the rollout.
A useful modernization plan starts with behavior and dependencies, not a rewrite decision. Identify the highest-value change that can be verified and introduced with a manageable impact.
COMMON STARTING POINTS
Map runtimes, libraries, data flows, scheduled jobs and integrations. Identify business-critical behavior, constraints and the areas that need closer investigation.
Plan compatible upgrade paths for established .NET, Java or PHP applications. Review breaking changes and validate representative workflows before rollout.
Introduce stable interfaces around selected capabilities. Separate changes where the dependencies allow, while keeping existing consumers and business rules in view.
Review schemas, queries and data ownership. Plan transformation, reconciliation and coexistence where old and new components must run together.
Improve navigation and user journeys without assuming that every underlying capability needs replacement. Connect new interfaces through agreed contracts.
Establish repeatable builds, environment configuration, regression checks and monitored releases. Define rollback or recovery options for the proposed change.
Illustrative scenario: an established application holds important approval rules but is difficult to release. The first change isolates one capability while preserving its existing integrations.
Document approval rules, permissions, integrations and data dependencies. Record representative scenarios and a performance baseline.
Choose the interface and data boundary. Agree compatibility requirements, coexistence and the conditions for moving users to the updated capability.
Implement the change and run regression checks against the recorded scenarios. Reconcile data and investigate any behavior differences.
Rehearse deployment and the feasible recovery path. Monitor the agreed health signals before extending the migration to further capabilities.
The assessment connects technical changes to business priorities. Each release has a defined boundary, acceptance evidence and an agreed way to respond if something goes wrong.
A dependency map, critical scenarios, current delivery constraints and the risks that influence the plan.
Candidate changes, sequencing, dependencies and the rationale for the proposed first migration slice.
The scoped implementation with regression, compatibility and data-check evidence relevant to the release.
Pipeline and configuration guidance, health checks, known limitations and documented recovery responsibilities.
Clarify the scope, responsibilities and decisions behind a useful engagement.
Not automatically. We assess whether a runtime upgrade, interface improvement, integration boundary or targeted replacement addresses the problem. A broader rewrite is considered against business value, dependency risk and the cost of preserving existing behavior.
Where the architecture allows, staged coexistence can support a gradual transition. The plan must define data ownership, integration contracts and reconciliation so the two parts do not create conflicting records or behavior.
Use representative regression scenarios, test the release process and agree rollout windows with the business. Recovery needs are assessed for each change; a data migration may require a different approach from simply reverting application code.
A focused AI feature can be considered once its data access, integration points and review requirements are understood. It should have its own evaluation and acceptance criteria alongside the wider application changes.
The first assessment reviews the stack, dependencies, source access, critical workflows and release constraints. Findings shape a bounded first milestone; unresolved dependencies and assumptions are made explicit before implementation is agreed.