Approach - A disciplined path from code archaeology to investment decision.
The assessment starts with the decision leadership needs to make, then works backward into the evidence required to make it well.
Secure intake
A bounded, decision-led scope.
- Align on the executive decision and success criteria
- Inventory source repositories, jobs, data, interfaces, and documentation
- Agree access, confidentiality, retention, and validation protocols
- Identify business and technical subject-matter experts
Analyze the estate
An evidence-linked model of how the system works.
- Use AWS Transform-supported workflows where they fit the estate
- Extract business rules, dependencies, process flows, and code relationships
- Identify functional domains and likely decomposition boundaries
- Flag ambiguity, risk concentrations, and missing evidence
Validate and plan
A defensible modernization path and roadmap.
- Reconcile machine-produced findings with business and technical experts
- Evaluate disposition options by domain rather than by slogan
- Model prerequisites, delivery waves, dependencies, and decision gates
- Make assumptions and confidence levels visible
Select and hand off
A partner decision grounded in the actual work.
- Translate the assessment into provider-selection criteria
- Compare qualified partners through a weighted scoring model
- Prepare a vendor-ready technical and business brief
- Transfer findings, evidence, risks, and open decisions to delivery
Principles - Fast enough to create momentum. Careful enough to earn trust.
Automation accelerates the investigation; judgment determines what becomes a recommendation.
- Evidence before opinion. Recommendations link back to code, process, data, documentation, interviews, or an explicit assumption.
- People validate machines. Extracted rules and inferred relationships are reviewed with the people who understand business behavior and operations.
- Independence is structural. The analysis does not depend on selling the implementation path or provider that ultimately gets selected.
Use of AWS Transform - Tool-assisted analysis, applied within verified boundaries.
AWS Transform can support mainframe analysis, dependency discovery, business-logic extraction, documentation, decomposition, wave planning, and transformation workflows. Applicability depends on the platforms, languages, and artifacts in scope.
Accelerate
Use machine-assisted analysis to traverse more code and documentation than manual discovery alone.
Constrain
Define where the tooling is applicable and make unsupported or low-confidence areas visible.
Validate
Treat generated findings as evidence to review—not an automatic source of final truth.
Start with clarity
Know what you have before choosing who will rebuild it.
Begin with a focused conversation about your application estate, constraints, and the decision your leadership team needs to make.