Migration for AI readiness
Migrate once and prepare a foundation for future AI decisions
Combine technical migration with data cleaning, documentation and validation so the new system does not inherit old uncertainty.
Discuss your project →What must be preserved
Before migration, we decide which history belongs in the live system, what remains in an accessible archive and which records need not move. The map covers entities, relationships, reference data, attachments, integrations and access rules.
- Data and dependency inventory
- Archiving and retention rules
- Field mapping and transformations
- Control reconciliations before cutover
Preparation for analytics and AI
Migration can establish stable identifiers, resolve evident duplicates, align critical reference data and document field provenance. This does not guarantee readiness for every AI use case, but creates a verifiable foundation.
Acceptance without assumptions
Completeness is demonstrated through quantitative reconciliation, control records and critical-process validation. Cutover follows approval by accountable client representatives, with a rollback path agreed before the critical phase.
Fit before scope
Is this the right starting point?
Use these criteria for an initial orientation. The final recommendation follows a review of your context, data and constraints.
A good fit when
- Software is restricted, unsupported or creates operational risk
- Data, history and integrations must be preserved
- Process and acceptance owners are available
Resolve this first when
- Only licence procurement is required without dependency analysis
- A target was selected without validating processes and data
- No one owns reconciliation and the cutover decision
A verifiable first stage
What you can use to make the next decision
The exact scope is agreed before work begins. These are typical decision artefacts, not promised business results.
Dependency inventory
Versions, users, processes, data, integrations and critical support dates.
Replacement-wave plan
Target options, sequence, parallel operation, training and rollback path.
Acceptance rules
Reconciliations, control operations and evidence your team will use to approve the result.
Five questions · no datasets or credentials
Frequently asked questions
Frequently asked questions
Must everything be cleaned before migration?
No. We prioritise issues affecting critical processes, reconciliation and intended future uses. Some cleaning can be delivered in stages.
Can the old system remain as an archive?
Yes, when access, support, security and retention are defined. The archive is designed separately from the live system.
Does migration automatically make data AI-ready?
No. It creates a stronger foundation, while readiness must be evaluated for a specific AI use case, users and quality criteria.
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