BI platform migration to Power BI
Rebuild your semantic models in Power BI faster
— with proof they work.
SemanticOps uses AI to accelerate semantic model creation during Power BI migration, then validates the output with regression tests, RLS checking, documentation exports, and rollback — so teams deliver migrations with evidence, not hope.
The problem
Migrating to Power BI means rebuilding, not copying
Every BI platform stores logic differently. Moving to Power BI requires recreating calculations, relationships, and security rules from scratch — without an automated way to validate the output matches the source.
Tableau
Workbook logic and calculated fields do not map to DAX. Measures need to be rebuilt from scratch using Power BI semantic model conventions.
SAP BusinessObjects / Crystal Reports
Universe-based query definitions and report-level calculations require semantic model equivalents. Business logic is often buried in report layers rather than a shared model.
Qlik
Set Analysis and associative logic have no direct Power BI equivalent. Measures, filters, and associations need to be re-expressed as DAX with explicit relationships.
Excel / Power Query
Pivot logic, named ranges, and nested Power Query transforms need to be formalized into a reusable, governed semantic model with proper relationships and calculated measures.
All platforms: Existing reports still need to validate against the new Power BI model before go-live. Without regression tests, there is no systematic way to confirm behavior was preserved.
The workflow
How AI accelerates the Power BI rebuild
SemanticOps gives AI agents governed access to generate semantic model structure from source systems — while keeping humans in control of what gets applied and providing evidence of correctness at every step.
Agent reviews source model / report structure
AI agent inspects the source report, workbook, or model to extract table definitions, calculation logic, filters, and relationships.
Agent proposes Power BI semantic model equivalent
Agent generates a target semantic model with tables, measures, relationships, and role definitions that represent the migrated logic.
Policy engine governs what is auto-created vs. reviewed
Governance rules define which operations are applied automatically and which require human sign-off — no unbounded agent writes.
Regression tests validate output matches expected values
Expected-value tests are built alongside migration to confirm the new model produces results that match the source system.
Documentation export provides migration artifact
Model structure — tables, measures, relationships, annotations — is exported to human-readable documentation for client handover.
Rollback available at each migration phase
Snapshots are taken before each phase. If a step produces unexpected results, the previous state is recoverable.
For consultants and system integrators
Migrations are time-pressured. AI plus governance means faster delivery with better evidence.
Power BI migration projects run on tight deadlines and client trust. SemanticOps lets you deliver faster — with AI accelerating semantic model creation — while producing the test evidence, documentation, and audit trail that turns a delivered model into a verifiable deliverable.
Regression tests validate migrated measure logic
Documentation export is a ready-made client artifact
Audit log proves creation activity and policy decisions
RLS / OLS validation covers security role correctness
Rollback protects each migration phase
AI-assisted rebuild reduces manual measure reconstruction time
Plan your Power BI migration with AI and governance.
SemanticOps accelerates semantic model creation during migration while validating correctness with regression tests, documentation, and rollback at every stage.