Legacy data warehouses face rigid walls of maintenance costs and cannot support generative AI or machine learning. Basic “lift-and-shift” migrations fail by reproducing performance bottlenecks and pipeline fragility on the cloud. Critical KPI logic is often buried in undocumented stored procedures. Legacy SQL patterns (RBAR) cause failures on Spark engines, while lack of step-by-step validation risks data integrity post-cutover.
Saama uses AI-powered code translation and automated equivalence proofs to reset monolithic architectures into verified Medallion structures. This approach eliminates technical debt and ensures AI-ready infrastructure.
Re-baselining fragmented data into a governed, queryable foundation for enterprise intelligence.
Refactoring monolithic legacy logic into distributed code blocks optimized for cloud speeds.
Compress decommissioning timelines to avoid parallel licensing costs.
Lift-and-shift is a strategy for moving problems. We treat migration as an architectural reset – where AI-powered refactoring compresses timelines, mathematical reconciliation preserves trust, and cutover becomes a confidence event, not a survival event.