Bridging the Discovery Deficit Gap

Novuz AI-driven discovery process analyzes 100% of system artifacts. By parsing every line of code using graph-based models it creates a high-fidelity digital twin of the data warehouse. This includes full object inventory, end-to-end lineage, automated complexity scoring, and redundant code detection. Migration shifts from guesswork to a clearly scoped, predictable engineering program.

Predictive Reengineering

Novuz proprietary encoder consists of context aware models that infer precise, deterministic transformations using a Bayesian network. Delivering at 90%+ accuracy, this structured approach avoids the probabilistic errors of general-purpose LLMs and ensures reliable conversion of mission-critical business logic. The result is faster production readiness, and lower operational risk.

Full System Replication

Novuz approach includes all-aspects of migration, from metadata to data, from ETL jobs to orchestration into the overall modernization scope. Rather than treating migration as a standalone lift-and-shift, it ensures full alignment with converted schemas, pipelines, and orchestration. The result is seamless data flow from day one, faster production readiness, and significantly lower operational risk.