Sathus AI 2.0 is now generally available — evaluation harnesses and guardrails included. Explore
Decommission costly legacy enterprise data warehouses. Transition to open, governed lakehouse architectures on Databricks and Snowflake with zero data loss.
Common obstacles that prevent enterprises from realizing the full value of AI initiatives.
Proprietary appliances (Teradata, Exadata) drain IT budgets with slow scaling capabilities.
Core capabilities that enable production-grade AI systems with enterprise governance.
Open table formats providing ACID transactions, time travel, and unified streaming/batch SQL.
Centralized data lineage, column-level masking, and automated data quality validation.
Bronze raw ingestion, Silver cleansed tables, Gold analytical marts, Apache Iceberg, Unity Catalog governance, and dbt transformation pipelines.
The tools and platforms we use to deliver production-grade AI systems.
Data platform modernization stages.
Automate translation of legacy PL/SQL scripts to PySpark and dbt SQL.
Replicate data pipelines in parallel, verifying 100% financial and metric alignment.
Execute zero-downtime cutover and decommission legacy hardware.
Measurable results that drive enterprise value from AI initiatives.
Lowered infrastructure and licensing costs following cloud lakehouse migration.
70%
Real-world examples of how we've delivered AI solutions for enterprise clients.
Insurance Corp • Financial Services • 7 Months
Legacy SQL Server warehouse failed to handle peak actuarial workload queries.
Databricks lakehouse migration using Apache Iceberg table formats.
70% cost reduction and 10x faster actuarial risk report generation.
Common questions about our AI Engineering practice and approach.
We run parallel dual-execution validation pipelines comparing row-by-row checksums before decommissioning any legacy source systems.
Let's discuss how our engineering practices can accelerate your business outcomes.
