Without a unified foundation, models never reach production.
We consolidate fragmented systems into a lakehouse and modern warehouse, from legacy migrations to real-time pipelines, with quality, security and observability built in.
Azure
Where this sits in the three stagesBuilt inside Stage 2, Build; scoped in Assess.
We modernize without rip-and-replace, plugging into the teams and tools you already run and migrating on defensible timelines.
A Delta Lake and Unity Catalog design that handles batch and streaming in one place, with ACID reliability and elastic scale.
Reliable ingestion from every source system, with contracts and tests so data lands clean and on time.
Monitoring, alerting and freshness checks that catch issues before they reach a dashboard or a model.
Move off aging warehouses and on-prem stacks without stalling the roadmap.
InsuranceProducing the weekly risk report
We build on the lakehouse and cloud your risk and security teams already approve.
DatabricksCentralized metadata, fine-grained access and full lineage, to stay compliant across your data and AI estate.
Centralized access, lineage and policy across the estate.
Enforce schemas so producers and consumers stay in sync.
Missing lineage and access control turn compliance into a fire drill; we build both in from the first pipeline.
We consolidate fragmented systems into a lakehouse and modern warehouse, from legacy migrations to real-time pipelines, with quality, security and observability built in. The work covers four things: lakehouse and warehouse architecture, streaming and batch pipelines, quality and observability, and legacy migration and modernization.
Yes. We build on Claude and Databricks, and on the lakehouse and cloud your risk and security teams already approve. The architecture is a Delta Lake and Unity Catalog design that handles batch and streaming in one place, with ACID reliability and elastic scale.
No. We modernize without rip-and-replace, plugging into the teams and tools you already run and migrating on defensible timelines. Legacy migration moves you off aging warehouses and on-prem stacks without stalling the roadmap.
With centralized metadata, fine-grained access and full lineage, to stay compliant across your data and AI estate. That means a Unity Catalog rollout for centralized access, lineage and policy; data contracts that enforce schemas so producers and consumers stay in sync; and lineage built in from the first pipeline, not added after.
Without a unified foundation, models never reach production. Level 1 includes the governed data platform every later level depends on, which is why the data platform is built in Stage 2, Build, and scoped in Stage 1, Assess. Quality and observability checks catch issues before they reach a dashboard or a model.
For Berkadia, a commercial real estate lender, we built an automated valuation pipeline across the lending book on Databricks on Azure, a Level 2 (L2) system that made underwriting 40% faster.
