Multi-source ingestion, reconciliation and a canonical model — the foundation everything else is built on. Real-time ETL/ELT into a lakehouse on Azure Fabric, Databricks or Snowflake, with lineage, quality gates and access controls designed in from day one.
ERP, CRM, HRMS and spreadsheets that never reconcile — every number has three versions.
Reporting waits days on hand-stitched extracts, and errors slip through unnoticed.
Without a governed source of truth, analytics and AI inherit the mess — garbage in, garbage out.
From raw sources to a catalogued, governed single source of truth.
Incremental loads, change-data-capture and streaming ingestion — data lands current, not batch-stale.
Delta / medallion lakehouse on Azure Fabric, Databricks or Snowflake — one platform for BI, ML and AI.
Conformed dimensions, master data management, business-friendly semantic layers — one definition per metric.
Checksum validation, schema enforcement and automated quality checks — trust the numbers by construction.
Catalog, lineage, row/column-level security and audit trails via Purview / Lake Formation — provenance on every field.
SSIS engineering and cloud migration paths — GCP-to-Azure, on-prem-to-lakehouse, without breaking downstream reporting.
A centralized data lake and governed Power BI platform on Azure Fabric — financials, CRM, timesheets and resourcing in one source of truth.
Read the case study →A data mesh and self-serve analytics platform — multi-property ingestion with unified revenue and pricing dashboards.
Read the case study →A 3-phase Azure data platform unifying fragmented practice-management systems with governed Power BI and row-level security.
Read the case study →A SQL Server medallion architecture and Power BI semantic model delivering real-time revenue, TAT and inventory insights across labs.
Read the case study →A cloud migration and FP&A dashboard suite unifying care, billing and insurance data with EBITDA, LTM and CFS bridge analysis.
Read the case study →Fragmented highway data unified into a GIS monitoring platform with automated SLA compliance and decision intelligence.
Read the case study →Data availability
Manual data prep
Faster insight
Lineage & audit trail
Lakehouses on Azure Fabric, Databricks or Snowflake using a Delta or medallion architecture, plus SQL Server and provider-agnostic AWS stacks (Glue, EMR, Iceberg, Redshift).
Checksum validation, schema enforcement and quality gates, master data management and canonical models, plus catalog, lineage and row/column-level security via Purview or Lake Formation.
Yes — SSIS engineering and cloud migration paths such as GCP-to-Azure or on-prem-to-lakehouse, without breaking downstream reporting.
A governed single source of truth is the foundation analytics and AI build on — without trusted data there is no trustworthy AI.
Tell us where your data lives — we'll bring the engineers who've built governed lakehouses across pharma, healthcare, hospitality and professional services.