Data Engineering that AI can actually use.
AI is only as good as your data. We build cloud-native data platforms on Databricks, Snowflake, BigQuery, and Microsoft Fabric — with governance, real-time pipelines, lineage, and AI-readiness from day one. Lakehouse-native, vendor-agnostic, audit-ready.
Most data warehouses can't feed AI. Ours do, from day one.
Legacy ETL + warehouse architectures weren't designed for AI workloads. We build lakehouses that serve both BI and AI — with real-time pipelines, embedding stores, and governance baked in.
Databricks · Snowflake · Fabric
One platform for BI, ML, AI. Delta Lake / Iceberg / Snowflake hybrid. AI-workload-ready from day one.
Lineage · catalog · access
Unity Catalog, Snowflake Horizon, custom governance frameworks. Lineage tracked, access controlled, audit-logged.
Kafka · Kinesis · Pub/Sub
Streaming pipelines for fraud detection, agentic AI feedback loops, real-time analytics. Sub-second latency targets.
Embeddings inside
Vector DBs (Pinecone, Qdrant, Postgres + pgvector) integrated with the warehouse. Embeddings refresh on data updates. RAG-ready.
From pipelines to platforms. End to end.
Databricks · Snowflake
Lakehouse architecture from scratch. Bronze / silver / gold layering. Delta Lake / Iceberg. Workload-tier optimization.
Batch + streaming
Airflow, dbt, Kafka, Spark Streaming. Idempotent, observable, retry-safe. Per-pipeline SLA monitoring.
Legacy → cloud lakehouse
Strangler-pattern migrations from on-prem warehouses to cloud lakehouse. Zero-downtime cutover.
Catalog · lineage · access
Unity Catalog, Snowflake Horizon, custom governance frameworks. Column-level lineage. Access controls.
Streaming + CDC
Change-data-capture from source systems. Streaming aggregation. Real-time dashboards and alerts.
Embeddings · feature stores
Embedding generation, vector indexing, feature stores. RAG-ready corpus management.
Proof. Not pitch decks.
Lakehouse for regulated FinTech.
Migration from on-prem warehouse to cloud lakehouse for a regulated GCC FinTech. Real-time streaming for fraud detection. Embedding store for agentic AI. Full lineage and audit trail for regulator reviews. 99.99% pipeline SLA.
ENGINEERINGDatabricks default
Default platform for new lakehouse builds. Unity Catalog for governance. Delta Lake for storage. Workload-tier compute.
AI-READYVector + relational
Embeddings co-located with relational data. Refresh-on-update. Used in production for our agentic AI deployments.
Questions buyers actually ask.
Databricks or Snowflake?
Databricks for AI/ML-heavy workloads (notebook UX, Spark depth, MLflow integration). Snowflake for BI-first, multi-region simplicity, governance. Microsoft Fabric for Microsoft-shop integration. We're neutral and recommend per use case.
Can you migrate from legacy data warehouse?
Yes — Teradata, Oracle Exadata, on-prem SQL Server, legacy Hadoop. Strangler-pattern migration: parallel run, gradual cutover, retire when stable. Typical timeline: 9-18 months for large estates.
How do you handle data quality?
Great Expectations, dbt tests, Soda, or custom validation framework. Tests run as part of every pipeline. Data quality SLAs per dataset. Anomaly detection on production traffic.
Can the data layer feed AI workloads?
Yes — that's the point. Embedding generation, vector indexing, feature stores, real-time event streams to agentic AI. The lakehouse serves both BI and AI from the same foundation.
Audit your data maturity.
Free data maturity assessment. We benchmark your current architecture, governance, and AI-readiness, and return a prioritized improvement list.