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What we build
Data platforms with warehouse and lakehouse architecture on Databricks and PostgreSQL — modelled, documented and governed, so analysts query one source of truth instead of five almost-truths. Batch pipelines for the heavy lifting and real-time event streaming where minutes matter: telemetry, transactions, operational alerts.
On top: analytics people actually use. Power BI dashboards wired to governed models, self-serve datasets with definitions attached, and metrics that mean the same thing in every meeting.
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Built AI-ready
The same foundations that make analytics trustworthy make AI possible: clean lineage, quality checks at ingestion, and access controls that let you point a RAG pipeline or an agent at your data without a compliance incident. We build data platforms assuming machine intelligence will consume them — because within a year, it will.
Data quality is enforced, not hoped for: schema contracts, anomaly detection on volumes and distributions, and alerting that catches a broken feed before your CFO does.
04 / Decrypted questions
Asked before every mission.
Do we need a data warehouse or a lakehouse?
A warehouse if your data is mostly structured and BI-shaped; a lakehouse when raw, semi-structured or ML-bound data matters too. In practice most mid-size platforms land on a lakehouse pattern with warehouse-style governed marts on top.
When is real-time streaming worth the complexity?
When a decision loses value in minutes — fraud, outage response, live operations. If the business acts on daily rhythms, well-built batch is cheaper and just as effective. We size the latency to the decision, not the fashion.
How long does a data platform take to build?
A governed first platform — ingestion from core systems, modelled warehouse, first dashboards — typically ships in 8–12 weeks, then grows source by source. The first reconciled dashboard usually lands within a month.
How do you handle data governance without slowing everyone down?
Governance as defaults, not gates: access roles defined once, quality checks in the pipeline, lineage captured automatically, definitions published beside the data. Done this way it speeds teams up — nobody re-litigates numbers.