Modern Data Platform.
Microsoft Fabric, Databricks, or hybrid lakehouse — designed and shipped to production-engineering standards. We don't pick a platform you don't already have. We make the one you have actually work.
— Built inside your tenancy. Your licenses. Your data residency.
Three things every enterprise platform misses.
Reference architecture, not improv
A documented bronze-silver-gold layout, naming conventions, ingestion patterns, and access tiers. Same shape across every domain — so handover doesn't depend on the original engineer being available.
- Medallion + zone discipline
- Naming & tagging conventions
- Reusable ingestion patterns
- IAM & access tier baseline
Production-engineering rigor
CI/CD for data pipelines, environment promotion, observability, alerting, runbooks. Treating the data platform like the production system it actually is.
- Git-backed pipelines
- Dev / UAT / Prod parity
- Lineage-aware monitoring
- On-call runbooks
FlexiContext at the seam
The semantic layer sits between platform and consumer. KPIs, hierarchies, exceptions live in the registry — not duplicated across BI tools, notebooks, and ML pipelines.
- Semantic model unification
- BI / notebook / ML sharing
- Versioned definitions
- Owner-per-entity
Two platforms. Operator-grade on each.
We refuse to pretend we know every cloud lakehouse equally. We commit to two — and stay current on each.
A platform your engineers can extend without us.
Reference architecture, documented
Diagrams, naming, IAM, lineage hooks. The shape every new domain follows.
Three production-grade domains
Three end-to-end pipelines built to standard — usually finance, operations, customer. The pattern others copy.
FlexiContext semantic layer
Implemented inside Fabric or Unity Catalog, populated for top KPIs, owned by your data team.
Engineering handbook
The team's playbook: how to add a domain, how to promote to prod, how to handle a failed run, how to onboard a new engineer.