Ingestion and modeling into a governed warehouse or lakehouse on Symbiosis Data Platform, quality monitoring that catches breakage before dashboards lie, catalog/lineage/ownership, optional CDP identity resolution, and the semantic feed so AI and analytics share one version of the truth.
Agents and analytics inherit whatever the pipelines feed them - every AI ambition is a data-quality bet. Data engineering is the foundation of the AI work.
Dashboards nobody trusts train the org to ignore evidence. Trust is engineered - lineage, tests, freshness contracts - then earned back one reliable report at a time.
Sprint, then phase, then retainer
Assessment first, always. Scope and investment are bespoke to your estate - book a demo and we'll walk the plan together.
DATA SPRINT
Estate review, platform blueprint, first pipeline slice planned.
- → Estate map
- → Platform blueprint
- → Prioritized backlog
PLATFORM PHASE
Platform live, critical pipelines migrated, quality gates on.
- → Live platform
- → Critical pipelines
- → Quality monitoring
Data Engineering & Analytics, answered
Warehouse or lakehouse?
Decided by your workloads and team, not by fashion. Heavy BI with SQL-fluent analysts points one way; large semi-structured volumes and ML pipelines point another; plenty of companies rightly run a hybrid. The sprint settles it with evidence.
Our reports disagree with each other. Where do we start?
With the metric layer, not more dashboards. Conflicting reports almost always mean conflicting definitions - we fix the definitions in code, assign owners, and rebuild the top reports on the shared layer first.
Start with the sprint.
Fixed fee, few weeks, and you end up with a plan you could execute without us. Most clients don't - but the leverage is yours either way.