Pipelines, warehouses, semantic models, and dashboards built for the cadence executives actually run on. Modern stack. Boring delivery. Real numbers.
Most analytics programs collapse under the weight of dashboards no one trusts. We build the boring layer right, sources of truth, semantic models, lineage, governance, then layer storytelling on top.
Result: a smaller, sharper portfolio of dashboards your operators actually open on Monday. AI-augmented analytics where it earns its keep, not as a marketing badge.
Three checkpoints, one operating cadence. We don't disappear into a pipeline build for six months and emerge with a Tableau workbook.
Decisions inventory, source survey, KPI tree, target architecture. We start from the question, not the data lake.
Warehouse, pipelines, dbt models, semantic layer. Tested, documented, lineage-tracked, alerting on freshness and quality.
A small portfolio of dashboards owned by named operators. Embedded analytics where end-users live. Narrative AI on top, optional.
Quality monitoring, governance reviews, ongoing data engineering throughput. Optional embedded analytics engineers.
Aggregated across the last 18 months of analytics engagements. Yours will be different, the operating discipline isn't.
If your question isn't here, the diagnostic will surface it.
Bring the problem. We'll come back with a written brief: what to build, what to defer, and where AI actually moves the number. No deck pitches.