Data platforms, semantic layers, and AI-augmented analytics built for how decisions actually get made. Vendor-neutral across Snowflake, Databricks, and Microsoft Fabric. The warehouse is chosen for your workload, not for the partner deck.
Most analytics programs collapse under dashboards nobody trusts. The warehouse is right but the semantic model is inconsistent. Finance and operations disagree on the same metric. The dashboard exists; the decision doesn't improve. We build the boring layer correctly — sources of truth, semantic models, lineage, governance — then surface analytics on top of data that earns trust.
Platform choice follows compliance posture and workload economics. Snowflake for structured enterprise workloads with mature governance requirements. Databricks for ML-heavy pipelines and unstructured data. Microsoft Fabric for organizations deep in the Microsoft stack. The right answer is usually written in your existing contracts, not in a comparison blog post.
Every data engagement runs the Aizen delivery spine. Platform decisions are documented at the Design stage with rationale. Dashboard scope is locked before build. Learn how Aizen works →
Decisions inventory, source survey, KPI tree, platform selection, and target architecture. Deliverable: written brief that starts from the question, not the data lake. Platform choice — Snowflake, Databricks, or Fabric — is an Aizen Event with documented rationale.
Data model, semantic layer design, pipeline architecture, governance policy, and dbt project structure. Every table, metric, and ownership assignment documented before build starts. Aizen Events run for vendor integrations, compliance constraints, and any scope addition.
Warehouse build, ELT pipelines, dbt models, semantic layer, and a locked dashboard portfolio owned by named operators. Tested, lineage-tracked, freshness-alerted. Embedded analytics where end-users live. AI-augmented analytics via Otonmi where the underlying data earns it.
Quality monitoring, governance reviews, ongoing data engineering throughput. Runbook and KPI baseline handed off. Optional embedded analytics engineers for programs scaling the platform team.
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.