Data Foundation & Engineering
Audit, clean, and unify your data estate: warehouses, lakes, and real-time streams that every AI initiative can actually depend on. This is the layer everything else in your roadmap stands on.
The layer every AI initiative quietly depends on
Most AI projects that stall do not fail on the model. They fail on the data underneath it: warehouses that do not agree with each other, dashboards built on three different definitions of "revenue," and pipelines nobody has looked at since the analyst who built them left. We have spent 25+ years building the pipelines, dashboards, and frameworks that drive real business decisions, and that BI heritage is exactly what a durable data foundation requires.
This engagement audits your systems of record, reporting estate, and the institutional knowledge scattered across spreadsheets and inboxes, then designs and builds the governed, accessible layer, warehouse, lake, or real-time stream, that your BI and your AI initiatives can both stand on. We work with the stack you already run rather than proposing a rebuild, and we are explicit about what must change versus what can stay.
The result is not a diagram. It is a working foundation: pipelines that run, a semantic layer people and models can both query, and monitoring that tells you when something drifts before your dashboards do.
Four phases, built on what you have
Audit
Inventory systems of record, data quality, freshness, and access patterns across your entire estate.
Architect
Design the warehouse, lake, or streaming layer sized to your real workloads, not a vendor template.
Build
Stand up pipelines, models, and a semantic layer your BI and AI tools can both query and trust.
Govern
Put access control, lineage, and quality monitoring in place so the foundation holds as it scales.
Deliverables you can build on
Data estate audit
An honest inventory of your systems of record, quality gaps, freshness, and access patterns, with the specific fixes prioritized by impact.
Target architecture
A warehouse, lake, or streaming design sized to your real workloads and budget, not a generic reference architecture.
Governed pipelines
Working pipelines from source to warehouse, with lineage and access controls built in from day one, not bolted on after.
Semantic layer
Shared definitions and metrics that BI dashboards and AI agents both query, so "revenue" means the same thing everywhere.
Quality monitoring
Automated checks that flag drift, staleness, or broken pipelines before they show up as a wrong number on someone's dashboard.
Documentation & handoff
A foundation your own team can operate and extend, documented well enough that it does not depend on us to run it.
Built on what you have, not a rebuild
Scoped and fixed-fee
The audit and architecture phases are scope-bounded and fixed-fee, so you know the cost before we begin.
Built on what exists
We extend and repair your current stack wherever it is sound. Rip-and-replace is a last resort, not a default.
Documented and handed off
Every pipeline and model ships with documentation your own engineers can maintain without calling us.
Vendor-agnostic
Snowflake, Databricks, Fabric, Redshift, BigQuery, or something else entirely: we architect for your stack, not ours.
Questions we hear early
Do we need to migrate everything at once?
No. Most engagements phase the migration around the highest-value data first, so you see value before the estate is fully unified.
We already have a warehouse. Is this still useful?
Usually, yes. Most audits find real gains in the semantic layer, pipeline reliability, and governance around an existing warehouse, not a wholesale replacement of it.
Do you support our specific platform?
We are vendor-agnostic across Snowflake, Databricks, Microsoft Fabric, Redshift, BigQuery, and most common warehouse and lake platforms.
Ready to build the foundation?
Tell us about your data estate and where it is holding your team back. We will scope the audit and tell you honestly what needs to happen first.
Talk to us