Data Engineering & Analytics
The foundation every AI initiative needs. AI initiatives fail on the data beneath them, not the model on top.
- Layers
- 05
- Engagement models
- 03
Fix the foundation first
Before a model can earn trust, the data underneath it has to. The hard part of AI is rarely the model: it is data that is fragmented, undocumented, and ungoverned. We build the warehouse, the pipelines, the transformations, the governance, and the analytics layer as one coherent stack, so every downstream AI effort stands on data that is modeled, tested, and trusted.
The data foundation, layer by layer
Five layers from the warehouse at the base up to self-service analytics at the surface. Each layer builds on the one below it, and each is a place an engagement can start, whether you need the whole stack or one missing piece.
Layer 01 / Storage
Warehouse / Lakehouse
The storage layer every other layer sits on: one governed home for your data.
- Cloud warehouse or lakehouse selection and setup
- Schema, partitioning, and storage design for analytics and ML
- Cost and performance tuning: clustering, materialization, compute sizing
- Workspace, role, and environment structure (dev, staging, prod)
Tooling
A layered data foundation
Each layer builds on the one below it, from the warehouse up to the analytics surface. Hover or select any layer to see what it carries and how trust accrues as data moves up the stack, so AI initiatives draw from data that is modeled, tested, governed, and trusted.
Foundation layer
Warehouse / Lakehouse
The storage foundation every other layer sits on: one governed home for analytics and ML workloads.
What runs here
Three ways to engage
10-14 wk
Full Build
End-to-end foundation, from warehouse to BI, delivered as one engagement. For teams starting fresh or rebuilding a stack that has stopped scaling.
3-8 wk per module
Modular Build
A specific layer or two, when you already have part of the stack in place. For teams that need pipelines, transformation, governance, or BI fixed without redoing everything.
3+ mo retainer
Team Augmentation
Embedded ASC engineers working inside your data team on a retainer. For teams that have the roadmap but need senior hands to execute and level up the squad.
The data architecture we build
A three-layer warehouse with ingestion, transformation, and governance at every step, then an AI agents and BI layer on top that pulls insights and executes tasks. Source systems feed in at the top; trust accrues as data moves down through ingestion, modeling, and governance onto the warehouse foundation everything rests on. Select any layer to see the real tools and components that run there.
Sources feeding the stack
Layer 05 of 05 / Foundation
Warehouse / Lakehouse
The storage foundation every other layer sits on: one governed home for analytics and ML workloads.
What runs here
Works with the other service lines
The ground AI stands on
A warehouse, pipelines, transformations, governance, and an analytics surface, scoped to how much you already have. We lead with the foundation, not the model, because the foundation is where AI initiatives fail.
5
Capability groups, from warehouse to BI
10-14 wk
Full build, end to end
3-8 wk
Per module, modular build
3
Engagement models (full, modular, augmentation)
Start with Data Engineering & Analytics
Tell us where you are, and we will scope the first engagement around the outcome that moves your business.