The data stack behind every account we run.
Cloud warehouse, ELT pipelines, version-controlled transformations, and the AI-agent analysis layer — the in-house data practice that powers attribution, measurement, and BI for every client engagement.
From raw data to insights.
Data infrastructure as competitive advantage.
Our modern data stack is not an internal productivity tool — it is the foundation every client account runs on. Cloud warehouse architecture, ELT pipelines that ingest from every ad platform and commerce system, version-controlled transformations with automated testing, and the AI-agent analysis layer that accelerates the work an analyst would otherwise do manually. The stack is built once, maintained continuously, and deployed against every new engagement without starting from scratch each time.
What clients consume through this stack: custom attribution models, marketing mix models, predictive LTV and churn scores, automated anomaly detection, role-specific dashboards, and the reverse-ETL layer that pushes insights back into operational systems. Everything that the Business Intelligence hub delivers is powered by this same underlying infrastructure — which is why a Labs engagement and a BI engagement often end up scoped together.
For clients operating their own internal data team, the Labs data stack integrates cleanly — shared semantic layer, shared metric definitions, shared identity resolution. The internal team is not replaced; it is extended with the engineering depth most mid-size in-house teams cannot sustain alone.
What makes the difference.
Deployable Warehouse Architecture
Cloud warehouse patterns we have deployed many times — partitioning, cost governance, access control, and the operational discipline that keeps costs linear as the warehouse scales.
ELT Library
Ingestion connectors and extraction patterns for every ad platform, commerce platform, and operational system we commonly work with. New client onboarding benefits from a library, not a fresh build each time.
Transformation Templates
Version-controlled transformation models for common marketing analytics patterns — attribution, cohort analysis, LTV, retention curves, conversion funnels. Deploy once, customise per client business logic.
AI-Agent Analysis Layer
Internal AI agents we built for account analysis, anomaly investigation, and the repetitive analyst work that does not scale with a human team. Deployed against client data through our infrastructure.
Reverse ETL Patterns
Integrations with the ad platforms, CRMs, and operational systems clients depend on. The activation layer that turns warehouse outputs into execution inputs across the client stack.
Integration With Internal Teams
Where clients have their own data team, the Labs stack integrates without replacing. Shared semantic layer, shared definitions, shared identity resolution. Extension, not displacement.
Deploying the stack.
Assess
Client's existing data state — sources, warehouse, team, tooling, maturity. The starting point determines whether we deploy the full stack or integrate with existing infrastructure.
Deploy
Stack components deployed from our library — warehouse configuration, ELT pipelines, transformation models, access control, and the AI-agent layer.
Customise
Transformation logic, metrics, and business rules adapted to the specific client context. Library patterns are the starting point; the specifics of the business define the end state.
Operate
Continuous operation with monitoring, cost governance, and ongoing development. The stack evolves with the client account as new requirements emerge.
Common questions.
Ready to run on the stack?
Let's talk about deploying the data stack against your account — warehouse, pipelines, transformations, and the AI-agent layer on top.
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