The data stack behind every account.
A cloud warehouse, data pipelines, version-controlled transformations and AI-agent analysis: our own data practice behind every engagement.
From raw data to insights.
Data as an advantage.
Our data stack is not a tool on the side. It is the base every client account runs on: a cloud warehouse, pipelines that bring in data from every ad platform and sales system, tested transformations and AI agents for the work an analyst would otherwise do by hand.
Through it you get attribution models, marketing mix models, LTV and churn forecasts, automatic anomaly detection, dashboards by role and a way to send findings back into operational systems. Everything Business Intelligence delivers runs on the same infrastructure, so the two often sit in one project.
If you already have your own data team, the stack fits in without replacing it, with shared metric definitions and shared user identification. Your team gains technical depth that few in-house teams can maintain alone.
What changes for you.
Ready warehouse
Cloud warehouse set-ups we have applied many times: data separation, cost control and access rights, so cost stays steady as data volume grows.
Connector library
Ready connections for every ad platform and sales system we use often. A new client starts from a library, not from zero.
Ready models
Tested models for the usual marketing questions: attribution, cohorts, LTV, retention and funnels. Set up once and adapted to the client's logic.
AI-agent analysis
AI agents we built for account analysis and anomaly investigation, the work that doesn't scale with people. They run on your data through our own infrastructure.
Back into your systems
Connections to ad platforms, CRMs and operational systems. Warehouse results become inputs for action across your stack.
Works with your team
If you have your own data team, the stack fits in without replacing it: shared definitions and shared identification. An extension, not a replacement.
How it is set up.
Assessment
The current state of your data: sources, warehouse, team, tools. That decides whether we set up the whole stack or fit into what exists.
Installation
We set up the parts from our library: warehouse, pipelines, models, access rights and the AI-agent layer.
Adaptation
The transformation logic, metrics and business rules are adapted to your context. The ready models are the starting point, not the end result.
Operation
Continuous operation with monitoring, cost control and development. The stack grows with your account as new needs come up.
Common questions.
Set up the stack.
Let's talk about the data stack in your account: warehouse, pipelines, models and AI agents on top.
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