BI that drives the account.
Most BI practices stop at the dashboard. Ours starts there and routes the output back into the bidding algorithm. Custom attribution, marketing mix models, and AI-powered analysis that change what the account spends tomorrow.
From insight to action.
A dashboard that nobody acts on is worse than no dashboard at all. Our Business Intelligence practice is engineered around one question: does this insight change what the ad account does next week? If not, we do not build it.
A modern cloud data stack is table stakes. What is not table stakes is the attribution layer, the marketing mix models, and the AI agents that read the data and flag the anomalies a human analyst would miss. That is where our BI practice lives.
Full-stack analytics
Data Engineering
Version-controlled data transformations, automated testing, and documentation generated from the code — the engineering practice that keeps reporting numbers consistent across tools and teams.
ML & AI
AI agents for data analysis, marketing mix models for budget decisions, predictive LTV and churn models — applied AI deployed where it produces better decisions than the alternative, not where it pads a proposal.
Explore ML & AIVisualizations
Role-specific dashboards, automated reporting, and BI tooling engineered to drive action — not to produce reassuring numbers for a weekly meeting that nobody uses afterwards.
Explore VisualizationsData Infrastructure
Cloud data warehouse architecture, schema design, and the foundation that decides whether your BI practice compounds or collapses under its own weight as the business scales.
Explore Data InfrastructureData Collection
Server-side tracking, first-party event pipelines, consent-aware instrumentation, and the data quality discipline that decides whether everything downstream works or silently misleads.
Explore Data CollectionData Activation
Customer segments pushed to ad platforms, predictive scores written into CRM, and warehouse-native audience logic activated in operational systems — the reverse ETL layer that turns BI output into execution input.
Explore Data ActivationData methodology.
Audit
Map every data source — ad platforms, GA4, CRM, fulfilment, finance. Identify duplicates, gaps, and the decisions blocked by each.
Architect
Design the warehouse schema, transformation layer, and reporting marts. Version-controlled transformations with documented lineage.
Ingest
ELT connectors for every source with backfill and incremental syncs. Server-side event pipelines for first-party marketing data.
Activate
Dashboards, alerts, and the models — custom attribution, MMM, AI-agent-driven anomaly detection — that change how the accounts spend next week.
Data-driven accounts.
E-commerce operators that run attribution models that were built from scratch. DTC brands whose weekly budget decisions come from MMM outputs. B2B services whose BI reports drive sales handoff, not just marketing reporting.
Ready to unlock?
Let's talk about the data layer that feeds every bidding decision downstream.
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