Real decisions. Real data. Real outcomes.

The challenge

A mid-size e-commerce company was running 12 concurrent marketing campaigns with no reliable way to attribute revenue to individual channels. Last-click models were systematically over-crediting paid search and under-crediting email and organic — leading to budget decisions based on misleading signal.

Our approach

We built an impact analysis template using a sample of their campaign data in the browser workspace. The template defined the causal pathways from ad exposure to purchase, controlling for seasonality and cross-channel overlap. The team downloaded the offline application and ran the same template against their full 18-month transaction dataset — identifying true causal contribution for each channel.

The challenge

A payments company's ML fraud model was flagging 3× more legitimate transactions than actual fraud, creating significant customer friction and manual review overhead. The team suspected the model was picking up correlated-but-non-causal features, but had no way to isolate them without sending sensitive transaction data to an external service.

Our approach

We built a root cause analysis template using anonymised sample data — no real customer records left the company's environment at any stage. The offline application ran the same template on their full transaction history entirely within their secure infrastructure, separating genuine risk signals from spurious correlations in the training data.

The challenge

A regional logistics operator was experiencing a 12% unplanned delay rate across their distribution network. Weekly operations reviews identified different root causes each time — weather, staffing, vehicle availability — but no consistent causal structure that could be acted on systematically.

Our approach

We built a root cause analysis template using three months of sample operational data. The template mapped the causal relationships between input variables (staffing levels, vehicle utilisation, route density, weather) and delay outcomes. The offline application ran the same template on each distribution centre's full historical data, producing centre-specific causal rankings.

The challenge

A clinical research organisation was spending 6–8 weeks per trial cycle manually investigating protocol deviations and adverse event clusters. Patient data could not leave their secure environment under any circumstances, making cloud-based analytics tools unusable.

Our approach

We built an impact analysis template using synthetic patient data that matched the structure of their real trial data. The template defined the causal pathways between protocol variables and deviation outcomes. The offline application was deployed within their air-gapped research environment — no network connection required — and ran the same template on each trial's full patient dataset.