Solutions
A model only matters when it changes a real decision. BigAI selects use cases by business value, measures them against KPIs agreed up front, and hands over models running in production.
Signals
The model works on a data scientist laptop and nobody knows how to get it into the real system.
An if-else rulebook written years ago that nobody dares to change.
92% accuracy reported, but nobody can say how much money that saves.
Capabilities
The most common mistake is choosing an algorithm and then hunting for a problem. BigAI works the other way round: list the decisions your business repeats most often, estimate the value of making each one 10% better, and only then ask whether the data supports a model.
That approach usually eliminates 70% of the initial ideas — and keeps the ones that can pay for themselves within a year.
Forecasts by SKU, store, region and time window, accounting for seasonality and promotions.
Real-time scoring combined with business rules, with every decision explainable.
Identify high-value customers and those about to leave, early enough to act.
SHAP analysis, bias testing and model documentation good enough for a risk committee.
Outcomes
Ranges aggregated across delivered BigAI projects. Specific targets are agreed during the assessment phase.
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| Metric | Before | After | Improvement |
|---|---|---|---|
| Demand forecast error (MAPE) | 25–40% | 8–15% | −60% |
| Anomalous transaction detection | Batch, end of day | Under 300 ms | Real time |
Case studies
From 14 disconnected sources and manual reconciliation to a single governed lakehouse serving both regulatory reporting and internal analytics.
Read case studyReplacing store-manager intuition with per-SKU forecasting that accounts for seasonality, weather and the promotion calendar.
Read case studyFAQ
A BigAI solution engineer will review your current data estate with you, identify the highest-value problem to solve and sketch a realistic roadmap. No commitment.