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08 / Data / AI
Predictive Intelligence Platform
Historical operational data could show what happened last month but offered little help with upcoming demand, workload pressure or abnormal behaviour. We built a forecasting layer that turns historical patterns into forward‑looking signals.
18.9%improvement in forecast error versus the previous baseline
72 hrsearlier identification of high‑risk operational periods
6connected data sources
What we built
A system designed around the actual work.
- Historical data pipelines
- Feature preparation
- Demand forecasting
- Anomaly detection
- Risk scoring
- Scenario comparison
- Forecast dashboards
- Automated model refresh
How it works
From input to action through one connected flow.
01
COLLECT
Consolidate historical and current data
02
MODEL
Prepare features and train forecasting models
03
PREDICT
Generate forecasts and risk signals
04
DECIDE
Surface scenarios through dashboards and alerts
Delivery scopeSolution architecture, workflow design, AI integration, application engineering and production implementation.
TechnologyPython · Forecasting models · SQL · APIs · BI dashboard
Measured outcome
Operational work became visible, measurable and easier to scale.
Teams gained an early‑warning layer instead of waiting for monthly reports. Forecasts were refreshed automatically and unusual patterns were surfaced with supporting context.