Solutions

Reading today’s payments from 2 systems

Card and account-to-account payments since midnight.

Scoring every payment for fraud risk

Each payment gets a risk score between 0 and 1.

Comparing with known fraud patterns

High-risk payments checked against this month’s patterns.

Reading sales from 3 systems

Point of sale, e-commerce and ERP, joined by store and product.

Forecasting demand for the next 14 days

A forecast for every store and product, with a likely range.

Checking supplier lead times

Stock on hand compared with the next confirmed deliveries.

Reading appointments from 4 clinic systems

Bookings, walk-ins and cancellations, joined by clinic and hour.

Forecasting patient arrivals by hour

Expected arrivals for every clinic, with a likely range.

Checking staff rosters

Nurses and doctors on shift compared with expected demand.

Reading usage and billing for 2 million lines

Calls, data, top-ups and bills for every active line.

Scoring every customer for churn risk

Each customer gets a risk score between 0 and 1.

Checking recent complaints and outages

High-risk customers matched with tickets and network incidents.

Reading live GPS and order data

Truck positions, orders and promised delivery windows.

Predicting arrival times for every route

An arrival estimate for each shipment, with a likely range.

Checking weather and port delays

Routes compared with weather alerts and port queues.

Reading sensor data from 240 turbines

Vibration, temperature and power output, every second.

Detecting unusual vibration patterns

A model trained on healthy turbines flags anything unusual.

Comparing with past failures

Flagged patterns matched against earlier gearbox failures.