Models that learnfrom your operations.Decisions that improve.
Machine learning trained on your own operational data, forecasting, detection and recommendation, hosted and governed in the Kingdom.
Why AI and machine learning matter now
The Kingdom is investing in AI at national scale, and operators already hold the data that models need, in SCADA, ERP, cameras and sensors.
Data is already there
Years of operational history sit unused in historians, ERP and ticketing systems.
Models are commodities
Open and licensed models are mature; the gap is integration and operations.
Pilots stall
Most AI pilots never reach production; the gap is operations, not the model.
Sovereignty matters
PDPL, NCA and SDAIA rules call for in-Kingdom training, hosting and audit.
The answer: ML on your own data, run as an operated service, one workflow first, then a reusable template.
Sources: SDAIA 2026; MIM National Industrial Strategy; Future Factories Program; GACA 2025; Saudi Aviation Strategy; Matarat Holding
AI & machine learning, operated in the Kingdom
We connect your data sources, train and validate models against your baseline, and run them as a monitored service with a human in the loop.
Data foundation
Pipelines from OT, IT and field devices into a governed, AI-ready data layer.
Model development
Forecasting, anomaly detection, classification and recommendation, chosen per use case.
Operations
Monitoring, retraining and audit trails through MLOps and a managed service.
Building blocks of an applied-AI programme
Six components we assemble per use case, most clients start with two or three.
Data pipelines & feature store
Clean, contextualised data from OT, ERP and devices, ready for training.
Model library
Forecasting, anomaly, classification and optimisation models, open or licensed.
Edge & cloud inference
Run models on gateways, cameras and vehicles or in-Kingdom cloud.
MLOps
Versioning, automated retraining, drift monitoring and rollback.
Explainability & audit
Every prediction traceable; model cards and audit logs for SDAIA alignment.
Decision interfaces
Dashboards, alerts and work orders in Arabic and English.
How it works: from raw data to a trusted decision
A six-stage loop that keeps models accurate as operations change.
Collect
Sensors, PLC/SCADA, ERP and video feeds
Prepare
Clean, label and contextualise
Train
Build and validate against baseline
Deploy
Edge or cloud, by policy
Monitor
Drift, accuracy and bias checks
Act
Alerts, recommendations, work orders
Where machine learning earns its keep
Eight use cases that pay back first, each starts with data you already collect.
Predictive maintenance
Vibration, current and temperature patterns flag failures before they stop a line.
Energy optimisation
Models find idle load and peak waste across lines, chillers and pumps.
Demand forecasting
Forecast passengers, stock, water or power demand by hour and site.
Quality inspection
Learned defect patterns from images and process data cut scrap.
Route & dispatch optimisation
Learn travel times and demand to plan fleets and crews.
Crowd & queue prediction
Forecast density and waiting times from counters and cameras.
Anomaly detection
Spot unusual readings, transactions or network behaviour early.
Revenue & pricing analytics
Predict occupancy and willingness to pay for parking, retail and services.
Where it creates value across Saudi sectors
The same model library, tuned to each sector’s data and critical assets.
Industry & factories
OEE, energy, quality and predictive maintenance on existing machines.
Airports
Passenger-flow forecasting, parking demand and non-aero revenue.
Hajj & crowd mobility
Crowd density, bus ETAs and incident prediction during peaks.
Fleet & logistics
ETAs, fuel, driver risk and cold-chain excursions.
Utilities & energy
Load forecasting, leak and outage prediction, asset health.
Retail & FMCG
Stock, replenishment and shelf analytics from cooler and POS data.
Start with one workflow on data you already own, then reuse the pipeline and model template across sites.
A week with machine learning on the line
Illustrative sequence from a packaging line in a Riyadh industrial city.
drift detected
Vibration on filler #2 drifts days before the usual failure signature; a work order is raised.
energy at idle
The energy model flags a chiller at full load through a planned stop; setpoints are changed.
scrap pattern
Image and process data link a reject spike to one supplier batch; purchasing is notified.
monthly report
The KPI pack shows unplanned stops, kWh per unit and reject rate against baseline.
Every alert carries the evidence behind it, operators decide, the model recommends.
Illustrative scenario, not a case study.
Where this technology goes to work
Let us find the firstmodel worth running.Know now.
A 45-minute walkthrough of your operation, and the one workflow worth proving first.