Artificial Intelligence · Machine Learning
Machine learning and data science in Saudi Arabia: models that forecast, detect and decide, trained where your data lives
GoAgile builds machine learning models for forecasting, anomaly and fraud detection, language and vision, and runs them in production with monitoring. Training and serving can happen on your own servers when data must stay in-house.
- Forecasting, anomaly detection and Arabic language models
- MLOps: versioning, monitoring and retraining
- On-premises training and serving when data must stay
- Forecasts and scores
- PredictForecasts and scores
- Anomalies and fraud
- DetectAnomalies and fraud
- In production
- MLOpsIn production
- GPU training
- On-premGPU training
What it is
What is machine learning, and how is it different from generative AI?
Machine learning trains a model on your historical data so it can predict, classify or detect something new, such as next month's demand, a likely late payment or an unusual transaction. Generative AI creates text and other content. Most business value from machine learning comes from well-defined questions, clean data and a model that is monitored after launch.
Data science is the work around the model: understanding the business question, preparing the data, choosing and testing techniques, and explaining the result. In practice, data quality and a clear decision to improve matter more than the algorithm.
We build models that fit your systems. Results flow into your ERP, dashboards and workflows, and models are versioned, monitored for drift and retrained when needed. Where data cannot leave your environment, we train and serve models on your own servers.
Key facts
- Techniques
- Forecasting, classification, anomaly detection, NLP and vision
- Data
- ERP, finance, sales, sensors, documents and logs
- Delivery
- Models, pipelines, dashboards and APIs
- Operations
- MLOps with monitoring and retraining
- Hosting
- Cloud, private cloud or on-premises GPUs
What we build
From a business question to a monitored model
Seven workstreams. Start with one decision you want to improve.
Forecast demand, cash and workload
Time-series and regression models for sales, inventory, cash flow, call volumes and staffing, built on your history and updated as new data arrives.
- Demand and inventory forecasts
- Cash-flow and collections forecasts
- Staffing and workload forecasts
- Price and churn prediction
- Forecast accuracy tracking
- Scenarios and what-if analysis
What you get
Plans based on numbers, with the accuracy measured.
Spot what looks wrong before it costs you
Models that learn normal behaviour and flag unusual transactions, claims, logins or sensor readings, with reasons a reviewer can act on.
- Transaction and posting anomalies
- Duplicate and split-payment detection
- Claims and expense checks
- Equipment and sensor anomalies
- Risk scores with reasons
- Reviewer feedback loops
What you get
Fewer losses and a shorter list for people to investigate.
Language models for Arabic and English text
Classification, entity extraction, sentiment and search for Arabic and English documents, tickets and messages, tuned for dialects and your terminology.
- Text classification and routing
- Entity and field extraction
- Sentiment and topic analysis
- Semantic search
- Dialect handling
- Evaluation on your own text
What you get
Text turned into structured data your systems can use.
Models that read images and video
Inspection, counting, safety and document vision, trained on your images and deployed where the cameras are.
- Quality and defect inspection
- Counting and presence detection
- Safety and PPE checks
- ID and document reading
- Edge deployment on site
- Review queues for uncertain cases
What you get
Visual checks done consistently, at any hour.
Models that stay accurate after launch
Version control for data and models, automated pipelines, monitoring for drift and retraining when accuracy slips.
- Reproducible pipelines
- Model registry and versioning
- Drift and accuracy monitoring
- Retraining triggers
- Rollback
- Cost and latency tracking
What you get
A model that is treated like a production system.
Train and serve models on your own hardware
We size, build and operate GPU environments for training and serving, so sensitive data never leaves your premises.
- GPU sizing for training and serving
- Kubernetes and container platforms
- Secure data and model stores
- Access control and audit
- Air-gapped options
- Cost comparison with cloud
What you get
Machine learning on sensitive data, in your own environment.
Data that models can trust
Pipelines, warehouses and quality checks that bring data from your ERP, finance and operational systems into one governed place.
- Pipelines from ERP and finance systems
- Warehouse and lakehouse design
- Data quality rules and monitoring
- Master data alignment
- Feature stores
- Access control and lineage
What you get
Models trained on data you can explain.
How we work
From question to production model
We begin with the decision you want to improve, then work back to the data.
- 1
Frame
The business question, the decision it supports and how success will be measured.
- 2
Data
Sources found, quality assessed and a clean training set prepared.
- 3
Model
Techniques compared on your data, with results explained plainly.
- 4
Validate
Testing on unseen data, review with the business and risk checks.
- 5
Deploy
The model connected to your systems, dashboards or workflows.
- 6
Monitor
Drift, accuracy and value tracked, with retraining when needed.
Reference
Which model answers which business question?
Two tables we use in the first workshop.
Business questions and the techniques that answer them
| Business question | Technique | Data needed | Output |
|---|---|---|---|
| How much will we sell or need next month? | Time-series forecasting | Sales, stock and calendar history | Forecast with ranges |
| Which invoices will be paid late? | Classification | Payment history and customer data | Risk score per invoice |
| Which transactions look unusual? | Anomaly detection | Transactions and past reviews | Flags with reasons |
| What are customers writing about? | NLP and topic analysis | Tickets, emails and chats | Topics, sentiment and routing |
| Is this product defective? | Computer vision | Labelled images | Pass, fail or review |
| Which customers may leave? | Churn prediction | Usage and service history | Risk list for outreach |
From notebook to production
| Stage | What happens | Output |
|---|---|---|
| Prototype | A model is tested on a sample of your data | A result and a decision to go on |
| Pipeline | Data preparation and training are automated | A repeatable process |
| Deployment | The model serves results to a system or dashboard | A live service |
| Monitoring | Accuracy, drift and cost are tracked | Alerts and reports |
| Retraining | The model is updated as data changes | A maintained model |
Many projects stall between the first and third stage. MLOps is how we avoid that.
Technology
Open tools and platforms, run where your data lives
We use proven open-source tools and the platforms you already own.
Modelling
Data and BI
Compute and serving
Logos are trademarks of their respective owners and are shown to indicate platforms we work with.
Built for Saudi enterprises
Models built on governed data, in the place you choose
Training data is often your most sensitive data. We protect it from the first extract.
Train on your data without moving it
We train and serve models on your servers or in a Saudi-hosted environment, with personal data minimised, masked or anonymised before use, in line with the PDPL.
- Data minimisation and masking
- On-premises or in-Kingdom training
- Secure data and model stores
- Retention and deletion rules
- Access control and logs
- Cross-border transfers avoided or approved
What you get
Models built without sensitive data leaving your control.
Fair, explainable and accountable models
Each model has an owner, a documented purpose and tests for bias and stability, in line with SDAIA's AI Ethics Principles, including fairness, transparency and accountability.
- Model register and owners
- Bias and stability testing
- Explanations for scores and flags
- Human review of decisions that affect people
- Change control for models
- Alignment with SDAIA principles and sector rules
What you get
Models you can explain to a regulator.
Limits on what a model can decide alone
Models score and recommend. Rules and people decide where the stakes are high, with thresholds, review queues and overrides.
- Thresholds and confidence limits
- Review queues for uncertain cases
- Override and appeal paths
- Monitoring for drift and misuse
- Fallbacks when a model is down
- Logs of every decision
What you get
Safe use of predictions in real processes.
Arabic and English data handled properly
Arabic text, names and dates need care. We normalise and test them so models perform on your real data.
- Arabic text normalisation
- Name and entity matching
- Hijri and Gregorian dates
- Bilingual datasets
- Dialect-aware evaluation
- Reports in Arabic and English
What you get
Models that work on Saudi data, not just English benchmarks.
Why GoAgile
Why teams build machine learning with GoAgile
Models that never reach production deliver nothing. These are our habits.
Business question first
We start from the decision you want to improve and the value it carries.
Data engineering included
Pipelines, quality and master data handled by the same team.
MLOps, not just models
Versioning, monitoring and retraining so results stay accurate.
On-premises GPU capability
We train and serve models on your own hardware when data must stay.
Connected to your systems
Results land in your ERP, dashboards and workflows.
Local and accountable
A Riyadh team with ISO 27001 certified practices.
More from AI
Explore our other AI services
Each one stands alone and works better with the others: knowledge, agents, automation and the governance around them.
- AI Consulting & StrategyFind where AI pays off, set the guardrails and build a roadmap you can fund.View service
- Generative AI SolutionsRAG assistants, copilots and private LLMs grounded in your documents and systems.View service
- AI AgentsAgents that take action in your ERP and service systems, with approvals, guardrails and audit.View service
- Intelligent Process AutomationDocuments, approvals and back-office work automated with RPA, AI and human review.View service
- AI Chatbots & Virtual AssistantsArabic and English assistants on web, WhatsApp and voice, handing off to people.View service
Which decision would you improve with a model?
Tell us the question, the data you hold and where the answer needs to appear. We will assess feasibility and propose a pilot.
Plan a machine learning project →Questions answered
Machine Learning & Data Science FAQs
What is machine learning?
What is the difference between machine learning and generative AI?
How much data do we need?
Can you work with Arabic data?
Can models be trained on our own servers?
How do you keep a model accurate over time?
How do you explain a model's decisions?
How long does a machine learning project take?
Do you connect models to our ERP and dashboards?
Talk to a data science specialist
Tell us the decision you want to improve and the data you hold. We will respond with a feasibility plan.
Why GoAgile for machine learning?
Models built on governed data, run in production and, when needed, on your own servers.
- Forecasting, anomaly and language models
- MLOps with monitoring and retraining
- Results connected to your ERP and dashboards
- ISO 27001 information security practices