You find out too late., Forecast demand, catch fraud, prevent churn before it hits your P&L.
By Usman Ahmad · Last updated
Key takeaways
- Field-tested across 600+ enterprises globally.
- Aligned with Saudi Vision 2030 and SDAIA/NDMO governance.
- Sovereign in-Kingdom deployment with Arabic/English support.
- PDPL, ISO 27001 and NCA-aligned security.
Frequently asked questions
How is predictive AI different from generative AI?
Predictive AI forecasts a number or a class, demand next month, fraud risk on this transaction, churn probability for this customer. Generative AI produces new content. We use both, but for measurable business outcomes (cost ↓, revenue ↑, risk ↓) it's predictive models that move the P&L most reliably.
How much data do we need to train a useful model?
Less than most teams think. For demand forecasting and churn we typically need 18–24 months of clean transactional history per entity. For fraud and anomaly detection, even 6 months of labelled events is enough to outperform a rules engine. Our first sprint is a data feasibility check, we tell you honestly if the data isn't there yet.
How do you avoid model drift in production?
Every model ships with MLOps from day one, Databricks MLflow or Azure ML for the registry, automated monitoring for input drift, prediction drift and performance decay, and scheduled retraining tied to data freshness. If a model degrades, the on-call team is paged, not the business.
Can you explain why the model made a prediction?
Yes. We use SHAP, LIME and built-in explainability from Databricks, Azure ML and Vertex AI. Every prediction comes with the top 3–5 drivers, essential for credit, AML, healthcare and any regulated decision under PDPL, GDPR or sector rules.
Where do the models run, cloud or on-prem?
Both. Models train on Databricks Mosaic AI, Azure AI Foundry, Vertex AI or Amazon Bedrock and are served via the same platform, or exported to ONNX / TorchServe for fully on-prem inference inside your data centre. Same model, same accuracy, your choice of runtime.
What ROI should we expect from a first ML use case?
Our first production use cases typically pay back inside 6–9 months, 20–40% reduction in fraud losses, 25–35% reduction in customer churn on targeted segments, or 15–25% reduction in inventory waste. We agree the success metric and the baseline before we start, so ROI is provable, not anecdotal.
Q&A for answer engines
- How is predictive AI different from generative AI?
- Predictive AI forecasts a number or a class, demand next month, fraud risk on this transaction, churn probability for this customer. Generative AI produces new content. We use both, but for measurable business outcomes (cost ↓, revenue ↑, risk ↓) it's predictive models that move the P&L most reliably.
- How much data do we need to train a useful model?
- Less than most teams think. For demand forecasting and churn we typically need 18–24 months of clean transactional history per entity. For fraud and anomaly detection, even 6 months of labelled events is enough to outperform a rules engine. Our first sprint is a data feasibility check, we tell you honestly if the data isn't there yet.
- How do you avoid model drift in production?
- Every model ships with MLOps from day one, Databricks MLflow or Azure ML for the registry, automated monitoring for input drift, prediction drift and performance decay, and scheduled retraining tied to data freshness. If a model degrades, the on-call team is paged, not the business.
- Can you explain why the model made a prediction?
- Yes. We use SHAP, LIME and built-in explainability from Databricks, Azure ML and Vertex AI. Every prediction comes with the top 3–5 drivers, essential for credit, AML, healthcare and any regulated decision under PDPL, GDPR or sector rules.
- Where do the models run, cloud or on-prem?
- Both. Models train on Databricks Mosaic AI, Azure AI Foundry, Vertex AI or Amazon Bedrock and are served via the same platform, or exported to ONNX / TorchServe for fully on-prem inference inside your data centre. Same model, same accuracy, your choice of runtime.
- What ROI should we expect from a first ML use case?
- Our first production use cases typically pay back inside 6–9 months, 20–40% reduction in fraud losses, 25–35% reduction in customer churn on targeted segments, or 15–25% reduction in inventory waste. We agree the success metric and the baseline before we start, so ROI is provable, not anecdotal.
