The minimum data requirements depend on your use case. For demand forecasting, 12-24 months of historical sales data is ideal. For churn prediction, customer interaction logs and engagement metrics are key. We start with a free data audit to assess what you have, identify gaps, and recommend the best path forward.

Our models achieve 85-95% accuracy depending on the use case, data quality, and prediction horizon. Short-term forecasts (1-4 weeks) are typically more accurate than long-term ones. We always benchmark against baseline models and provide confidence intervals so you understand the reliability of each prediction.

We support both. Batch processing is ideal for daily or weekly forecasts like demand planning and financial projections. Real-time predictions are used for fraud detection, dynamic pricing, and recommendation engines where millisecond response times are needed. We design the architecture based on your latency requirements.

Yes. We integrate with all major BI platforms including Power BI, Tableau, Looker, and Metabase. Models are served via REST APIs that your dashboards can query directly. We also support data warehouse integrations with Snowflake, BigQuery, and Amazon Redshift for seamless data flow.

Our team has deep experience with Saudi market dynamics including seasonal patterns around Ramadan, Hajj, and Saudi National Day. We account for local economic indicators, SAMA regulations for financial models, and Arabic text data for sentiment analysis. Our data pipelines handle Arabic date formats, Hijri calendar integration, and Saudi-specific data sources.

ROI varies by use case. Demand forecasting typically reduces inventory costs by 15-30%. Churn prediction programs retain 10-25% more customers. Dynamic pricing can increase revenue by 5-15%. We establish baseline metrics before implementation and track ROI throughout the engagement so you see measurable business impact.