Real Estate & Logistics

Use Case / Problem Statement

Swivvl was scaling rapidly without a solid data foundation — no warehouse, no self-serve analytics, and no ML infrastructure.

• Data siloed across product databases and third-party systems
• Business teams dependent on engineering for every query
• No ML infrastructure for predictive capabilities
• Manual reporting and inconsistent data quality
• Scaling to $1B in transactions without governance

Solution & Development

• Azure data infrastructure (ADF + Synapse + dbt).
• Data quality checks and lineage tracking.
• ML infrastructure with feature store and deployment pipeline.
• Power BI dashboards with self-serve analytics.
• Knowledge transfer with full documentation and training.

Use Case / Problem Statement

Operations teams were reactive — no forecasting capability to anticipate demand spikes or allocate resources.

• No demand forecasting or ML model in production
• Seasonal spikes caused operational failures
• Planning backward-looking and manual

Solution & Development

• Multi-model time-series forecasting (ARIMA, Prophet, GBM).
• Multi-horizon outputs (7/14/30-day).
• External signal integration (weather, holidays).
• Capacity planning recommendations and anomaly detection.
• Forecasting API integrated into ops dashboard.

Use Case / Problem Statement

Leadership relied on stale, manually compiled reports and the analytics team was stuck in Excel maintenance.

Solution & Development

• Dimensional data model with standardized KPIs.
• Tableau dashboards across ops, sales, and executive views.
• Automated report pipelines with scheduled delivery.
• Self-serve analytics training and documentation.