Most SMBs hold more historical and operational data than they use, and many AI initiatives stall because the underlying data isn't ready for them. We build the pipelines and governance first, then turn the result into forecasts that change decisions.

01 —

Good data first

Before any model is built, we run a data readiness assessment — checking structure, completeness, history depth and labelling. Where needed, we ingest, clean and organise data from disparate sources and architect data lakes, warehouses or streaming pipelines, with governance and access controls built in from the start so the foundation holds up under compliance scrutiny.

Minimum requirement for predictive work: 12–24 months of clean, structured historical data.

02 —

From raw data to forecasts

  • Churn prediction — score customers by risk and trigger retention workflows automatically
  • Demand forecasting — for inventory, staffing and procurement planning
  • Dynamic pricing — adjusted from demand signals, competitor data and your margin rules
  • Customer lifetime value and operational performance / bottleneck prediction
03 —

Who it's for

SMBs with significant historical operational or customer data, making recurring decisions — pricing, inventory, retention, capacity — where better data foundations and predictions would change the outcome. Most common in ecommerce, subscription, hospitality and logistics.