Effective AI starts with the right data. We help you ingest, clean and organise data from disparate sources and architect scalable pipelines for analytics and machine learning — data lakes, warehouses and real-time streaming, with governance built in from the start. Once the foundation is in place, we build predictive and prescriptive models that turn your historical data into forecasts you can act on.
Pipelines · lakes & warehouses · streaming · predictive modelling
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.
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.
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.