AI Data Strategy & Predictive Model Development
Every AI ambition dies or thrives on data readiness. We turn scattered spreadsheets, lakes, and legacy databases into governed, ML-ready pipelines — then train models on the asset only you own: your data.
What it includes
A mapped inventory of your sources — warehouses, SaaS exports, spreadsheets, event streams — scored for quality, lineage, and fitness for the models you actually want.
Ingestion and transformation built on dbt, Airflow, and Kafka into Snowflake, BigQuery, or Databricks — incremental, tested, and observable.
Forecasting, classification, and recommendation models built with PyTorch, XGBoost, or fine-tuned foundations — chosen by the problem, not the hype cycle.
Versioned datasets, experiment tracking, drift monitoring, and retraining triggers — so the model that ships keeps working after the launch week.
What you get
What lands in your hands at the end, not activity reports.
- Data readiness report with a prioritized remediation map
- Production pipelines with data tests and lineage documentation
- Trained model(s) with evaluation reports and a model card
- Drift monitoring dashboard and retraining playbook
Where it is used
- 01Retail: demand forecasting from sales history plus seasonality and promotions
- 02Fintech: risk scoring on first-party behavioral data instead of generic bureau features
- 03Healthcare operations: patient-flow prediction from scheduling and intake records
Tools and platforms we work with
Solutions that use it
The same service, framed by the problem it solves.