We build predictive models and decision dashboards that turn historical operations data into forecasts your teams can act on.
Prediction only matters if operators trust it and can act in time. We start from the decision: reorder stock, schedule maintenance, prioritize leads, then work backward to features and models.
Pipelines clean and join ERP, IoT, CRM, and warehouse data. Models range from classical forecasting to gradient boosting and deep sequence models when the signal warrants complexity.
Outputs land in dashboards and alerting channels with uncertainty bands, not false precision. Retraining schedules and drift monitors keep models honest as your business changes.
You own the notebooks, services, and feature store definitions. No black-box vendor lock-in for the metrics that steer your P&L.
Frequently Asked Questions
Do we need a data warehouse first?
Helpful but not mandatory. We can start from operational databases and files, then harden toward a warehouse as volume and team maturity grow. Codoop delivers this as production software you own, in 14-day sprints, with staging demos and a full source handover.
How accurate will forecasts be?
We set baselines against your current process and publish error metrics. Accuracy depends on data quality and process stability, we are transparent about both. Codoop delivers this as production software you own, in 14-day sprints, with staging demos and a full source handover.
Can models run on a schedule or in real time?
Both. Batch jobs suit daily demand planning; streaming or near-real-time inference suits equipment health and fraud-style signals. Codoop delivers this as production software you own, in 14-day sprints, with staging demos and a full source handover. Work starts from a written architecture note and finishes with repositories, runbooks, and full client ownership of the code.