Loading page…
We build forecasting, anomaly detection, and decision support models — and integrate them into the dashboards and workflows your team already uses.
We scope around the system you actually need to operate, maintain, and operate — not a fixed vendor product or a one-size-fits-all implementation.
Time-series models accounting for seasonality, external signals, and promotional effects — integrated into your planning tools.
Statistical and ML-based systems that flag outliers in operational, financial, and sensor data in real time with explainable alerts.
Classification models identifying at-risk customers, transactions, or assets before the outcome becomes visible — with probability scores.
Collaborative and content-based filtering models that personalise product, content, or action recommendations at scale.
Surfacing model outputs inside Tableau, Looker, Power BI, or Metabase rather than requiring a separate interface or context switch.
Each engagement is broken into defined phases with reviewable outputs. Scope can adapt, but accountability stays visible.
Assess the quality, completeness, and relevance of your historical data before committing to any modelling approach.
Transform raw data into predictive features — lag variables, rolling statistics, external signals, and domain-specific encodings.
A working prototype with measurable performance — giving you a concrete benchmark before full development investment.
Select and train the right architecture — XGBoost, LightGBM, Prophet, LSTM, or ensemble — tuned via cross-validation on your data.
Surface model outputs inside your existing BI tools, databases, or applications via API, scheduled query, or webhook.
Technology choices follow your environment, operating constraints, team capability, and long-term ownership requirements.
The same technical capability can require very different controls, integrations, and operating models across industries.
Demand forecasting, dynamic pricing, recommendation engines, returns prediction, stock optimisation.
Fraud scoring, credit risk, revenue forecasting, portfolio anomaly detection, AML signals.
Delivery time prediction, route demand forecasting, capacity planning, delay anomaly detection.
Yield prediction, predictive maintenance scheduling, defect rate forecasting, supply demand sensing.
The exact architecture and delivery plan depend on your environment. These answers describe how OSYSTIC approaches the work.
For time-series forecasting, at least 2–3 full seasonal cycles gives a reasonable baseline. Less is workable but produces wider confidence intervals. We assess your specific data in discovery.
That depends on how quickly your underlying patterns change. We set up drift monitoring so retraining is triggered by evidence rather than a fixed calendar.
Yes — this is a specific design goal. We surface outputs via API, scheduled database table, or direct BI connector rather than creating a separate interface.
We use task-appropriate metrics — MAPE or WMAPE for demand, AUC for classification, RMSE for continuous outputs — agreed before training begins. We also measure against a naive baseline so improvement is meaningful.
Tell us what you are trying to forecast or detect. We will review your data and give you an honest picture of what is achievable.