sphere lab

IIoT

Our modular IIoT algorithms connect real-time industrial sensor data to failure prediction and performance optimization algorithms. We use hybrid models — supervised Machine Learning, time series neural networks, and physics-based digital twins — to estimate Remaining Useful Life (RUL), identify anomalies in milliseconds, and generate predictive alerts with an average lead time of 28 days. The recommendation layer turns these alerts into automatic work orders, prioritized by criticality and operational cost, while KPI dashboards show OEE gains and downtime reduction.

Advanced Sentiment Analysis

Our semantic processing algorithms operate on multiple layers simultaneously, capturing contextual nuances in complex communications. This approach enables the identification of subliminal communication patterns and emerging trends in large volumes of textual data, essential for political campaigns and crisis management.

Regulatory Crisis Prediction

We apply advanced stochastic modeling and simulation techniques to predict regulatory trends and potential compliance crises. Our simulation architecture incorporates multiple normative and behavioral domains simultaneously, allowing us to anticipate regulatory changes with scientific precision.

Multilevel Behavioral Modeling

We use a multilevel behavioral modeling approach for social network analysis and narrative propagation. Our algorithms detect emerging patterns of collective behavior, enabling strategic interventions in political campaigns and crisis management.

Behavioral Analytics for Credit & Investments

Our Behavior AI suite captures behavioral signals — transactions, mobile telemetry, app navigation, and social interaction — to calculate Behavior Scores that evolve in real time. We combine:

• Dynamic Bayesian models to estimate default and churn propensity under macroeconomic uncertainty;
• Temporal Attention Networks (Transformer TS) that detect micro-spending patterns and adjust limits before liquidity shocks;
• Counterfactual Reinforcement (offline RL) that tests credit offer or portfolio rebalancing policies without real risk, optimizing CLTV;
• Hierarchical behavioral segmentation that merges financial personality clusters + life events (e.g., job change, marriage) and generates personalized nudges.

The engine delivers predictive alerts with an average lead time of 18 days for default and 6 hours for anomalous portfolio movements. Risk dashboards show expected impact on NPL, VaR, and ARPAC, while plug-and-play APIs trigger adaptive limits, dynamic rates, and tailored investment recommendations.